UMTS integrated systems

THE JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOMMUNICATIONS Volume 14. Issue 4. December 2007

WANG Hui, LI Chun, JI Yang

Ondemand data broadcast scheduling based on AHP and GRA methods in wireless broadcast/UMTS integrated systems CLC number TN9 15

Document A

Abstract The increasing demand for interactive mobile multimedia service is causing the integration of 3rd generation (3G) cellular systems and wireless broadcast systems. The key challenge is to support data dissemination with low response time, request drop rate, and the unfairness of request drop. This article proposes a novel scheduling algorithm called DAG (on-demand scheduling utilizing analytic hierarchy process (AHP) and grey relational analysis (GRA)), which takes multiple factors-waiting time, number of active requests, deadline-into consideration, and models the data scheduling process as a multiple factors’ decision-making and best option-selecting process. The proposed approach comprises two parts. The first part applies AHP to decide the relative weights of multiple decision factors according to user requests, while the second adopts GRA to rank the data item alternatives through the similarity between each option and the ideal option. Simulation results are presented to demonstrate that DAG performs well in the multiple criterions mentioned above. Keywords AHP, DAG (on-demand scheduling utilizing AHP and GRA), GRA, on-demand data broadcast, scheduling

1 lntroductlon Advances in mobile computing and wireless networks have enabled the development of large-scale data-dissemination applications that provide information to vast numbers of mobile users. In cellular system, the data item is transmitted via different links for each client requesting it, and therefore, the load on the server and the network increases with the expansion of the client population. In contrast, data broadcast can satisfy the requirements of several potential clients using a single transmission. On the other hand, a pure wireless broadcast system always ignores the user requirements during

Received date: 2006-12-18 WANG Hui ( X ) ,LI Chun, JI Yang Key Laboratory of Universal Wireless Communications (Beijing University of Posts and Telecommunications) Ministry of Education, Wireless Technology Innovation Laboratory, Beijing 100876, China E-mail: [email protected]

Article ID

1005-8885 (2007) 04-0001-06

data dissemination, while a cellular system meets that through its uplink channel. The on-demand broadcast mode integrates the advantages of the cellular system and the broadcast system, where a large and dynamic client population requests data items from an information server and the server broadcasts data items in an ordered manner to the clients based on the requests [ 11. A key challenge in the design of the on-demand broadcast system is to devise a broadcast scheduling algorithm that provides good performance in responding to user request as quickly as possible, satisfying as many requests as possible and treating requests to different data items as equally as possible regardless of their popularity. Several researchers have proposed on-demand data broadcast scheduling algorithms in the literatures. Most of these [2,3] consider one decision factor. Such a single factor, for example the waiting time, the pending request number, or deadline, is not able to present the whole request urgency. Some others consider multiple decision factors [4,5], where the over-restricted assumptions, such as equal item length, etc., reduce the feasibility of the system models to a large extent. To increase the feasibility of the system model, some unnecessary constraints on the assumptions should be removed and more factors should be considered. Furthermore, multi-metric evaluation criteria should be taken into account to measure the performance of the scheduling algorithm, rather than using only one metric in the above algorithms. In this article, we propose a DAG algorithm (on-Demand data scheduling utilizing AHP [6] and GRA [7]) for data dissemination in the integrated system. Three factors, waiting time, pending requests, and deadline, which directly affect the performance of on-demand broadcast scheduling, are considered. The data scheduling process is modeled as a multiple factors’ decision-making and best option-selecting process, which is handled by AHP and GRA, respectively. The performance of the scheduling algorithm is not measured with only one metric, but is evaluated by comprehensive metrics: response time, request drop rate, and unfairness of request drop. The remaining sections of this article are organized as follows. Section 2 is devoted to the system model and the

2

2007

The Journal of CHUPT

important criteria for evaluating scheduling algorithms. AHP and GRA are introduced and applied to the on-demand data broadcast scheduling algorithm in Sect. 3. Section 4 exhibits and analyzes the simulation results. Finally, Section 5 presents the conclusions of this article.

