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Problems in the choice of a system for costing and pricing of in-house computer services
Problems in the Choice of a System for Costing and Pricing of In-House Computer Services Kenneth
L. Hamilton,
Georgia Institute
of Technology
The problem of pricing computer services has been discussed in the literature since the early days of computing. Costing and pricing have often been interchanged in the discussions. After a discussion of the problems of measurement of resource usage, pricing for cost recovery and for resource allocation are discussed. The paper concludes with suggestions for further research and a brief summary.
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January
1947
Let us postulate a description of the operation and growth of the computing needs of a firm to aid in the analysis of the problem of charging for computer services. Consider a firm which has needs for administrative, financial, accounting, or production computation and record keeping, for example, which it feels could better be met through the capabilities of a computer center. The firm estimates that as it grows and develops, its computing needs will also develop and require different resources. Assume that the firm has decided to acquire and operate its own in-house computing facility rather than purchase the needed services from some external source. Now, let us define the planning horizon of the firm such that in the short run, both the capabilities of the equipment and the demands of the users are fixed. Over the long run, demand for computer use is not fixed and may increase as the firm gains experience with the computer [ 151. The long run is a period of sufficient duration such that more and/or different resources must be acquired to satisfy the needs of the users within the firm. In Address correspondence to: Kenneth L. Hamilton, College of Industriai Management, Georgia Institute of Technology, Atlanta, Georgia 30332. JOURNAL
OF BUSINESS
RESEARCH
@ Elsevier North Holland, Inc., 1980
139 0148-2963/80/02139-20$01.7$
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addition, because the demand for data processing is derived from the demand function for the solution of other business problems, one expects the data processing demand function to be somewhat price inelastic [45]. (Demand may be viewed as a function of data processing capacity available.) However, for new applications, the reduction in the unit cost of computing (over time) which has been characteristic of the industry has been a major factor in promoting the continued development of these applications [9, 131. Research
Objective
A number of studies have been published concerning pricing policies for in-house computer services. In general, each deals with only one aspect of a multifaceted sequence of decisions. Authors have discussed issues ranging from (1) whether to charge users (policy); (2) for what should the users be charged (resources); (3) how and what to charge users (pricing); to (4) related areas such as accounting and utility peak-load pricing practices to provide insight into methodology for solutions to the pricing problem. The differences in these areas are such that each must be reviewed comprehensively in developing an appropriate policy and algorithm for assignment of the costs of operating and providing computer services to other divisions within the firm. The sensitivity of the user’s end cost to the set of all these factors is quite large. In practice, the estimated ability of the user to understand a system may become a strong subjective factor in the decision to implement a particular system [ 69, 451. A General Comment
on the Literature
Diamond [ 161 observes from Baker and Pound [3] that although many professionals had written papers in the field, relatively few had published more than one or two. This was interpreted as a lack of continuing study by researchers in this area. Examination of the computer pricing literature [28] also shows numerous authors with only one paper, a few with two, and a very few with more than two. Of the computer papers which have references, nearly every one cites Nielson, Smidt, Selwyn, and Sharpe. Few papers cite much subsequent work or each other’s current work. Baker and Pound also reported that very few of the approaches suggested in the R&D (research and development) literature were actually being put into practice. The reasons cited for this were
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difficulty in building realistic models, lack of historical data, and insufficient quantitative skills for the techniques proposed. A 1976 survey by Nolan [44] shows similar results. A number of papers have been written on the use of pricing for cost recovery and for resource allocation. Yet the level of practice is such that some computer centers operate under charging algorithms which encourage use of the scarce resources of the system and penalize through high charges the use of an alternative and more available resource. An example of this practice is the Control Data Cyber 74 system at the Georgia Institute of Technology, Atlanta, Georgia, which has a shortage of disk for both temporary working area and for permanent storage. Yet, the pricing system makes disk I/O (input/output activity) cheaper than tape I/O by a factor of over twenty to one. Similar cases have been noted at other computing centers. An Analytic
Model
A few analytic models have been proposed. Shaftel and Zmud [52] developed a mathematical programming model for flexible pricing. This model was limited by a constraint which was a function of several determinations of user utility and environment which could not be measured directly and thus made the model solvable only through an iterative process. Subsequent papers by Zmud [68, 701 added measures for service quality dimensions of user satisfaction or regret and a means to improve throughput. Balachandran and Stohr [4] gave computational examples and developed a model for optimal pricing of computer resources in a competitive environment. Although this model was based on a multiple center market, it could be adapted to the computer center which has multiple nonhomogeneous computing system which it must price and operate. Kriebel and Mikhail [35] present a model for dynamic pricing in computer center networks. Their paper also could be used to develop a pricing policy for the multimainframe center and includes an extensive bibliography. Marchand [37, 381 formulated a general equilibrium model to specify firstorder conditions that the prices and capacity of the facility must satisfy to be Pareto optimal. The model which follows is based on a synthesis of the above papers, with extensions to include the not for profit center and the disutility caused the ith user by the presence of all other users. It is the effort to model these factors more precisely that distinguishes this model from those referenced
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above. This formulation of the problem also allows cost-benefit analysis of the long-run capacity decisions. Model Specification There exists a computer center providing computational services within an organization. A set of some g resources of the total N resources available, some or all of which are limited in supply relative to demand, are selected to be monitored and priced. These resources may involve the time and space utilization of computer system resources including abstract factors such as priority, time of day, and measures of system quality [ 521 . In each budgetary control period t, t = 1, .-., T, the processing of a user request i for service consists of the production of a job i, i = 1, *.., M, which imposes demands Xi on the resources of the computing center. For example, for a particular job i, the Xi might correspond to lines printed, accesses to a magnetic tape, or seconds of central processor time. The total demand by all users at any point in time, X, is constrained by the technological restrictions R, inherent in the operating characteristics of the resource. In each control period, an externally imposed budget B is specified with which each user may purchase computer services. The set of prices {p} is specified at the beginning of the budgetary period and remains constant throughout the period. The function F of the maximum of each user’s utility function is not specified. This provides generality to the model in that this function will depend on the nature of the computing center management objectives. For example, one center may choose to maximize the utility of the least well-off user (F, a minimum function), and another may choose to maximize the average utility of all users. The fixed cost of operating all resources is given by the vector C, and MC represents the unit marginal cost of each resource. Problem of the ith User The ith user’s problem is to maximize the utility of the job initiated at time t and completed at time t’, given the disutility resulting from the delay and caused by the resource requirements of all m users, X, and within the externally imposed budget constraint Bi. The demands of all m users at any point in time are constrained by the technological capacities of the system {Rj} . Problem of the Computer Center The problem management faces is to maximize the (unspecified) function of each user’s maximized utility function by selecting (I) in the short run, the minimal set of nonzero prices {pi}, to control resource utilization, and (2) in the long run, the levels of resources {Rj} , which provide
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adequate levels of service such that the total revenue is approximately equal to total cost over some budgetary control period t, t = 1, e.0, T. An alternative nonsymbolic form of Nonsymbolic Specification the model is:l Select prices and resource levels that maximize some unspecified aggregate of the utilities obtainable by individual users from their budgets and the system resources while keeping total revenue approximately equal to cost.
