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Performance evaluation of e-commerce requests in wireless cellular networks
Information and Software Technology 48 (2006) 393–401 www.elsevier.com/locate/infsof
Performance evaluation of e-commerce requests in wireless cellular networks Irfan Awan *, Suhani Singh Department of Computing, University of Bradford, Horton Building, Great Horton Road, Bradford, West Yorkshire BD7 1DP, UK Received 7 November 2005; accepted 2 December 2005 Available online 7 February 2006
Abstract Recent technological advances in mobile devices and wireless networks enable mobile users to order goods in an anywhere and anytime fashion. Quality of Service (QoS) provision is one of the most challenging issues in the heterogeneous wireless network-based e-commerce systems. Such e-commerce systems enable users to roam between different wireless networks operators and geographical areas while providing interactive broadband services and seamless connectivity. Due to movement of users during e-commerce requests, one of the most important QoS factors for successful completion of users’ requests is related to handover of request from one cell to another. A handover could fail due to unavailability of sufficient bandwidth in the destination cell. Such failure of ongoing e-commerce requests is highly undesirable and can cause serious problems to the e-commerce users and the service providers. This paper proposes an enhanced priority queuing based handover scheme in order to ensure a seamless connectivity of e-commerce requests. It focuses on the performance anaylsis of the proposed scheme. Experimental study demonstrates that the proposed scheme provides QoS with low connection failure and mean response time for handover of e-commerce requests. q 2006 Elsevier B.V. All rights reserved. Keywords: e-commerce; Performance evaluation; Wireless handover mechanism
1. Introduction Mobile communication has simplified and revolutionized the way we communicate today. The pervasive nature (anytime and anywhere connectivity) of the mobile communications is believed to be the key reason behind its growing demand. Wireless Cellular networks [1] provide mobile users with a freedom to move throughout the network from one cell to another. Such networks maximize the utilization of the scarce frequency spectrum by dividing a geographic area into small service cells that support operations on distinct frequencies (channels) [2]. Each mobile user communicates through a transmitter, called Base Station (BS), which then connects to the cellular network [3]. With mobile devices, users can search web information, download files and images, listen to music, and perform complex e-commerce (or mobile commerce [31,32]) transactions. For instance, using a mobile device a user may wish to * Corresponding author. Tel.: C44 1274 233952; fax: C44 1274 233920. E-mail addresses: [email protected] (I. Awan), ssingh7@gmail. com (S. Singh).
0950-5849/$ - see front matter q 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.infsof.2005.12.017
make travel arrangements for the upcoming Football World Cup which is to be held in Germany. A user can buy match tickets, book flight, reserve accommodation, arrange for carrental, and pay for all these services. There may be various alternatives for each of these services such as services for different hotels, bed & breakfast (B&B), car-rental, and taxihire. Such e-commerce request is generally of long duration as it involves different systems which are distributed across the network. Thus, users may issue such requests from one cell and roam to another cell during the processing of the requests. Subsequent discussion uses this Football World Cup example in order to demonstrate the proposed approach. It is very crucial that the e-commerce service providers provide users with continuous connections during the whole session of their requests. In wireless network-based e-commerce, requests are required to be handed over seamlessly from one cell to another without loss of data packets. This is managed by network based handover control mechanism that redirects the requests at an appropriate moment to the new mobile node. After the successful handover process, the user communicates through the BS in the new cell. If no channels are available during handover process, the request is blocked. Blocking of ongoing request can have pessimistic affect on the performance of the system. A frequent handover request can
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cause network overhead but if the request is delayed for too long then requests may be forcefully terminated. Thus, an efficient handover algorithm is the solution that reduces the blocking of handover requests at minimum network overhead. Because of the severe impact of handover requests on the performance of cellular systems, several handover schemes have been proposed and analysed (e.g. [4–8]). The two main approaches to handover mechanisms are: Channel Reservation and Queuing of handover requests [9]. In the Channel Reservation Handover approach (also known as the Guard Channel Scheme), a set of channels is solely reserved to service handover requests. For example, if a user has booked his flight for the upcoming Football World Cup whilst travelling and his train has entered into a new cell, user’s request will be handed over to the reserved channel in the destination cell for completion of the remaining tasks such as making hotel reservation, etc. This technique introduces handover priority over new requests. Hong and Rappaport [9] claim that this scheme decreases handover blocking probability, but Zhuang, Bensaou and Chua [10] through their research prove that the scheme wastes the reserved channels in situations where handover requests are minimum as is the case in residential areas. In addition, an experiment by Purzynski and Rappaport [11] shows that the Channel Reservation approach cannot provide fair Quality of Service (QoS) to different types of services efficiently (such as more bandwidth for wide-band handover requests) and is inefficient under varying traffic conditions. Queuing of handover requests is an outbreak to the Reservation handover scheme. In this scheme, handover requests are allowed to queue. The basic queuing discipline in queuing handover requests is First in First out (FIFO) [12]. Priority queuing includes Pre-emptive Resume (PR) and Head of Line (HoL) among others [29]. The queuing of requests is possible due to the overlapping of cells which is called the handover area. Requests remain in the queue until channels become available or the signal of the requests drop to a very low level. In the latter situation, the requests are blocked [13]. For example, in the Football World Cup e-commerce scenario if a channel is available and the users are still in the handover area, the channel is provided to the requests with the highest priority, depending on the type of queuing techniques used. As new requests are not given service until the queue is empty, it guarantees high priorities for handover requests. This prioritisation reduces the probability of forced termination of handover requests at the expense of an increased requests blocking probability [14]. But both these quantities determine the QoS of cellular systems. Though Fantacci [15] claims that the queuing scheme with FIFO policy exhibits performance very close to that of the ideal prioritized handover scheme and provides a quality service, Xhafa et al. [16] does not consider it to be the most efficient handover algorithm as it does not take into account dynamics of the user motion. Thus, in this paper, we propose an enhanced priority queuing handover mechanism with a buffer threshold. According to this mechanism, a buffer consists of two partitions. The first part, before threshold, is shared by both handover and new requests and the second part,