Average response time ( R ) IS]: The mean value of the amount of time a client waits for an information item that it requires, is given by:

where N denotes the total number of user requests and R,

2 Systemmodel As seen in Fig. I , we consider a hybrid mobile broadcast model that captures all essential components of a typical on-demand broadcast system. Ihadcast server

I)o\rnlinh channel scheduler

Rcqucst

denotes the waiting time of the ith user request. Request drop rate ( D ) [4]: The ratio of the number of requests missing their deadlines to the total number of requests, which can be expressed as: N D = d N where N , denotes the total number of requests dropped. Unfairness of request drop (F) [9]: The statistical variance of the request drop rate, which can be referred as:

(3) where M denotes the total number of items in the server database and d, denotes the request drop rate of the ith item. Fig. 1 On-demand data broadcast system model

2.1 Model

In this model, all information requested is assumed to be available on the server. The database in the broadcast server is divided into several information items. The items are not necessarily of the same size. The time required to broadcast an item of unit length is referred to as one time unit. Hence, the time required to broadcast an item of length 1 is I time units. A large group of clients retrieve data items from a broadcast server. The clients send requests to the server through radio channel. Each request is characterized by a 3-tuple: , where i is the identifier of the request item, t is the time of request, and d is a relative deadline. The client monitors a downlink broadcast channel for the requested item until the item is broadcasted or the lifetime of the request expires. The uplink and downlink channels are independent. On receiving a request, the server inserts it into the request queue. At each broadcast instance, the scheduler selects a new item from the request queue. The selected item is sent to clients and the associated request (s) are removed from the request queue. 2.2

Objective

The primary goal of a scheduling algorithm is to try best to meet requests more quickly, satisfy more requests, and treat items reasonably. These can be measured by the following evaluation criteria:

2.3

Decision factors

Three factors influence these metrics directly, which are the waiting time, the request number, and the deadline. Waiting time ( a ): The amount of time for which a client waits for an information item that it requires. Request number ( p ): Number of active requests. A request is active if and only if its lifetime does not expire and it has not been handled. Deadline ( y) : The deadline represents the absolute (service) deadline of a request, given by t + d , beyond which the receipt of the requested item is considered invalid to the client. Thus, the optimized selection process can be expressed as: m,* = arg min I w ,D,F 1 [m,(a,P, y ) ~ (4) where m, denotes the ith alternative item.

8 Ondemand broadcast scheduling approach udng AHP and QRA We design the scheduling approach through integrating two mathematical techniques, the AHP and the GRA. The function of AHP [ l o ] is to determine the best solution to the complex problem by synthesizing all problem-decision factors. GRA is a method of selecting the best option among the comparative choices by building grey relationships with an ideal option [111. 3.1 AHP Implementation

AHP is a mathematical method that decomposes a complex

WANG Hui. et al.: On-demand data broadcast scheduling based on AHP and GRA methods in.. .

No. 4

problem into a series of decision factors, synthesizes their importance to the problem, and determines the best solution. 1) Decision Hierarchy The first step is structuring the problem as a decision hierarchy of independent factors. In the hierarchy as shown in Fig. 2, the goal of the decision “choosing an appropriate data item to broadcast” is at the topmost level. The subsequent level comprises the decision factors. The solution alternative data items are at the bottom level.