Model Assumptions To solve this formulation, one must make some restrictive assumptions. Users have perfect knowledge of device characteristics and prices of the computer center. We also assume that the user is price elastic in that users will alter their patterns of service consumption in the long run. The numeraire or money involved is real and not artificial (“funny money budgets”). The problem for the computer center management assumes that demand can be estimated accurately enough, perhaps by using historical data, and that enough information can be found to make some determination of the user’s utility functions. Elnicki and Hughes [ 211 and Dolan [ 191 have reported empirical analyses to show that such assumptions are not unreasonable and appear to be borne out in practice. Dolan noted that a fundamental problem was lack of information about consumer behavior when prices are imposed. Elnicki and Hughes reported that establishment of a pricing system appeared to substantially change the consuming habits of both internally and externally funded users. Model Solution and Interpretation To simplify the solution and implementation process of a very difficult model, an iterative and numerical approach is taken. Assume that only three resources are defined for pricing at the outset: CPU time weekdays, weeknights, and weekends. As a starting point for the {pi}, we will use market prices. The {Rj} are fixed in the first period by estimates of resource requirements at the time of equipment acquisition. Since { Cj} and {Mcj} are functions of the {Rj} , they are also set for this period. We then announce resource constraints and prices and observe user demands on the system. We attempt to find out levels of user satisfaction (utility) explicitly by asking or implicitly by observing system usage. 1 This interpretation of the model was suggested to analysis of the problem.
by an anonymous
reviewer
as an aid
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Terminology
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and No tation
p = n vector of unit prices of resources. indexed by j = 1, .-a, IZ of technological resource constraints, indexed by j = 1 ~-.*, n B = m vector of budgetary constraints, indexed by i = 1, .** m X = m X M matrix of resource requirements of all users, i= 1, *.., m on all resources. j = 1, .**, 12 xi = n vector of resource requirements of the ith job (user) on all resources, j = 1. --., n c = n vector of fixed costs of resources. indexed by j = 1, a**, n Mc = n vector of marginal costs for resources, indexed by j = 1, -.-, n R = n vector
After a period of observation, we then reevaluate the {pj} and {Rj}. We may choose F to be some function on user utility, say to make the average user better off. We then solve for the {pj} for the next period. We may also evaluate the {Rj} to determine if any resource constraints are binding or overly slack. If so, we might especially look for a low-cost way to change the {Rj} available by fine-tuning the system, or a change in the {pj} to modify user behavior. An alternative approach might be to start by setting {pj} nonzero for all resources and performing an analysis at the end of each period to determine which resources best measure machine usage and also which resources should be priced to control usage. From this point, we would continue the iterative process. While this process may seem slow or unduly complicated, such an iterative process seems to be the most likely way of converging to a set of prices to best control user behavior. The research of Dolan [ 191 and Elnicki and Howard [ 2 1 I suggests that prices can be used to increase demands during periods of low facility utilization, thereby extending the useful life of a given hardware con-
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figuration. A pricing scheme to which users do not react is a waste of computer center resources [ 581. We turn now to the underlying issue of the problem: should users be charged for their computer demands? Should Users Be Charged? A first point of discussion is to what extent should we concern ourselves with internal charges and prices? Collecting and processing development and operations data can tie up equipment and generate significant costs. It is not unusual for the resource monitoring and cost-accumulation system to use 5% of the computing resources [ 661. In addition, the billing process itself will generate additional costs in divisional and corporate accounting functions. Selwyn [48] has identified some organizational objectives and how they may be considered in pricing policy. Sharpe [ 531 has shown that internal profit centers maximizing their own profits do not maximize those of the entire organization. Turney [64] developed a cost-allocation model for transfer pricing of Management Information Systems (MIS). He encountered severe difficulty with determination of capacity. Since capacity is created by equipment resources, software, personnel, and computer design, a change in capacity may result from a change in any component of the system. Should only the users of that component be charged for the change, or should all users share in the charge, since the total capacity is improved? Cost determination with multiprogramming, multiprocessing computers was examined by Wiokowski and Wiokowski [67] , It was found that the job-stream mix could influence the use of resources. Considering all these preliminary difficulties, is it worth the effort to charge for in-house computer utilization and services or should they just be charged off as an overhead expense? Alward [ 11 asserts the decision to charge for computer services is not an unqualified “yes” in all instances for several reasons. One is the difficulty discussed above in determination of a fair, equitable, and understandable price structure. Another is the maturity and understanding of users and managers in computer processing. And yet, when the computer center is treated as an overhead item or free good, the incentive is to increase computer usage almost without limit [ 81. Consequently, there is a wide range of opinions on whether and how charge-backs should be used. Gill and Samet [23] present a pro and con paper on charging
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for computer usage in British universities. Gill argues for charging as a means of regulating the consumer. Samet responded with the case against charging and the use of money as an allocation control. The thrust of his argument was that using money as an allocation mechanism puts the burden on the wrong people. To make the user aware of the resource he is using, he must be charged something he can feel. Unless that something is “real” money (as opposed to “funny” or fiat money), the user is actually removed from feeling the charge and probably from worrying about it. It is, in fact, some administrator, such as a department head or manager, who must raise the funds and be concerned about their usage. Samet also points out that if the computer is not fully used, there is no need for any control function. On the other hand, if the demand exceeds the supply, charging serves no useful purpose as it does not make guaranteed time available even to those departments who can pay. Samet concludes it may therefore be necessary to have a rationing scheme to curtail the amount of time that anyone may purchase in a given period, leading to the very problems that a charging scheme is supposed to avoid. Clearly if the charging or control system is expected to encourage proper use of resources, it must be carefully designed [ 81. The consensus is that some controls must be exercised on computer usage as it is an expensive and easily misused good. A few firms have been successful in controlling use of the computer through an overview committee which approves computer project implementation and reviews ongoing projects for continuation or termination. A notable example is the data processing facility management of Southern Railways. In general, however, it appears that some form of a charging system is the more effective way of controlling usage. In the next section, we will discuss the first problem in establishing a charging system: that of measuring resource utilization and costs. Measurement
of Resource
Utilization
and Costs
In earlier computer systems, the hardware elements of the computer accounted for a significant portion of the capacity and cost of the computing system and center [50] . Other cost categories include keypunching and other data collection activities, programming support personnel, system management personnel, physical site facilities, air conditioning, maintenance, and expandable supplies such as punch cards, continuous forms, and the like