after threshold, is used to buffer only the handover requests. High priority is always given to handover requests through the queuing discipline namely Head of Line (HoL). This scheme ensures a seamless connectivity of long duration e-commerce requests. The system is modelled as a GE/GE/C/N/HoL queuing system with generalised exponential (GE) arrival and service times distributions, C channels, a finite capacity N and HoL priority scheduling rule. GE distribution is used to mod the burstiness of the traffic. To distinguish the work presented in this paper from previous studies, a comparative study of schemes based on queuing mechanisms is presented in Section 2. Section 3 explains the design criteria for the proposed model. Addressing the importance of modelling a realistic system, we briefly discuss the Generalised Exponential (GE) distribution. Section 4 illustrates the proposed model. Various experiments performed on the modelled system developed using QNAP-2 [17] are presented in Section 5 to validate the proposal. Simulation results demonstrate that the proposed scheme promises to provide QoS with low loss probability, Mean Queue Length (MQL) and mean Response Time of handover requests to new requests. The system is further developed to verify that the proposed scheme is more efficient than the existing queuing mechanisms. Results with high and increased throughput for handover and new requests, respectively, justified its efficiency. Section 6 concludes the paper and highlights the future prospects. 2. Related work One of the earliest methods to prioritise handover requests is Guard Channel Method developed by Guerin [18]. Guerin’s novel approach uses a combination of channel reservation [19] and queuing mechanism [20]. This approach reserves a number of channels for handover requests and allows new requests to buffer in an infinite queue. In contrast to the conventional handover, queuing of new requests minimises the blocking of fresh requests while maintaining low probability of handover blocking by reserving channels. Hong and Rappaport [9] tested a similar approach to Guerin but the authors only permitted handover requests to buffer in infinite queue with FIFO policy. Infinite queue may not represent a realistic system. Research by Chang et al. [21] is another approach to prioritise handover requests in which handover requests and fresh requests are queued separately in finite queues, differing from the techniques used by Guerin [13], Hong and Rappaport [9]. In addition, it enables the queued new requests to renege and queued handover requests to drop if they move outside the handover area before the handover request is granted. The authors believe that finite queuing mimics the real world better than infinite queuing although implementing two separate queues may be a costly approach. Jabbari and Terinay in [22] consider the movement of mobile users with different speeds. Thus, alternative to FIFO, commonly used by previous researchers, Jabbari and Tekinay promote a non-pre-emptive dynamic priority queuing discipline for handover requests called the measurement based priority scheme (MBPS) based
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on power measurements. The rate of degradation of the power levels of the queued requests are measured continuously. When a channel becomes free, the request with the lowest signal level is granted the service. The simulation results found in [22] indicate that the proposed scheme offers a better QoS with lower handover blocking probability, spectrum utilization and less delay. Xhafa and Tonguz [16] underline that the scheme does not consider the dynamics of user mobility as mobile users move with different speeds. In light of the aforementioned studies, a non-pre-emptive priority queuing scheme namely HoL with shared finite queuing is proposed. The proposed queuing scheme is further enhanced by introducing a cut-off priority technique using queue threshold. This technique is believed to improve the efficiency of cellular networks without a detrimental effect on the perceived QoS. 3. The proposed system A system with two types of users generating handover requests (HO) and new requests (NR) are considered to have access to a set of channels. In Football World Cup scenario, a fresh request for buying ticket, booking flight, etc. generated by a user within a cell is regarded as a NR and the a request generated by the roaming users in the neighbouring cell is treated as a HO. This system is regarded as a single cell with static channel assignment [23]. A system modelled as a single cell is sufficient for evaluating the performance of the proposed handover algorithm. HO and NR are treated equally once they occupy the channels, thus, both the requests have same service rate but distinct arrival rates. The additional features of the system are as follows. HO requests have full access to all the channels and queue in the system without any restriction whereas NR are restricted to queue only until the finite queue reaches a predefined value, threshold. When the queue reaches the threshold, then only HO requests are queued and NR are blocked. Such an approach has the advantage of improving the perceived QoS since fewer high priority requests (HO) will lose the connection due to the additional protection introduced by the space priority. For example, during the handover and new connection request, if all the channels are occupied, then both the requests are allowed to queue. If the queue already holds HO and NR, then when a new handover request is made, the handover request is placed before all the queued new requests following the HoL queuing discipline but remains at the end amongst the handover requests, the secondary queuing policy among HO requests being FIFO. This gives HO requests more priority than NR. Permitting new requests to queue does not block the requests if no channels are free but simply delayed and will ultimately receive the service, contributing to the increasing carried traffic. No requests are pre-empted once the channels are allocated. When a channel becomes free upon completion of a request, it is assigned to the request at the head of the queue.