3

and then comparing with a random index ( I,, ), which is the average I,, of a random generated reciprocal matrix. All I,, values for different matrix dimensions are provided in Ref. [ 6 ] . If I,, is equal to zero, the matrix is perfectly consistent; otherwise, I,, should be positive. The ratio of I,, to I,,

for

the same dimension matrix is called the consistency ratio ( I,, ). Adjustment of the comparisons is required when

I , , > 0 . This process is repeated downward level by level to the bottom of the hierarchy 3.2

GRA implementation

GRA is a method that decides the best option through defining the similarity between each option and the ideal option. The more the similarity, the more preferable is the option. Grey relational coefficient ( I , , , ) is used to describe

Decision hierarchy for DAG

Fig. 2

2) Pairwise comparison The relative magnitudes of factors are estimated through painvise cornparison by asking the questions: “Which is more important? How much is the relative importance?”. The judgments are ranked on a 9-point scale in AHP. Numbers 1 to 9 are used to present equally, weakly moderately, moderately, moderately plus, strongly, strongly plus, very strongly, very very strongly, and extremely important to the objective, respectively. The comparison results are presented in a square matrix referred to as the AHP matnx A given below:

a

P

A,,,= is a scalar called eigenvalue. Since the comparisons

performed in AHP are subjective, judgment errors are inevitable and have to be detected by calculating a consistency index I,, of the AHP matrix, given by

I,, =-

-n n-1

where

Am,

=-c1

n

which are larger-the-better, the normalizations are performed as:

For deadline, which is smaller-the-better, the normalizations are performed as:

, denotes the ratio of the Ith factor weight to thejth

factor weight 3) Eigenvector and consistency The weights of the factors are achieved through calculating the eigenvector W of the matrix A with the equation A W =A,,,,, W (6) where

k = 3 here) entities corresponding to the decision factors. Then, each series is presented as E, = { e , ( l ) , e,(2),...,e , ( k ) \ , where i = 1. 2, ..., n , For waiting time and request number, (

Y (5)

where u,

the similarity. 1) Bound definition and data normalization The subsequent level comprises the decision factors. The solution alternative data items are at the bottom level. The performance of different data items is evaluated by GRA. We assume that n (the number of data items actively requested) series ( E , , E2,...,E n ) are compared, and each series has k

AW,

,=l

W,

(7)

where

j = 1 , 2 ,...,k ,

U , = m a x ( e , ( j ) , e 2 ( j ..... ) q 3 ( j ) } . L, =

min { e, ( j ) , e l ( j ) ,...,en( j ) I

.

2) Grey relational coefficient calculation The upper bound in larger-the-better and the lower bound in smaller-the-better are chosen to compose the ideal option E, = { e,(l), e, ( 2 ) ,. . .,e, ( k ) ) . The grey relational coefficients are calculated as:

The Journal of CHUPT

4

where max()/min() is the function of computing the maximum/ (1.1)

(1.1)

minimum value of a set of numbers varying with i and j , which are independent. The decision maker compares the I,,, of different data items, and selects the item with the largest I,,, as the next one to be broadcasted. 3.3

DAG using AHP and GRA

The whole algorithm is demonstrated in Fig. 3 and is described as follows: DAG algorithm I,,, = set of all factor values for items, which need to be

/* new requests are the ones that come after the time when an item was most recently transmitted or the time zero when no item was broadcasted before */ if IT,,,,, = 0 return else I;,,,,, =Normalize ( /,A,, ) GRC = GRA ( W ,I;ARGbT 1 i = the data item with the largest I,,, /* If this holds for more than one item, choose any one of them arbitrarily.*/ I, = the length of i

scheduled R = waiting time column in ITA,,;,, D = deadline column in I,,,,,,

delete all the requests for the ith item from the I,,,,,?,. reserve interval ( t , t+ I , ) to broadcast i tt=l,

t = earliest time slot at which scheduling can occur M = the total number of items in the server database Define the AHP matrix A W = AHP ( A ) Procedure DAG ( t ) I,ARtitT += value for new requests to data items

,

1)ata colcctor

----------

R+ = 1, For j t l t o M if ( D ( j ) < t )

delete all the requests for thejth item from the I.rAKGm End procedure

,

Data opcrator

;"Hi"';"I~

~

3ata arhitraor

+ .. .. .. .. .... ~ . . . . . . . . . . . . . . ~ -.-.y)......--.....~..~~..-~~ . ~ ~