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[35], In general, these other costs will increase with hardware costs but some economies of scale appear to be present. Selwyn [ 501 found that the rate of increase in hardware costs was less than that of overall operating expense. Turney and Purdy [65] made a case study of management information system (MIS) departmental costs in one company and compared their results with a previous Diebold Group survey [ 181. Turney and Purdy separated MIS departmental costs into personnel (53%), hardware (32%), data processing supplies (6%) building (4%), and overhead (5%). (The share of departmental costs due to hardware alone is now steadily declining with technological advances, i.e., microelectronics.) Of the total costs, 42% could be traced to applications and 58% were joint costs. Of the traceable costs, 75% could be further associated with specific users (3 1.5% of the total). The measurement of nonhardware resources expended by a specific user is difficult [ 65 I. One would presume that accounting for hardware costs and traceability of hardware costs to users would be less difficult. Gladney, Johnson, and Stone [24] discussed some of the complex technical problems involved in measuring the resources delivered in multiprogramming and multiprocessing installations. Earlier programming systems in which a single user occupied the computer were relatively easy to handle in resource-usage accounting for cost assignment. With the advent of multiprogramming and timesharing, significant complexities arose. Elapsed time in multiprogramming systems is a function not only of the job, but also of the job mix and the environment. Cotton [ 111 says that timing is not a problem because most operating systems could determine actual running time for each program accurately. In contrast, Swoyer and Armstrong [62] show that since the hardware timer resolution of the IBM 360/65 is 16.6 ms, it is theoretically possible for a user either to run free or be overcharged by a factor of 13000 to one, depending on at which extreme of the clocking cycle his usage is measured. Resource requirements range from central processor (CPU) time to requirements for temporary and permanent storage. Storage of data outside the Central Processor or main memory means that some I/O must be done by the program during execution in order to get data and return results. It now becomes possible with a multiprogrammed machine for two users to be in contention for the device to which I/O is directed. For example, given that each user wants to read 1000 records of 1000 characters, either job
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running separately would take about 15 clock seconds (IBM 360/65). In a typical case with contention for position of the disk arm, each job running together could take as much as 90 clock seconds. Thus, a given job could take different times for execution, depending on the system load and the job mix. It is worth noting that this problem does not occur with tapes or nonshared disks, since only one job may access such a device at one time. The concern for adequate measures of usage leads to a discussion of reproducibility. Reproducibility is defined as the requirement that a job should “cost” or use the same amount of resources regardless of the time or job mix and is important for psychological and political reasons [ 251. Wildly varying prices are very disturbing to some users, and unpredictable prices make it more difficult for management to make up computer budgets. The rationale for concern about reproducibility is its relationship to the cost recovery and pricing structures. In the multiprogramming and multiprocessing environment, reproducibility becomes very difficult. It is not, however, essential to fairness, as fluctuations will average out in the long run. To illustrate these problems, the Fortran program used to manipulate the bibliography for this paper was run a number of times on weekdays and weekends. This program is input-output constrained, as it reads data in from one disk file and writes to another disk file to use a system-sorting routine to alphabetize authors and titles. It then reads the data back into memory and writes to the output file for printing in the desired formats. Nonparametric statistical analysis was used to evaluate program run times in seconds of CPU time and minutes of terminal connect time (elapsed wall-clock time). Runs were categorized by whether the run was Monday-Friday or Saturday-Sunday, irrespective of clock hour. There was a statistically significant difference between categories at the 0.007 level in elapsed wall-clock time (terminal connect time). The difference between categories in CPU times was not statistically different. There was, however, a statistically significant positive correlation between the elapsed wall-clock time and the amount of CPU time used (tested over the complete sample space). Given that the user is charged for both terminal connect time and for CPU time used, when there is such a variation that the mean elapsed clock time during the week is more than 4.3 times greater than on the weekend (16.56 min vs 3.83 min), a real difference in cost and consequently, perhaps, in user price can occur. Assuming that we have the technical capabilities
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to measure pricing.
Services
consumption
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of resources,
we turn now to the issues of
The Role of Price Price is defined by economists as a ratio of quantities: the amount of some good Y that must be given up to obtain a unit of some other good X. As an extreme example, one could speak in terms of price in clams per minute of computer time (in modern economics, price is normally quoted in terms of money as a medium of exchange). Consequently, unless a user actually gives up some resource in exchange for the computing services, he is not paying a “price.” Kreitzberg and Webb [34] carefully distinguish job cost from job price: job cost is the amount which it costs the installation to process a job; job price is that amount a user of the computer facility pays for having his job processed. The next section will address methods of pricing when the emphasis is on setting prices such that the price of each job is equal to the cost of each job and that the sum of all job prices will equal the sum of all job costs and of operating the installation for that period of time. Pricing for Cost Recovery From the supply side of the market, the issue of resource allocation is in a fundamental sense a question of cost allocation [35]. The supplies of computing services are characterized by (1) a high ratio of fixed costs to variable operating costs in the short run, (2) economies of scale, and (3) significant indivisibilities (step function in capacity increases) [35]. The high ratio of fixed-to-variable cost is especially true for pure computation since the machine rental generally accrues whether the machine is idle or is being used in production. Consequently, considerable attention has been devoted to the determination and assignment of these fixed costs among various users [ 1, 2, 7, 81, among others. This literature could be referred to as pricing for cost allocation [ 1 l] . The design of an adequate billing system for a modern computer system is not a simple task. Beyond economic efficiency and ease of administration, several additional qualitative criteria have been suggested for pricing (billing) rules in a computer services environment, e.g. [ 341. These criteria include: 1. Total cost recovery (including a “fair” return on the supplier’s investment) or else “limited losses” (i.e., an upper bound on the user subsidy);
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2. equity (i.e., treat all users “fairly,” such as by basing charges on the incremental cost caused by the user at the time of service); 3. reproducibility and auditability (i.e., the rule should facilitate the reproduction of the basis for the charges made and the monitoring of results); 4. ease of price estimation by users and revenue estimation by management (i.e., prior to servicing); 5. Pareto optimality for users in tradeoffs between quality of service provided and prices charged for fixed job requirements. In addition to this summary by Kriebel and Mikhail [3.5], the billing system provides a tool by which the computer can be evaluated for performance and cost/benefits [ 201. The selection of resources for measurement implies a choice problem of selecting the resources on which the costs are to be allocated [431. In an example of a nonmultiprogrammed computer, wall-clock time was an adequate measure since only a single user could access the machine. Only very rarely will a multiprogrammed computer have dedicated access (“block time”) usage. Consequently, some set of resources must be selected on which to base costing. It would appear that certain resources can be easily measured with one of several alternatives; for example, a printer could be measured in minutes, lines, or pages [42]. Even this has difficulties in application. In addition, the set of resource measures finally selected must be easily obtained from the operating system. It is important also to select the smallest possible set of resources for charging. A oneparameter cost measure may capture 80% of the user variance in the use of facilities, a two- or three-parameter measure 90% to 95%, and a loo-parameter measure will not do much better than a three-parameter measure [lo]. With more measures, it is true the center may do a better job of resource monitoring and management; however, the user reaches a saturation point in trying to plan jobs and estimate costs, and after a point, may just ignore the whole system [42]. Also, a pure cost-recovery system will normally consider only measurement of physical and not temporal resources. Cost recovery pricing may be based on full or average cost, marginal cost, market cost, and of course, a free good charged to overhead and reported to the user for information only. Using a fullcost approach, prices of each of the services are estimated by dividing the total cost of the corporate facility by the propor-