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loss probability. In order to satisfy the blocking probability requirement, HO and NR traffic rate has to be estimated accurately under realistic assumptions. Firstly, we consider that the cellular traffic is bursty in nature, meaning requests tend to arrive in bulks. Poisson distribution (cf. [24,25]) is the most commonly used distribution to model the number of events occurring within a given time interval. Until when Chlebus and Ludwin [26] first questioned if handover traffic was always Poissonian, most of the models assumed to have Poissonian traffic to model the number of events occurring within a given time interval. A Poisson arrival process does not adequately characterise arrival traffic for cellular networks as the theory assumes one arrival at random time. It is only applicable in a non-blocking environment as in telephone networks, but not in growing internet traffic. In cellular networks, there exists request blockage during insufficient resources available. Supporting the research, we consider that the cellular traffic is bursty in nature, meaning requests tend to arrive in bulks. Section 3.2 briefly describes the Generalised Exponential (GE) Distribution [28,29] which is used to model the traffic with burstiness property. 3.2. The GE distribution The GE distribution is a mixed interevent-time distribution of the form [28]: FðtÞ Z PðX% tÞ Z 1Kt eKst ;
t% 0;
where tZ2/(C2C1), sZtn and X is the interevent time random variable (rv). {1/n, C2} are the mean and the Squared Coefficient of Variation (SCV), the ratio of the variance to the square of the mean, of the interevent time distribution X, respectively. Raad et al. [30] states that all distributions can have a SCV higher than 1 except the exponential distribution which has a coefficient of 1. SVC provides an important measure of the variability of the distribution [27] (Fig. 1). The counting process of the GE distribution is a Compound Poisson Process (CPP) with parameter 2n/(C2C1) and a geometrically distributed batch sizes with mean l/t, (C2C 1)/2 and SCV, (C2K1)/(C2C1). GE distribution is useful for modelling random variables with SCV greater than 1. The GE distribution has a memory-less property [28] like Poisson distribution. The GE distribution is considered to be the most appropriate distribution to model the inter-arrival times of handover and new requests as it supports simultaneous arrivals, unlike Poisson distribution. In this context, the
3.1. Traffic models As discussed above, one of the key parameters that evaluates the performance of the system is the HO and NR
Fig. 1. The GE distribution with parameters t and s (0%t% ).
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Fig. 2. The proposed priority queuing handover scheme with HoL policy and threshold in finite buffer.
burstiness of the arrival process is characterised by the SCV of the inter-arrival time [28]. 4. Model description In this paper, the proposed GE/GE/C/N/HoL queuing system (cf. Section 3) has been used to model a cell with HO and NR inter-arrival and service times at each channel having GE distribution (cf. Fig. 2). C represents the total number of channels available in the cell and N is the total capacity of the queue to temporarily hold the incoming HO requests and NR. Free channels are assigned to the requests in the queue according to HoL scheduling discipline. This adds to the unique features of the model from the existing ones. Each cell is considered to be a queue and each channel as a server. Restricted number of requests capacity in the queue mimics the limited number of channels available due to scarce frequency spectrum availability. Let, i represents the classes of requests (handover and new requests), li and mi represent the mean arrival rate and service rate, Cai2 and Csi2 are the SCVs of the inter-arrival and service times for the ith class, respectively. We consider that the values of mi and Csi2 for all types of requests are the same as we assume that all channels have the same service rates. When a GE arrival process with rate li is sampled with probability tZ 2=ðCai2 C 1Þ, the GE arrival rate (sZtli) of the request will be 2li ðCai2 C 1Þ and, the GE inter-arrival time is ðCai2 C 1Þ=2li . Likewise, for the GE service process with rate mi, the probability that the request will receive service is 2mi ðCsi2 C 1Þ and, the GE inter-service time is ðCsi2 C 1Þ=2mi .