; ; Stc;

2007

I

Database on conditions 01' ditfcrcnt itcrns

StcpZ(AllP)

Step 3(AffP)

comparison of factors Stcp4(;RA)

priority of factors

Stcp 6

Norma,i7ation

, ~

ordata

~

Calculation of the GRCs of ditlerent items

Dclinitiori 01' the ideal item to broadcast

,

T

Stell 7

1

Fig. 3

The AHP and GRA based scheduling approach

4 Slmulatlon results and analyds To evaluate the performance of our algorithm, it has been simulated and its performance has been compared with first come first served (FCFS) [ 2 ] and most requests first (MRF) [3]. Prior to analyzing the simulation and results, some important parameters and settings used in the simulation have been described. 4.1 Slmulatlon environment

We designed and set up our simulation environment according to the user activity model and the system model adopted by most researchers [12]. Amval of request: It is assumed to follow the Poisson Process, and A represents the average request intensity.

Demand probability: It is assumed to follow the Zipf distribution [ 131, expressed as:

P,=r (lli)' ; l
(12)

,=I

where B is a parameter named access skew coefficient. Different values of the access skew coefficient B yield different Zipf distributions. We set 0 = I for the famous "80-20" law, that is, most people only access 20% of the information while the remaining 80% is accessed by only a few. Length distribution: It is assumed to follow increasing distribution.

where

4

and L,

are parameters that characterize the

WANG Hui, et al.: Ondemand data broadcast scheduling based on AHP and GRA methods in...

No. 4

distribution. 4 and 4 are both positive integers. The round() function above returns a rounded integer value of its

.

argument [8]. Relative deadlines: It is assumed to follow normal distribution. p represents the mean, and the standard derivation is set to be p / 3 . The default and range values of the parameters are set as in Table 1. MATLAB 7 is used as the simulation tool. Table 1 Parameters and settings Symbol Run time (time unit)

M I (requests/ time unit)

4.2

Default I OOO I00

Range

100

[ O , ! 0001

e

I

4,

l

r,

10

5

compared with the FCFS and MRF algorithms, respectively. The reason is that FCFS ignores the popularity of item while the MRF algorithm only tries to satisfy the need of hot data item so that requests for infrequently accessed items must wait until sufficient requests have arrived. The success of DAG is owing to the fact that it takes into account the time constraints for satisfying more urgent requests earlier and provides more bandwidth to hot data items while avoiding starvation of cold data items.

Results and discussion

Figure 4 plots the average response time for different values of user request intensity. In average, 17% and 39% improvements are accomplished using DAG as compared with FCFS and MRF algorithms, respectively. The differentiated performance lies in the fact that both FCFS and MRF ignore factors that also influence the response time. In such case, the item with earliest request but which is requested by very few clients, or the item with most requests but a little waiting time will be broadcasted first; while in DAG both the waiting time and the request number are considered and the item is only broadcasted because it is popular (request by several clients) andor clients requesting for it have been waiting for a certain amount of time.

I.ainbda( reqfltime unit)

Fig. 5 Comparison of the request drop rate for different values of user request intensity

The unfairness of request drop is shown in Fig. 6 and it can be seen that according to the different values of user request intensity, 89% improvements are accomplished using DAG as compared with the MRF algorithm while a little worse than FCFS. This is because FCFS allocates the same bandwidth to all requested items regardless of their popularity, and therefore, performs slightly better than DAG In contrast, MRF is not a starvation-free algorithm; it is quite possible that a request for a very cold data item is never satisfied.

70 r

0.40

E

-M 0.35 0.30 cr ! i0.25 -; 0.20 3

r

-1

,g

0. I5

c

0.10

c

.a

a

0.05

I nu

101

10:

10'

Lambda(req'time unit)

Fig. 4 Comparison of the overall mean response time for different values of user request intensity

The request drop rate is shown in Fig. 5. It can be seen that according to the different values of user request intensity, 74% and 80% improvements are accomplished using DAG as

0

d

10'

101

10:

i 01

l.ambda(reqitime unit)

Fig. 6 Comparison of the unfairness of request drop for different values of user request intensity

From the above simulation results, it can be seen that the DAG approach performs considerably better than FCFS and

6

The Journal of CHUPT

MRF for all three metrics in most situations.