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tionate amount of resources required by one unit quantity of the service. Estimating the amount of resources required for one unit of service may not be too difficult; estimating before the fact the total amount of service required during the time period is very difficult 1493. This means the computer center must estimate total consumption since the price of each job is a proportionate amount of the total cost of the facility for the time period. If there were no estimate of consumption, then there would be no way to provide data for the budgets and analysis of the users. Generally, this concept uses historical data for cost factors. It also has inverse economic motivating factors: as the usage increases, the cost goes down and more usage is encouraged. Full-cost charging also may assure that all departmental costs are recovered but at the expense of suboptimal decisions or passing along extra expense to the users due to the poor performance of the computer division [ 53, 641. Market pricing techniques are based on the prices charged on the open market for like services. This practice is a desirable transfer pricing technique as it generally leads to optimal decisions [30] and there are no conflicts in fullfilling the criteria of transfer pricing. In some extreme cases (such as the majority of MIS services) the product is so unique that no external market exists for it. The administrative and organizational advantages however of the market price prompt some firms to initiate the use of a substitute system in situations where market price data is inadequate [641. The surrogate that is used is the negotiated price. Unfortunately, the negotiated price remains an artificial price and its use for decision making and evaluation purposes is therefore limited. The remaining aspect of charging for cost recovery still to be resolved is reproducibility of charges. This has received much attention in the earlier literature [24, 34, 54, 561, but Morris I401 in a 1976 paper comments that the only people who still seem concerned with the repeatability issue are those connected with installations which have not yet begun to bill their users. After these installations begin to charge, the discussion of reproducibility is seldom heard again. Pricing for resource allocation is Pricing for Resource Allocation distinct from the pricing for cost recovery discussed previously in that some of the constraints specified as goals of a billing system are now relaxed with the expectation that, in doing so, resources will be allocated and used in a more efficient manner. The first restraint relaxed is reproducibility in that users may be encouraged by lower prices to run their jobs during off-peak hours. Conse-
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quently, the user will expect that job costs to him will differ, and that some benefits, either in the way of increased response or lowered costs, will be available. With the addition of pricing as an allocating device, it is expected now that prices will not necessarily bear any relationship to the actual costs. For example, with average-cost pricing, as the work approached saturation, the average cost went to a minimum. Using this approach, as the workload of a resource increases, the price to the user may also increase in order to influence him to change his consumption habits. Conversely, as usage decreases or during the introduction of a new device, the price may be set very low to encourage use of that resource. The criterion that the price approach Pareto optimality for users in tradeoffs between quality of service received and prices charged becomes more significant. With a system for resource allocation the goal of the computer center, that revenue received equal operating cost, may still hold and be further complicated by the relationships between costs and prices. The use of pricing as a mechanism for allocating resources is generally thought to be a well-understood process. To be sure, the price mechanism does not always work as well as in theory [ 541. The Singer, Kanter, and Moore paper [54] is one of the better syntheses in the literature on pricing of computer services. Several authors [ 15, 22, 54, 551, have discussed the need for the use of pricing for resource allocation. Greenberger [ 261 considered pricing problems in a 1966 paper, looking at the effects of priority pricing on time share and batch users. He suggested the use of a bidding procedure as well as a nonlinear pricing model. The nonlinear model has interesting properties in that, while it can price strongly for high resource users, it is also very difficult for both the center and the user to estimate actual prices. Sutherland [60] reported on an application of a pricing system in use at Harvard University which used both bidding and fiat (“funny”) money. Smidt [55] commented on the problems of average-cost procedures and proposed a system of flexible prices. Greenberger [26] also examined variants of the priority rules which determine the access patterns for a given set of users, such as first come, first served and the c/t rule (the next job served is that with the highest waiting cost per service time). Priority rules of these types are equivalent to prices under the special assumption that all users place the same values on computer time and experience the same costs for waiting [ 541 . Under most conditions, priority rules will result in a misallocation relative to the one obtained by pricing.
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The one advantage of priorities over prices is that they are inexpensive to administer [54]. The flexible pricing concept is one where rates specifically are set to govern usage rather than to reflect costs of providing service or to enable a user to move ahead of another in the queue. The flexible aspect results from a periodic examination and readjustment of the prices. Diamond and Selwyn [ 171 and Nielson [41] give descriptions of flexible pricing implementations in university computing centers. Lehman [36] describes an implementation in a commercial research facility. The flexible pricing systems which have been implemented have tended to arrive at the pricing schedules through the intuitive abilities of those responsible for administering the pricing system [54]. Shaftel and Zmud [ 521 proposed an analytical technique for implementing a flexible pricing system based on mathematical programming. They found that, consistent with Diamond and Selwyn [ 171, control mechanisms in addition to flexible pricing are needed to satisfy all of management’s objectives concerning resource utilization. Priority pricing results from a somewhat different aspect of resource control. When a commodity cannot be stored, the producer is generally unable to adjust the supply to meet randomly fluctuating demands. If demand becomes greater than supply, some consumers must be put into queues. Marchand [37] points out that since consumers can differ significantly in the urgency of their requests, important social costs may be involved if adequate procedures are not available to favor the users whose requests are most important. The principal hypothesis of priority pricing is that decentralized decision making based on a price mechanism can be used to achieve an optimal assignment of priorities among competing requests [38]. Marchand objects to the head of the line priority rule because of its lack of flexibility. A strictly preemption rule has the same objective if a lower priority job is always interrupted by a higher priority job, regardless of its nearness to completion. Marchand proposes a discretionary priority rule in which a lower priority job will be interrupted if and only if its remaining service time is larger than some specified fraction of its initial service time. In consequence this improves the relative priority of a job as its remaining service time diminishes. This rule also encourages users to more carefully specify expected job times, as underestimation may cause early termination for insuttlcient resources. Similarly, gross overestimation would reduce their chances of reasonable turnaround times
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because the time to completion the job to be interrupted.
Suggestions
for Further
I,. Hamilton
would always be large and cause
Research
The scope of all research projects unfortunately must be limited to some extent in scope and methodology to be feasible and have some hope of completion. A number of topics in this paper indicated some potential for further exploration beyond the scope of this paper. Included are the following suggested projects: 1. Applications of the economics of incentives to examine the effects on users of pricing. 2. Real vs fiat money for perceptions of budget limitations. 3. Selection of the minimum set of resources in multiprogramming environment necessary to specify system pricing with cost recovery. 4. Comparison of computer pricing with electric and telephone utility pricing literature (e.g., peak load and congestion studies, and capacity costs). 5. Analysis of pricing when two or more nonhomogeneous computers are available in one center. Should pricing be used to control the work-load distribution between machines as well as on each one?
Summary A number of studies have been published concerning pricing policies for in-house computer services. This paper has discussed issues ranging from (1) whether to charge users (policy); (2) for what should the users be charged (resources); and (3) how and what to charge users (pricing) to provide insight into methodoIogy for solutions to the pricing problem. The differences in these areas are such that each must be reviewed comprehensively in developing an appropriate policy and algorithm for assignment of the costs of operating and providing computer services to other divisions within the firm. The estimated ability of the user to understand a system may become a strong subjective factor in the decision to implement a particular system.
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The author is grateful to Peter G. Sassone, James F. Courtney, Jr., James A. Largay, III, Bernell K. Stone, and F. E. Williams of the Georgia Institute of Technology and Ms. Susan Voss of Emory University for their helpful comments on earlier drafts. The author also acknowledges the suggestions of the anonymous referees. References 13
Management
Ac-
Alward, Sam A., How to Cost and Charge (September 1975): 54-59.
2.
Anthony, P., Functional Cost Accounting count. 58 (October 1976): 33-41.
3.
Baker, N. R., and Pound, W. H., R and D Project Selection: IEEE Trans. Eng. Management (March 1964): 124-134.
4.
Balachandran, V., and Stohr, E. A., Optimal Pricing of Computer Resources in a Competitive Environment, Northwestern Univ. Discussion Paper 268 (January 1977), Evanston, IL.