Handover Mechanism where several users want to make flight reservation and book accommodation for the Football World Cup using their mobile phones. Request from these users can be of handover type if generated in other cells or fresh ones. Different sets of experiments have been carried out. In each set, either the test criteria or the environment is changed. Firstly, the outputs of the experiments performed on the proposed enhanced priority queuing system with HoL policy and buffer threshold are captured and analysed. Then, the performance of the proposed system is compared with the conventional handover, non-priority queuing scheme under FIFO policy, and priority queuing scheme with finite complete buffer sharing (CBS) scheme under HoL policy (cf. Table 1). The performance evaluation of the system under consideration is conducted at two levels. The first level models the number of channels in the cell as the varying input parameter and the second level models the handover mean arrival rate as a varying input parameter (cf. Table 2). Both determine the performance of the system on the basis of Mean Queue Length Table 1 Simulation experiments: major scenarios Scenario
Description
Comments
I
Conventional handover scheme
II
Non-priority queuing handover scheme Priority queuing handover scheme—without threshold
Only handover requests are queued. New requests are blocked if no channels are available during channel request Both requests are queued in finite buffer with FIFO policy Both requests are queued in shared finite buffer with HoL policy but no threshold Both requests are queued in shared finite buffer with threshold and HoL policy
III
5. Numerical results IV
The simulation model developed QNAP-2 [17] is used to experiment with various scenarios applicable to Queuing
Priority queuing handover scheme—with threshold
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Table 2 Simulation experiments: system input parameters System parameters
SET A—variation in number of channels
SET B—variation in handover traffic load
Number of channels Queue capacity Threshold value (only applicable for scenario IV) Handover arrival rate SCV for handover arrival requests Handover service rate SCV for handover service rate New requests arrival rate SCV for new arrival requests New requests service rate SCV for new requests service rate
1–8 20 15
3 20 15
3 3
1.0–4.5 3.0
8.0 3.0 7.0 3.0 8.0 3.0
8.0 3.0 7.0 3.0 8.0 3.0
(MQL), Response Time, and loss probability for HO and NR. The proposed model has been tested for various scenarios listed in Table 1. The following input parameters have been used in all simulation experiments. Each experiment is subdivided into two sets: SET A: number of channels is varied from 1 to 8 SET B: handover traffic load is varied from 1.0 to 4.5. In residential areas, most of the traffic is generally assumed to be of type new requests. Hence, the handover arrival rate in each set of experiments is less than the new requests arrival. All the channels provide the same service to either of the requests. Therefore, the service rate for both types of requests is kept the same. SCV for both types of requests are put higher than 1 to model burstiness of the traffic. To minimize system congestion, arrival rate of both types of requests are left lower than the service rate. The simulation results for the modelled system is analysed in two steps: Step 1. Performance evaluation of the proposed Model Step 2. Comparative performance evaluation of the proposed scheme with existing queuing handover techniques (cf. Table 1).
Fig. 3. Loss probability of HO and NR in priority queuing handover with buffer threshold.
Table 3 Simulation results: experiment 1, SET A summary Scenario
IV
SET A Loss probability for handover requests
Loss probability for new requests
5.188!10K2–3.347!10K6
3.464!10K1–1.687!10K4
represents the system performance during scarce spectrum resource (cf. Table 3). Another experiment on priority queuing scheme with HoL policy and buffer threshold (as shown in Fig. 4) demonstrate the impact of HO traffic load on the loss probabilities for HO and NR. It is understood from the graph that the loss probability for HO and NR requests increases with the increase in the HO traffic load. It can be seen that for higher traffic load the difference between the loss probabilities of NR and HO requests increases. The loss probability for HO requests is much less than that of new requests (cf. Table 4). Experiment 2—MQL. The results obtained from the experiment for MQL with the increasing number of channels are presented in Fig. 5. The MQL for HO requests are lower than that of NR. The difference in the MQL between HO and NR decreases with the increase in the number of available channels in the system. The graph proves that for minimum channel resources, MQL for handover is less than that for NR (cf. Table 5).
5.1. Performance study Experiment 1—loss probability. Fig. 3 shows the results from the experiment when HO and NR perform the priority queuing scheme with HoL policy and buffer threshold in increasing number of channels. The graph highlights the loss probability of HO and NR. Initially, the loss probability for HO requests is much less than that of the new requests but the loss probability of high priority (HO) and low priority (NR) requests decreases with the increase in the number of available channels. The high loss probabilities of NR and low loss probability of HO when there is minimum channel resource
Fig. 4. Loss probability of HO and NR in priority queuing handover with buffer threshold.
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Table 4 Simulation results: experiment 2, SET B summary
Table 6 Simulation results: experiment 2, SET B summary
Scenario
Scenario
IV
SET B Loss probability for handover requests
Loss probability for new requests
6.99!10K5–1.003!10K3
2.927!10K3–1.406!10K2
Fig. 5. MQL of HO and NR in priority queuing handover with buffer threshold.
Fig. 6 demonstrates the impact of increasing HO traffic load on MQL for NR and HO requests. The graph illustrates that the increase in HO traffic load in the system is directly proportional to the increase in MQL of the requests. Through HoL policy and buffer threshold technique, the model promises lower MQL for HO requests than of NR though the queues as the HO traffic load increases (cf. Table 6). Experiment 3—response time (delay). Fig. 7 represents the system response time for both types of requests against various number of channels. The delay for NR is comparatively very high to HO when channel resource is less. The given graph shows the inverse relationship of increasing number of channels in the system to Response Time. When the channel number in Table 5 Simulation results: experiment 2, SET A summary
MQL for handover requests IV
IV
MQL for new requests
1.765–3.738!10K1
8.466–8.773!10K1
Fig. 6. MQL of HO and NR in priority queuing handover with buffer threshold.