5 Concluslons In this article, we present a novel on-demand data broadcast scheduling approach DAG using a combination of AHP and GRA to evaluate user requests quantitatively and to rank the data item alternatives efficiently. AHP takes advantage of hierarchy and pairwise comparison, and GRA focuses on determining the difference between the comparative and ideal options. Unlike other approaches, we consider multiple decision factors, and weigh them based on their importance to user experience. Assumption of equal data item length is discarded in our algorithm, and multi evaluation metrics are used for measuring its performance. The simulation results reveal that the proposed scheduling approach can always guarantee quicker response, satisfy more requests, and treat data items reasonably. Acknowledgements This work is supported by EU project under the information society technologies (IST) Programme (04546 I ), the National Natural Science Foundation of China (607721 12).

References I . Franklin M, Zdonik S. A framework for scalable disseminationbased systems. Proceedings of the 12th Conference on Objectoriented Programming Systems, Languages and Applications, Oct 5-9, 1997, Atlanta, GA, USA. New York, NY, USA: ACM, 1997: 94- 105

2007

evaluation. IEEE Transactions on Parallel and Distributed Systems, 2006, 17 ( I ) : 3-14 5 . Aksoy D, Franklin M. RxW: A scheduling approach for large-scale on-demand data broadcast. IEEWACM Transactions on Networking, 1999, 7 (6): 846-860 6. Saaty T L. Fundamentals of decision making and priority theory with the analytic hierarchy process. Pittsburg, PA, USA: RWS Publications, 2000 7. Deng Ju-long. Introduction to grey system theory. The Journal of Grey System, 19x9. I ( I ) : 1-24 X. Vaidya N H, Hameed S. Scheduling data broadcast in asymmetric communication environments. Wireless Networks, 1999, 5 (3): 171-182 9. Wang Hao, Zhong Yu-zhuo. A novel schedule strategy based on

stream merging. Chinese Journal of Computers, 2001, 24(3): 325-230 (in Chinese) 10. Yu Hua, Shu Hua-ying. Risk analysis of telecom enterprise

financing. The Journal of China Universities of Posts and Telecommunications, 2005, 12(4): 103-107 I . Lin Chin-tsai, Yang Shih-yu. Selection of home mortgage loans using grey relational analysis. The Journal of Grey System, 1999, I I (4): 359-368 2. Jiang Shu, Vaidya N H. Scheduling algorithms for a data broadcast system: Minimizing variance of the response time. (1 998-02-04). http://historical.ncstrl.org/tr/ps/tamucsTTR98-(X)5.ps 13. Breslau L, Cao P, Fan L, et al. Web caching and Zipf-like distributions: Evidence and implications. Proceedings of Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies, Mar 21-25, New York, NY, USA. Los Alamitos, CA, USA: IEEE Computer Society, 1999: 126-134

2. Dykeman H D, Ammar M H, Wong J W. Scheduling algorithms

for videotex systems under broadcast delivery. Proceedings of IEEE International Conference on Communications, Jun 22-25, 1986, Toronto, Canada. Piscataway, NJ, USA: IEEE, 1986: 1847-185 1 3. Wong J W. Broadcast delivery. Proceedings of the IEEE, 1988, 76 (12): 1566-1577 4. Xu Jian-liang, Tang Xue-yan, Lee Wang-chien. Time-critical on-demand broadcast: algorithms, analysis, and performance

Biography: WANG Hui, Ph. D. Candidate in Beijing University of Posts and Telecommunications. interested in the research on convergent network of digital terrestrial and mobile communication, mobile multimedia.