5.
Bernard, D., Emery, J. C., Nolan, R. L., and Scott, R. H., Charging for Computer Services, Wharton School of Finance and Commerce [AD A038 082) (February 1977), Philadelphia, PA.
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for
L. Hamilton,
Georgia Institute
of Technology
The problem of pricing computer services has been discussed in the literature since the early days of computing. Costing and pricing have often been interchanged in the discussions. After a discussion of the problems of measurement of resource usage, pricing for cost recovery and for resource allocation are discussed. The paper concludes with suggestions for further research and a brief summary.
We men of science may be momentarily truth will drive us on to eventual victory.
daunted,
but this unending Terry
quest for
and the Pirates
January
1947
Let us postulate a description of the operation and growth of the computing needs of a firm to aid in the analysis of the problem of charging for computer services. Consider a firm which has needs for administrative, financial, accounting, or production computation and record keeping, for example, which it feels could better be met through the capabilities of a computer center. The firm estimates that as it grows and develops, its computing needs will also develop and require different resources. Assume that the firm has decided to acquire and operate its own in-house computing facility rather than purchase the needed services from some external source. Now, let us define the planning horizon of the firm such that in the short run, both the capabilities of the equipment and the demands of the users are fixed. Over the long run, demand for computer use is not fixed and may increase as the firm gains experience with the computer [ 151. The long run is a period of sufficient duration such that more and/or different resources must be acquired to satisfy the needs of the users within the firm. In Address correspondence to: Kenneth L. Hamilton, College of Industriai Management, Georgia Institute of Technology, Atlanta, Georgia 30332. JOURNAL
OF BUSINESS
RESEARCH
@ Elsevier North Holland, Inc., 1980
139 0148-2963/80/02139-20$01.7$
Kenneth
140
L. Hamilton
addition, because the demand for data processing is derived from the demand function for the solution of other business problems, one expects the data processing demand function to be somewhat price inelastic [45]. (Demand may be viewed as a function of data processing capacity available.) However, for new applications, the reduction in the unit cost of computing (over time) which has been characteristic of the industry has been a major factor in promoting the continued development of these applications [9, 131. Research
Objective
A number of studies have been published concerning pricing policies for in-house computer services. In general, each deals with only one aspect of a multifaceted sequence of decisions. Authors have discussed issues ranging from (1) whether to charge users (policy); (2) for what should the users be charged (resources); (3) how and what to charge users (pricing); to (4) related areas such as accounting and utility peak-load pricing practices to provide insight into methodology for solutions to the pricing problem. The differences in these areas are such that each must be reviewed comprehensively in developing an appropriate policy and algorithm for assignment of the costs of operating and providing computer services to other divisions within the firm. The sensitivity of the user’s end cost to the set of all these factors is quite large. In practice, the estimated ability of the user to understand a system may become a strong subjective factor in the decision to implement a particular system [ 69, 451. A General Comment
on the Literature
Diamond [ 161 observes from Baker and Pound [3] that although many professionals had written papers in the field, relatively few had published more than one or two. This was interpreted as a lack of continuing study by researchers in this area. Examination of the computer pricing literature [28] also shows numerous authors with only one paper, a few with two, and a very few with more than two. Of the computer papers which have references, nearly every one cites Nielson, Smidt, Selwyn, and Sharpe. Few papers cite much subsequent work or each other’s current work. Baker and Pound also reported that very few of the approaches suggested in the R&D (research and development) literature were actually being put into practice. The reasons cited for this were
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difficulty in building realistic models, lack of historical data, and insufficient quantitative skills for the techniques proposed. A 1976 survey by Nolan [44] shows similar results. A number of papers have been written on the use of pricing for cost recovery and for resource allocation. Yet the level of practice is such that some computer centers operate under charging algorithms which encourage use of the scarce resources of the system and penalize through high charges the use of an alternative and more available resource. An example of this practice is the Control Data Cyber 74 system at the Georgia Institute of Technology, Atlanta, Georgia, which has a shortage of disk for both temporary working area and for permanent storage. Yet, the pricing system makes disk I/O (input/output activity) cheaper than tape I/O by a factor of over twenty to one. Similar cases have been noted at other computing centers. An Analytic
Model
A few analytic models have been proposed. Shaftel and Zmud [52] developed a mathematical programming model for flexible pricing. This model was limited by a constraint which was a function of several determinations of user utility and environment which could not be measured directly and thus made the model solvable only through an iterative process. Subsequent papers by Zmud [68, 701 added measures for service quality dimensions of user satisfaction or regret and a means to improve throughput. Balachandran and Stohr [4] gave computational examples and developed a model for optimal pricing of computer resources in a competitive environment. Although this model was based on a multiple center market, it could be adapted to the computer center which has multiple nonhomogeneous computing system which it must price and operate. Kriebel and Mikhail [35] present a model for dynamic pricing in computer center networks. Their paper also could be used to develop a pricing policy for the multimainframe center and includes an extensive bibliography. Marchand [37, 381 formulated a general equilibrium model to specify firstorder conditions that the prices and capacity of the facility must satisfy to be Pareto optimal. The model which follows is based on a synthesis of the above papers, with extensions to include the not for profit center and the disutility caused the ith user by the presence of all other users. It is the effort to model these factors more precisely that distinguishes this model from those referenced
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above. This formulation of the problem also allows cost-benefit analysis of the long-run capacity decisions. Model Specification There exists a computer center providing computational services within an organization. A set of some g resources of the total N resources available, some or all of which are limited in supply relative to demand, are selected to be monitored and priced. These resources may involve the time and space utilization of computer system resources including abstract factors such as priority, time of day, and measures of system quality [ 521 . In each budgetary control period t, t = 1, .-., T, the processing of a user request i for service consists of the production of a job i, i = 1, *.., M, which imposes demands Xi on the resources of the computing center. For example, for a particular job i, the Xi might correspond to lines printed, accesses to a magnetic tape, or seconds of central processor time. The total demand by all users at any point in time, X, is constrained by the technological restrictions R, inherent in the operating characteristics of the resource. In each control period, an externally imposed budget B is specified with which each user may purchase computer services. The set of prices {p} is specified at the beginning of the budgetary period and remains constant throughout the period. The function F of the maximum of each user’s utility function is not specified. This provides generality to the model in that this function will depend on the nature of the computing center management objectives. For example, one center may choose to maximize the utility of the least well-off user (F, a minimum function), and another may choose to maximize the average utility of all users. The fixed cost of operating all resources is given by the vector C, and MC represents the unit marginal cost of each resource. Problem of the ith User The ith user’s problem is to maximize the utility of the job initiated at time t and completed at time t’, given the disutility resulting from the delay and caused by the resource requirements of all m users, X, and within the externally imposed budget constraint Bi. The demands of all m users at any point in time are constrained by the technological capacities of the system {Rj} . Problem of the Computer Center The problem management faces is to maximize the (unspecified) function of each user’s maximized utility function by selecting (I) in the short run, the minimal set of nonzero prices {pi}, to control resource utilization, and (2) in the long run, the levels of resources {Rj} , which provide
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adequate levels of service such that the total revenue is approximately equal to total cost over some budgetary control period t, t = 1, e.0, T. An alternative nonsymbolic form of Nonsymbolic Specification the model is:l Select prices and resource levels that maximize some unspecified aggregate of the utilities obtainable by individual users from their budgets and the system resources while keeping total revenue approximately equal to cost.