–8.059!10
K1
MQL for new requests 1.185–1.484
minimum, response time for HO requests is less than that for the NR. The graph shows that the Response for HO is very less as compared to NR (cf. Table 7). Another experiment has been carried out to see the behaviour of the system’s response for increasing HO requests. The graph (cf. Fig. 8) shows a gradual increment of response time for HO and NR when the HO traffic load increases. The response time for HO requests is lower than that for NR. The increasing traffic load does not make significant impact on Response Time of HO and NR unlike in experiment 2, SET B (cf. Fig. 6, Table 8).
Table 7 Simulation results: experiment 3, SET A summary
SET A MQL for handover requests
1.544!10
K1
Fig. 7. Mean response time of HO and NR in priority queuing handover with buffer threshold.
Scenario Scenario
SET B
IV
SET A Response time for handover requests
Response time for new requests
6.193!10K1–1.251!10K1
1.851–1.254
Fig. 8. Mean response time of HO and NR in priority queuing handover with buffer threshold.
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Table 8 Simulation results: experiment 3, SET B summary Scenario
IV
SET B Response time for handover requests
Response time for new requests
1.54!10K1–1.802!10K1
1.701!10K1–2.162!10K1
5.1.1. Summary for STEP 1 of simulation Experiments 1–3, validates that the proposed priority queuing handover mechanism in finite queue provides QoS by lower loss probability, MQL and mean Response Time of HO to NR. This means the scheme takes into account that HO requests are of higher priority and the blocking them can result in poor service. As previously discussed, the QoS for the modelled system is defined as low handover blocking probability and minimized new request blocking probability. In both the instances, in experiment 1, the loss probability of HO is less than that of NR. Graphs produced by experiment 2 justifies that the MQL for HO request is lower than that of NR. Likewise, response time for NR requests is higher than the HO requests in the proposed system. This simulation model proves that the proposed system minimizes the trade-off of handover blocking probability with high request blocking probability. To validate further that the performance of the proposed queuing mechanism is better than the existing queuing handover schemes, comparative results are generated. These results are analysed in Section 5.2. 5.2. Performance comparison Experiment 4—throughput. Throughput shows the efficiency of the system; the higher the value the better the system’s efficiency. Figs. 9 and 10 represent the results obtained from the experiments when HO and NR performed under different scenarios (cf. Table 1). It is clear from the
Fig. 10. Throughput of new requests under different scenarios in increasing number of channels.
graphs that for both types of requests, the throughput increases by increasing the number of channels. Minimum availability of channels shows that the throughput of HO and NR differs distinctly in different scenarios. Though the conventional queuing mechanism shows highest throughput of HO in (Fig. 9), its lowest throughput for NR (cf. Fig. 10) penalises its efficiency as it decreases request-to-traffic. Non-priority with FIFO policy and priority with CBS shows better performance than conventional queuing but the proposed priority queuing with buffer threshold outperforms by increasing the throughput of HO greatly with slight decrease in the throughput of NR (cf. Table 9). Experiment 5—loss probability. A similar experiment to one discussed above, is carried out to study the implication on loss probabilities of HO and NR with the changes in input parameters for the HO traffic rate. The results are summarised in Figs. 11 and 12. The graphs illustrate that the loss probabilities for HO and NR increases by increasing the traffic load for HO requests. The loss probability of NR for Conventional Queuing is lowest at all times. This is penalised with the high loss probability of NR. Priority with buffer threshold balances the new request blocking probability and loss probability for HO requests (cf. Table 10). Non-priority-FIFO and priority-CBS have low loss probability for new requests but the loss probabilities are high for HO. When HO are requests of high priority, high HO blocking Table 9 Simulation results: experiment 4, summary
Fig. 9. Throughput of HO requests under different scenarios in increasing number of channels.
Scenario
Throughput for handover requests
Throughput for new requests
I II III IV
2.98671!10K1–2.99408!10K1 2.28073!10K1–2.98364!10K1 2.29499!10K1–2.98364!10K1 2.84989!10K1–2.98803!10K1
2.36758!10K1–6.97863!10K1 5.30436!10K1–6.98922!10K1 5.29663!10K1–6.98922!10K1 4.57486!10K1–6.99390!10K1
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Fig. 11. Loss probability of HO requests under different scenarios in increasing traffic.
A handover could fail due to unavailability of sufficient bandwidth (free channels) in the destination cell. Such failure of ongoing e-commerce requests is highly undesirable and can cause serious problems to the e-commerce users and the service providers. This paper investigated the existing queuing handover mechanisms and proposes an enhanced priority queuing based handover scheme in order to ensure a seamless connectivity of e-commerce requests for their successful completion. It focuses on the performance anaylsis of the proposed scheme. Experimental study demonstrates that the enhanced priority queuing scheme with buffer threshold outperforms the existing queuing schemes and provides QoS with low connection failure and mean response time of handover e-commerce requests. References
Fig. 12. Loss probability of NR under different scenarios in increasing traffic. Table 10 Simulation results: experiment 5, summary Scenario
I II III IV
SET A Loss probability for handover requests
Loss probability for new requests
1.901!10K3–0 2.419!10K1–0 2.383!10K1–0 5.188!102–3.347!10K6
6.608!10K1–3.15!10K3 2.419!10K1–2.862!10K6 2.398!10K1–2.862!10K6 3.464!10K1–1.687!10K4
probabilities means bad system performance. Contrast to this, priority queuing with buffer threshold provides low priority blocking and acceptable slight increase in request blocking probability. 6. Conclusion and future prospects Mobile computing is encouraging users to get maximum benefits from e-commerce systems from their mobile devices whilst roaming between different wireless networks. Due to movement of users during e-commerce requests, one of the most important QoS factors is related to handover of requests from one cell to another.
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I. Awan, S. Singh / Information and Software Technology 48 (2006) 393–401 [15] R. Fantacci, Performance evaluation of prioritized handoff schemes in mobile cellular networks, IEEE Transactions on Vehicular Technology 49 (2) (2000) 485–493. [16] A.E. Xhafa, O.K. Tonguz, Dynamic priority queuing of handover calls in wireless networks: an analytical framework, IEEE Journal on Selected Areas in Communication 22 (5) (2004) 904–916 May 2004. [17] M. Veran, D. Potier, QNAP-2: a portable environment for queuing systems modelling, in: D. Potier (Ed.), Modelling Techniques and Tools for Performance Analysis, North Holland, Amsterdam, 1985, pp. 25–63. [18] R. Guerin, Queueing-blocking system with two arrival streams and guard channels, IEEE Transactions on Communications 36 (2) (1988) 153–163. [19] E.C. Posner, R. Gueri, Traffic Policies in Cellular Radio that minimize blocking of Handoff Calls, ITC-11, Kyoto, 1985. [20] Unknown Author, ‘Cellular Communication-Digital Systems’, IEC: On Line Education (IEC.org), [Available: http://iec.org/online/tutorials/ cell_comm/topic06.html?Next.xZ40&Next.yZ8, Accessed: April 20, 2005], 2004. [21] Chung-Ju Chang, Tian-Tsair Su, Yueh-Yiing Chiang, Analysis of a cutoff priority cellular radio system with finite queueing and reneging/dropping, IEEE/ACM Transactions on Networking 2 (2) (1994) 166–175. [22] S. Tekinay, B. Jabbari, A measurement-based prioritization scheme for handovers in mobile cellular networks, IEEE Journal on Selected Areas in Communications 10 (1992) 1343–1350. [23] H. Schulte, W. Cornell, Multi-area mobile telephone system, IRE Transactions on Vehicular Communications 9 (1) (1960) 49–53. [24] H.G. Ebersman, O.K. Tonguz, Handoff ordering using signal prediction priority queuing in personal communications systems, Sixth IEEE International Symposium on Personal, Indoor and Mobile Radio
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Performance evaluation of e-commerce requests in wireless cellular networks Irfan Awan *, Suhani Singh Department of Computing, University of Bradford, Horton Building, Great Horton Road, Bradford, West Yorkshire BD7 1DP, UK Received 7 November 2005; accepted 2 December 2005 Available online 7 February 2006
Abstract Recent technological advances in mobile devices and wireless networks enable mobile users to order goods in an anywhere and anytime fashion. Quality of Service (QoS) provision is one of the most challenging issues in the heterogeneous wireless network-based e-commerce systems. Such e-commerce systems enable users to roam between different wireless networks operators and geographical areas while providing interactive broadband services and seamless connectivity. Due to movement of users during e-commerce requests, one of the most important QoS factors for successful completion of users’ requests is related to handover of request from one cell to another. A handover could fail due to unavailability of sufficient bandwidth in the destination cell. Such failure of ongoing e-commerce requests is highly undesirable and can cause serious problems to the e-commerce users and the service providers. This paper proposes an enhanced priority queuing based handover scheme in order to ensure a seamless connectivity of e-commerce requests. It focuses on the performance anaylsis of the proposed scheme. Experimental study demonstrates that the proposed scheme provides QoS with low connection failure and mean response time for handover of e-commerce requests. q 2006 Elsevier B.V. All rights reserved. Keywords: e-commerce; Performance evaluation; Wireless handover mechanism
1. Introduction Mobile communication has simplified and revolutionized the way we communicate today. The pervasive nature (anytime and anywhere connectivity) of the mobile communications is believed to be the key reason behind its growing demand. Wireless Cellular networks [1] provide mobile users with a freedom to move throughout the network from one cell to another. Such networks maximize the utilization of the scarce frequency spectrum by dividing a geographic area into small service cells that support operations on distinct frequencies (channels) [2]. Each mobile user communicates through a transmitter, called Base Station (BS), which then connects to the cellular network [3]. With mobile devices, users can search web information, download files and images, listen to music, and perform complex e-commerce (or mobile commerce [31,32]) transactions. For instance, using a mobile device a user may wish to * Corresponding author. Tel.: C44 1274 233952; fax: C44 1274 233920. E-mail addresses: [email protected] (I. Awan), ssingh7@gmail. com (S. Singh).