Model Assumptions To solve this formulation, one must make some restrictive assumptions. Users have perfect knowledge of device characteristics and prices of the computer center. We also assume that the user is price elastic in that users will alter their patterns of service consumption in the long run. The numeraire or money involved is real and not artificial (“funny money budgets”). The problem for the computer center management assumes that demand can be estimated accurately enough, perhaps by using historical data, and that enough information can be found to make some determination of the user’s utility functions. Elnicki and Hughes [ 211 and Dolan [ 191 have reported empirical analyses to show that such assumptions are not unreasonable and appear to be borne out in practice. Dolan noted that a fundamental problem was lack of information about consumer behavior when prices are imposed. Elnicki and Hughes reported that establishment of a pricing system appeared to substantially change the consuming habits of both internally and externally funded users. Model Solution and Interpretation To simplify the solution and implementation process of a very difficult model, an iterative and numerical approach is taken. Assume that only three resources are defined for pricing at the outset: CPU time weekdays, weeknights, and weekends. As a starting point for the {pi}, we will use market prices. The {Rj} are fixed in the first period by estimates of resource requirements at the time of equipment acquisition. Since { Cj} and {Mcj} are functions of the {Rj} , they are also set for this period. We then announce resource constraints and prices and observe user demands on the system. We attempt to find out levels of user satisfaction (utility) explicitly by asking or implicitly by observing system usage. 1 This interpretation of the model was suggested to analysis of the problem.
by an anonymous
reviewer
as an aid
144
Terminology
Kenneth
1,. Hamilton
and No tation
p = n vector of unit prices of resources. indexed by j = 1, .-a, IZ of technological resource constraints, indexed by j = 1 ~-.*, n B = m vector of budgetary constraints, indexed by i = 1, .** m X = m X M matrix of resource requirements of all users, i= 1, *.., m on all resources. j = 1, .**, 12 xi = n vector of resource requirements of the ith job (user) on all resources, j = 1. --., n c = n vector of fixed costs of resources. indexed by j = 1, a**, n Mc = n vector of marginal costs for resources, indexed by j = 1, -.-, n R = n vector
After a period of observation, we then reevaluate the {pj} and {Rj}. We may choose F to be some function on user utility, say to make the average user better off. We then solve for the {pj} for the next period. We may also evaluate the {Rj} to determine if any resource constraints are binding or overly slack. If so, we might especially look for a low-cost way to change the {Rj} available by fine-tuning the system, or a change in the {pj} to modify user behavior. An alternative approach might be to start by setting {pj} nonzero for all resources and performing an analysis at the end of each period to determine which resources best measure machine usage and also which resources should be priced to control usage. From this point, we would continue the iterative process. While this process may seem slow or unduly complicated, such an iterative process seems to be the most likely way of converging to a set of prices to best control user behavior. The research of Dolan [ 191 and Elnicki and Howard [ 2 1 I suggests that prices can be used to increase demands during periods of low facility utilization, thereby extending the useful life of a given hardware con-
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Services
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figuration. A pricing scheme to which users do not react is a waste of computer center resources [ 581. We turn now to the underlying issue of the problem: should users be charged for their computer demands? Should Users Be Charged? A first point of discussion is to what extent should we concern ourselves with internal charges and prices? Collecting and processing development and operations data can tie up equipment and generate significant costs. It is not unusual for the resource monitoring and cost-accumulation system to use 5% of the computing resources [ 661. In addition, the billing process itself will generate additional costs in divisional and corporate accounting functions. Selwyn [48] has identified some organizational objectives and how they may be considered in pricing policy. Sharpe [ 531 has shown that internal profit centers maximizing their own profits do not maximize those of the entire organization. Turney [64] developed a cost-allocation model for transfer pricing of Management Information Systems (MIS). He encountered severe difficulty with determination of capacity. Since capacity is created by equipment resources, software, personnel, and computer design, a change in capacity may result from a change in any component of the system. Should only the users of that component be charged for the change, or should all users share in the charge, since the total capacity is improved? Cost determination with multiprogramming, multiprocessing computers was examined by Wiokowski and Wiokowski [67] , It was found that the job-stream mix could influence the use of resources. Considering all these preliminary difficulties, is it worth the effort to charge for in-house computer utilization and services or should they just be charged off as an overhead expense? Alward [ 11 asserts the decision to charge for computer services is not an unqualified “yes” in all instances for several reasons. One is the difficulty discussed above in determination of a fair, equitable, and understandable price structure. Another is the maturity and understanding of users and managers in computer processing. And yet, when the computer center is treated as an overhead item or free good, the incentive is to increase computer usage almost without limit [ 81. Consequently, there is a wide range of opinions on whether and how charge-backs should be used. Gill and Samet [23] present a pro and con paper on charging
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L. Hamilton
for computer usage in British universities. Gill argues for charging as a means of regulating the consumer. Samet responded with the case against charging and the use of money as an allocation control. The thrust of his argument was that using money as an allocation mechanism puts the burden on the wrong people. To make the user aware of the resource he is using, he must be charged something he can feel. Unless that something is “real” money (as opposed to “funny” or fiat money), the user is actually removed from feeling the charge and probably from worrying about it. It is, in fact, some administrator, such as a department head or manager, who must raise the funds and be concerned about their usage. Samet also points out that if the computer is not fully used, there is no need for any control function. On the other hand, if the demand exceeds the supply, charging serves no useful purpose as it does not make guaranteed time available even to those departments who can pay. Samet concludes it may therefore be necessary to have a rationing scheme to curtail the amount of time that anyone may purchase in a given period, leading to the very problems that a charging scheme is supposed to avoid. Clearly if the charging or control system is expected to encourage proper use of resources, it must be carefully designed [ 81. The consensus is that some controls must be exercised on computer usage as it is an expensive and easily misused good. A few firms have been successful in controlling use of the computer through an overview committee which approves computer project implementation and reviews ongoing projects for continuation or termination. A notable example is the data processing facility management of Southern Railways. In general, however, it appears that some form of a charging system is the more effective way of controlling usage. In the next section, we will discuss the first problem in establishing a charging system: that of measuring resource utilization and costs. Measurement
of Resource
Utilization
and Costs
In earlier computer systems, the hardware elements of the computer accounted for a significant portion of the capacity and cost of the computing system and center [50] . Other cost categories include keypunching and other data collection activities, programming support personnel, system management personnel, physical site facilities, air conditioning, maintenance, and expandable supplies such as punch cards, continuous forms, and the like