0950-5849/$ - see front matter q 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.infsof.2005.12.017
make travel arrangements for the upcoming Football World Cup which is to be held in Germany. A user can buy match tickets, book flight, reserve accommodation, arrange for carrental, and pay for all these services. There may be various alternatives for each of these services such as services for different hotels, bed & breakfast (B&B), car-rental, and taxihire. Such e-commerce request is generally of long duration as it involves different systems which are distributed across the network. Thus, users may issue such requests from one cell and roam to another cell during the processing of the requests. Subsequent discussion uses this Football World Cup example in order to demonstrate the proposed approach. It is very crucial that the e-commerce service providers provide users with continuous connections during the whole session of their requests. In wireless network-based e-commerce, requests are required to be handed over seamlessly from one cell to another without loss of data packets. This is managed by network based handover control mechanism that redirects the requests at an appropriate moment to the new mobile node. After the successful handover process, the user communicates through the BS in the new cell. If no channels are available during handover process, the request is blocked. Blocking of ongoing request can have pessimistic affect on the performance of the system. A frequent handover request can
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cause network overhead but if the request is delayed for too long then requests may be forcefully terminated. Thus, an efficient handover algorithm is the solution that reduces the blocking of handover requests at minimum network overhead. Because of the severe impact of handover requests on the performance of cellular systems, several handover schemes have been proposed and analysed (e.g. [4–8]). The two main approaches to handover mechanisms are: Channel Reservation and Queuing of handover requests [9]. In the Channel Reservation Handover approach (also known as the Guard Channel Scheme), a set of channels is solely reserved to service handover requests. For example, if a user has booked his flight for the upcoming Football World Cup whilst travelling and his train has entered into a new cell, user’s request will be handed over to the reserved channel in the destination cell for completion of the remaining tasks such as making hotel reservation, etc. This technique introduces handover priority over new requests. Hong and Rappaport [9] claim that this scheme decreases handover blocking probability, but Zhuang, Bensaou and Chua [10] through their research prove that the scheme wastes the reserved channels in situations where handover requests are minimum as is the case in residential areas. In addition, an experiment by Purzynski and Rappaport [11] shows that the Channel Reservation approach cannot provide fair Quality of Service (QoS) to different types of services efficiently (such as more bandwidth for wide-band handover requests) and is inefficient under varying traffic conditions. Queuing of handover requests is an outbreak to the Reservation handover scheme. In this scheme, handover requests are allowed to queue. The basic queuing discipline in queuing handover requests is First in First out (FIFO) [12]. Priority queuing includes Pre-emptive Resume (PR) and Head of Line (HoL) among others [29]. The queuing of requests is possible due to the overlapping of cells which is called the handover area. Requests remain in the queue until channels become available or the signal of the requests drop to a very low level. In the latter situation, the requests are blocked [13]. For example, in the Football World Cup e-commerce scenario if a channel is available and the users are still in the handover area, the channel is provided to the requests with the highest priority, depending on the type of queuing techniques used. As new requests are not given service until the queue is empty, it guarantees high priorities for handover requests. This prioritisation reduces the probability of forced termination of handover requests at the expense of an increased requests blocking probability [14]. But both these quantities determine the QoS of cellular systems. Though Fantacci [15] claims that the queuing scheme with FIFO policy exhibits performance very close to that of the ideal prioritized handover scheme and provides a quality service, Xhafa et al. [16] does not consider it to be the most efficient handover algorithm as it does not take into account dynamics of the user motion. Thus, in this paper, we propose an enhanced priority queuing handover mechanism with a buffer threshold. According to this mechanism, a buffer consists of two partitions. The first part, before threshold, is shared by both handover and new requests and the second part,
after threshold, is used to buffer only the handover requests. High priority is always given to handover requests through the queuing discipline namely Head of Line (HoL). This scheme ensures a seamless connectivity of long duration e-commerce requests. The system is modelled as a GE/GE/C/N/HoL queuing system with generalised exponential (GE) arrival and service times distributions, C channels, a finite capacity N and HoL priority scheduling rule. GE distribution is used to mod the burstiness of the traffic. To distinguish the work presented in this paper from previous studies, a comparative study of schemes based on queuing mechanisms is presented in Section 2. Section 3 explains the design criteria for the proposed model. Addressing the importance of modelling a realistic system, we briefly discuss the Generalised Exponential (GE) distribution. Section 4 illustrates the proposed model. Various experiments performed on the modelled system developed using QNAP-2 [17] are presented in Section 5 to validate the proposal. Simulation results demonstrate that the proposed scheme promises to provide QoS with low loss probability, Mean Queue Length (MQL) and mean Response Time of handover requests to new requests. The system is further developed to verify that the proposed scheme is more efficient than the existing queuing mechanisms. Results with high and increased throughput for handover and new requests, respectively, justified its efficiency. Section 6 concludes the paper and highlights the future prospects. 