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[35], In general, these other costs will increase with hardware costs but some economies of scale appear to be present. Selwyn [ 501 found that the rate of increase in hardware costs was less than that of overall operating expense. Turney and Purdy [65] made a case study of management information system (MIS) departmental costs in one company and compared their results with a previous Diebold Group survey [ 181. Turney and Purdy separated MIS departmental costs into personnel (53%), hardware (32%), data processing supplies (6%) building (4%), and overhead (5%). (The share of departmental costs due to hardware alone is now steadily declining with technological advances, i.e., microelectronics.) Of the total costs, 42% could be traced to applications and 58% were joint costs. Of the traceable costs, 75% could be further associated with specific users (3 1.5% of the total). The measurement of nonhardware resources expended by a specific user is difficult [ 65 I. One would presume that accounting for hardware costs and traceability of hardware costs to users would be less difficult. Gladney, Johnson, and Stone [24] discussed some of the complex technical problems involved in measuring the resources delivered in multiprogramming and multiprocessing installations. Earlier programming systems in which a single user occupied the computer were relatively easy to handle in resource-usage accounting for cost assignment. With the advent of multiprogramming and timesharing, significant complexities arose. Elapsed time in multiprogramming systems is a function not only of the job, but also of the job mix and the environment. Cotton [ 111 says that timing is not a problem because most operating systems could determine actual running time for each program accurately. In contrast, Swoyer and Armstrong [62] show that since the hardware timer resolution of the IBM 360/65 is 16.6 ms, it is theoretically possible for a user either to run free or be overcharged by a factor of 13000 to one, depending on at which extreme of the clocking cycle his usage is measured. Resource requirements range from central processor (CPU) time to requirements for temporary and permanent storage. Storage of data outside the Central Processor or main memory means that some I/O must be done by the program during execution in order to get data and return results. It now becomes possible with a multiprogrammed machine for two users to be in contention for the device to which I/O is directed. For example, given that each user wants to read 1000 records of 1000 characters, either job
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L. Hamilton
running separately would take about 15 clock seconds (IBM 360/65). In a typical case with contention for position of the disk arm, each job running together could take as much as 90 clock seconds. Thus, a given job could take different times for execution, depending on the system load and the job mix. It is worth noting that this problem does not occur with tapes or nonshared disks, since only one job may access such a device at one time. The concern for adequate measures of usage leads to a discussion of reproducibility. Reproducibility is defined as the requirement that a job should “cost” or use the same amount of resources regardless of the time or job mix and is important for psychological and political reasons [ 251. Wildly varying prices are very disturbing to some users, and unpredictable prices make it more difficult for management to make up computer budgets. The rationale for concern about reproducibility is its relationship to the cost recovery and pricing structures. In the multiprogramming and multiprocessing environment, reproducibility becomes very difficult. It is not, however, essential to fairness, as fluctuations will average out in the long run. To illustrate these problems, the Fortran program used to manipulate the bibliography for this paper was run a number of times on weekdays and weekends. This program is input-output constrained, as it reads data in from one disk file and writes to another disk file to use a system-sorting routine to alphabetize authors and titles. It then reads the data back into memory and writes to the output file for printing in the desired formats. Nonparametric statistical analysis was used to evaluate program run times in seconds of CPU time and minutes of terminal connect time (elapsed wall-clock time). Runs were categorized by whether the run was Monday-Friday or Saturday-Sunday, irrespective of clock hour. There was a statistically significant difference between categories at the 0.007 level in elapsed wall-clock time (terminal connect time). The difference between categories in CPU times was not statistically different. There was, however, a statistically significant positive correlation between the elapsed wall-clock time and the amount of CPU time used (tested over the complete sample space). Given that the user is charged for both terminal connect time and for CPU time used, when there is such a variation that the mean elapsed clock time during the week is more than 4.3 times greater than on the weekend (16.56 min vs 3.83 min), a real difference in cost and consequently, perhaps, in user price can occur. Assuming that we have the technical capabilities
In-House
Computer
to measure pricing.
Services
consumption
149
of resources,
we turn now to the issues of
The Role of Price Price is defined by economists as a ratio of quantities: the amount of some good Y that must be given up to obtain a unit of some other good X. As an extreme example, one could speak in terms of price in clams per minute of computer time (in modern economics, price is normally quoted in terms of money as a medium of exchange). Consequently, unless a user actually gives up some resource in exchange for the computing services, he is not paying a “price.” Kreitzberg and Webb [34] carefully distinguish job cost from job price: job cost is the amount which it costs the installation to process a job; job price is that amount a user of the computer facility pays for having his job processed. The next section will address methods of pricing when the emphasis is on setting prices such that the price of each job is equal to the cost of each job and that the sum of all job prices will equal the sum of all job costs and of operating the installation for that period of time. Pricing for Cost Recovery From the supply side of the market, the issue of resource allocation is in a fundamental sense a question of cost allocation [35]. The supplies of computing services are characterized by (1) a high ratio of fixed costs to variable operating costs in the short run, (2) economies of scale, and (3) significant indivisibilities (step function in capacity increases) [35]. The high ratio of fixed-to-variable cost is especially true for pure computation since the machine rental generally accrues whether the machine is idle or is being used in production. Consequently, considerable attention has been devoted to the determination and assignment of these fixed costs among various users [ 1, 2, 7, 81, among others. This literature could be referred to as pricing for cost allocation [ 1 l] . The design of an adequate billing system for a modern computer system is not a simple task. Beyond economic efficiency and ease of administration, several additional qualitative criteria have been suggested for pricing (billing) rules in a computer services environment, e.g. [ 341. These criteria include: 1. Total cost recovery (including a “fair” return on the supplier’s investment) or else “limited losses” (i.e., an upper bound on the user subsidy);
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2. equity (i.e., treat all users “fairly,” such as by basing charges on the incremental cost caused by the user at the time of service); 3. reproducibility and auditability (i.e., the rule should facilitate the reproduction of the basis for the charges made and the monitoring of results); 4. ease of price estimation by users and revenue estimation by management (i.e., prior to servicing); 5. Pareto optimality for users in tradeoffs between quality of service provided and prices charged for fixed job requirements. In addition to this summary by Kriebel and Mikhail [3.5], the billing system provides a tool by which the computer can be evaluated for performance and cost/benefits [ 201. The selection of resources for measurement implies a choice problem of selecting the resources on which the costs are to be allocated [431. In an example of a nonmultiprogrammed computer, wall-clock time was an adequate measure since only a single user could access the machine. Only very rarely will a multiprogrammed computer have dedicated access (“block time”) usage. Consequently, some set of resources must be selected on which to base costing. It would appear that certain resources can be easily measured with one of several alternatives; for example, a printer could be measured in minutes, lines, or pages [42]. Even this has difficulties in application. In addition, the set of resource measures finally selected must be easily obtained from the operating system. It is important also to select the smallest possible set of resources for charging. A oneparameter cost measure may capture 80% of the user variance in the use of facilities, a two- or three-parameter measure 90% to 95%, and a loo-parameter measure will not do much better than a three-parameter measure [lo]. With more measures, it is true the center may do a better job of resource monitoring and management; however, the user reaches a saturation point in trying to plan jobs and estimate costs, and after a point, may just ignore the whole system [42]. Also, a pure cost-recovery system will normally consider only measurement of physical and not temporal resources. Cost recovery pricing may be based on full or average cost, marginal cost, market cost, and of course, a free good charged to overhead and reported to the user for information only. Using a fullcost approach, prices of each of the services are estimated by dividing the total cost of the corporate facility by the propor-
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tionate amount of resources required by one unit quantity of the service. Estimating the amount of resources required for one unit of service may not be too difficult; estimating before the fact the total amount of service required during the time period is very difficult 1493. This means the computer center must estimate total consumption since the price of each job is a proportionate amount of the total cost of the facility for the time period. If there were no estimate of consumption, then there would be no way to provide data for the budgets and analysis of the users. Generally, this concept uses historical data for cost factors. It also has inverse economic motivating factors: as the usage increases, the cost goes down and more usage is encouraged. Full-cost charging also may assure that all departmental costs are recovered but at the expense of suboptimal decisions or passing along extra expense to the users due to the poor performance of the computer division [ 53, 641. Market pricing techniques are based on the prices charged on the open market for like services. This practice is a desirable transfer pricing technique as it generally leads to optimal decisions [30] and there are no conflicts in fullfilling the criteria of transfer pricing. In some extreme cases (such as the majority of MIS services) the product is so unique that no external market exists for it. The administrative and organizational advantages however of the market price prompt some firms to initiate the use of a substitute system in situations where market price data is inadequate [641. The surrogate that is used is the negotiated price. Unfortunately, the negotiated price remains an artificial price and its use for decision making and evaluation purposes is therefore limited. The remaining aspect of charging for cost recovery still to be resolved is reproducibility of charges. This has received much attention in the earlier literature [24, 34, 54, 561, but Morris I401 in a 1976 paper comments that the only people who still seem concerned with the repeatability issue are those connected with installations which have not yet begun to bill their users. After these installations begin to charge, the discussion of reproducibility is seldom heard again. Pricing for resource allocation is Pricing for Resource Allocation distinct from the pricing for cost recovery discussed previously in that some of the constraints specified as goals of a billing system are now relaxed with the expectation that, in doing so, resources will be allocated and used in a more efficient manner. The first restraint relaxed is reproducibility in that users may be encouraged by lower prices to run their jobs during off-peak hours. Conse-
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quently, the user will expect that job costs to him will differ, and that some benefits, either in the way of increased response or lowered costs, will be available. With the addition of pricing as an allocating device, it is expected now that prices will not necessarily bear any relationship to the actual costs. For example, with average-cost pricing, as the work approached saturation, the average cost went to a minimum. Using this approach, as the workload of a resource increases, the price to the user may also increase in order to influence him to change his consumption habits. Conversely, as usage decreases or during the introduction of a new device, the price may be set very low to encourage use of that resource. The criterion that the price approach Pareto optimality for users in tradeoffs between quality of service received and prices charged becomes more significant. With a system for resource allocation the goal of the computer center, that revenue received equal operating cost, may still hold and be further complicated by the relationships between costs and prices. The use of pricing as a mechanism for allocating resources is generally thought to be a well-understood process. To be sure, the price mechanism does not always work as well as in theory [ 541. The Singer, Kanter, and Moore paper [54] is one of the better syntheses in the literature on pricing of computer services. Several authors [ 15, 22, 54, 551, have discussed the need for the use of pricing for resource allocation. Greenberger [ 261 considered pricing problems in a 1966 paper, looking at the effects of priority pricing on time share and batch users. He suggested the use of a bidding procedure as well as a nonlinear pricing model. The nonlinear model has interesting properties in that, while it can price strongly for high resource users, it is also very difficult for both the center and the user to estimate actual prices. Sutherland [60] reported on an application of a pricing system in use at Harvard University which used both bidding and fiat (“funny”) money. Smidt [55] commented on the problems of average-cost procedures and proposed a system of flexible prices. Greenberger [26] also examined variants of the priority rules which determine the access patterns for a given set of users, such as first come, first served and the c/t rule (the next job served is that with the highest waiting cost per service time). Priority rules of these types are equivalent to prices under the special assumption that all users place the same values on computer time and experience the same costs for waiting [ 541 . Under most conditions, priority rules will result in a misallocation relative to the one obtained by pricing.
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The one advantage of priorities over prices is that they are inexpensive to administer [54]. The flexible pricing concept is one where rates specifically are set to govern usage rather than to reflect costs of providing service or to enable a user to move ahead of another in the queue. The flexible aspect results from a periodic examination and readjustment of the prices. Diamond and Selwyn [ 171 and Nielson [41] give descriptions of flexible pricing implementations in university computing centers. Lehman [36] describes an implementation in a commercial research facility. The flexible pricing systems which have been implemented have tended to arrive at the pricing schedules through the intuitive abilities of those responsible for administering the pricing system [54]. Shaftel and Zmud [ 521 proposed an analytical technique for implementing a flexible pricing system based on mathematical programming. They found that, consistent with Diamond and Selwyn [ 171, control mechanisms in addition to flexible pricing are needed to satisfy all of management’s objectives concerning resource utilization. Priority pricing results from a somewhat different aspect of resource control. When a commodity cannot be stored, the producer is generally unable to adjust the supply to meet randomly fluctuating demands. If demand becomes greater than supply, some consumers must be put into queues. Marchand [37] points out that since consumers can differ significantly in the urgency of their requests, important social costs may be involved if adequate procedures are not available to favor the users whose requests are most important. The principal hypothesis of priority pricing is that decentralized decision making based on a price mechanism can be used to achieve an optimal assignment of priorities among competing requests [38]. Marchand objects to the head of the line priority rule because of its lack of flexibility. A strictly preemption rule has the same objective if a lower priority job is always interrupted by a higher priority job, regardless of its nearness to completion. Marchand proposes a discretionary priority rule in which a lower priority job will be interrupted if and only if its remaining service time is larger than some specified fraction of its initial service time. In consequence this improves the relative priority of a job as its remaining service time diminishes. This rule also encourages users to more carefully specify expected job times, as underestimation may cause early termination for insuttlcient resources. Similarly, gross overestimation would reduce their chances of reasonable turnaround times
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because the time to completion the job to be interrupted.
Suggestions
for Further
I,. Hamilton
would always be large and cause
Research
The scope of all research projects unfortunately must be limited to some extent in scope and methodology to be feasible and have some hope of completion. A number of topics in this paper indicated some potential for further exploration beyond the scope of this paper. Included are the following suggested projects: 1. Applications of the economics of incentives to examine the effects on users of pricing. 2. Real vs fiat money for perceptions of budget limitations. 3. Selection of the minimum set of resources in multiprogramming environment necessary to specify system pricing with cost recovery. 4. Comparison of computer pricing with electric and telephone utility pricing literature (e.g., peak load and congestion studies, and capacity costs). 5. Analysis of pricing when two or more nonhomogeneous computers are available in one center. Should pricing be used to control the work-load distribution between machines as well as on each one?
Summary A number of studies have been published concerning pricing policies for in-house computer services. This paper has discussed issues ranging from (1) whether to charge users (policy); (2) for what should the users be charged (resources); and (3) how and what to charge users (pricing) to provide insight into methodoIogy for solutions to the pricing problem. The differences in these areas are such that each must be reviewed comprehensively in developing an appropriate policy and algorithm for assignment of the costs of operating and providing computer services to other divisions within the firm. The estimated ability of the user to understand a system may become a strong subjective factor in the decision to implement a particular system.
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Services
The author is grateful to Peter G. Sassone, James F. Courtney, Jr., James A. Largay, III, Bernell K. Stone, and F. E. Williams of the Georgia Institute of Technology and Ms. Susan Voss of Emory University for their helpful comments on earlier drafts. The author also acknowledges the suggestions of the anonymous referees. References 13
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