2. Related work One of the earliest methods to prioritise handover requests is Guard Channel Method developed by Guerin [18]. Guerin’s novel approach uses a combination of channel reservation [19] and queuing mechanism [20]. This approach reserves a number of channels for handover requests and allows new requests to buffer in an infinite queue. In contrast to the conventional handover, queuing of new requests minimises the blocking of fresh requests while maintaining low probability of handover blocking by reserving channels. Hong and Rappaport [9] tested a similar approach to Guerin but the authors only permitted handover requests to buffer in infinite queue with FIFO policy. Infinite queue may not represent a realistic system. Research by Chang et al. [21] is another approach to prioritise handover requests in which handover requests and fresh requests are queued separately in finite queues, differing from the techniques used by Guerin [13], Hong and Rappaport [9]. In addition, it enables the queued new requests to renege and queued handover requests to drop if they move outside the handover area before the handover request is granted. The authors believe that finite queuing mimics the real world better than infinite queuing although implementing two separate queues may be a costly approach. Jabbari and Terinay in [22] consider the movement of mobile users with different speeds. Thus, alternative to FIFO, commonly used by previous researchers, Jabbari and Tekinay promote a non-pre-emptive dynamic priority queuing discipline for handover requests called the measurement based priority scheme (MBPS) based
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on power measurements. The rate of degradation of the power levels of the queued requests are measured continuously. When a channel becomes free, the request with the lowest signal level is granted the service. The simulation results found in [22] indicate that the proposed scheme offers a better QoS with lower handover blocking probability, spectrum utilization and less delay. Xhafa and Tonguz [16] underline that the scheme does not consider the dynamics of user mobility as mobile users move with different speeds. In light of the aforementioned studies, a non-pre-emptive priority queuing scheme namely HoL with shared finite queuing is proposed. The proposed queuing scheme is further enhanced by introducing a cut-off priority technique using queue threshold. This technique is believed to improve the efficiency of cellular networks without a detrimental effect on the perceived QoS. 3. The proposed system A system with two types of users generating handover requests (HO) and new requests (NR) are considered to have access to a set of channels. In Football World Cup scenario, a fresh request for buying ticket, booking flight, etc. generated by a user within a cell is regarded as a NR and the a request generated by the roaming users in the neighbouring cell is treated as a HO. This system is regarded as a single cell with static channel assignment [23]. A system modelled as a single cell is sufficient for evaluating the performance of the proposed handover algorithm. HO and NR are treated equally once they occupy the channels, thus, both the requests have same service rate but distinct arrival rates. The additional features of the system are as follows. HO requests have full access to all the channels and queue in the system without any restriction whereas NR are restricted to queue only until the finite queue reaches a predefined value, threshold. When the queue reaches the threshold, then only HO requests are queued and NR are blocked. Such an approach has the advantage of improving the perceived QoS since fewer high priority requests (HO) will lose the connection due to the additional protection introduced by the space priority. For example, during the handover and new connection request, if all the channels are occupied, then both the requests are allowed to queue. If the queue already holds HO and NR, then when a new handover request is made, the handover request is placed before all the queued new requests following the HoL queuing discipline but remains at the end amongst the handover requests, the secondary queuing policy among HO requests being FIFO. This gives HO requests more priority than NR. Permitting new requests to queue does not block the requests if no channels are free but simply delayed and will ultimately receive the service, contributing to the increasing carried traffic. No requests are pre-empted once the channels are allocated. When a channel becomes free upon completion of a request, it is assigned to the request at the head of the queue.
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loss probability. In order to satisfy the blocking probability requirement, HO and NR traffic rate has to be estimated accurately under realistic assumptions. Firstly, we consider that the cellular traffic is bursty in nature, meaning requests tend to arrive in bulks. Poisson distribution (cf. [24,25]) is the most commonly used distribution to model the number of events occurring within a given time interval. Until when Chlebus and Ludwin [26] first questioned if handover traffic was always Poissonian, most of the models assumed to have Poissonian traffic to model the number of events occurring within a given time interval. A Poisson arrival process does not adequately characterise arrival traffic for cellular networks as the theory assumes one arrival at random time. It is only applicable in a non-blocking environment as in telephone networks, but not in growing internet traffic. In cellular networks, there exists request blockage during insufficient resources available. Supporting the research, we consider that the cellular traffic is bursty in nature, meaning requests tend to arrive in bulks. Section 3.2 briefly describes the Generalised Exponential (GE) Distribution [28,29] which is used to model the traffic with burstiness property. 3.2. The GE distribution The GE distribution is a mixed interevent-time distribution of the form [28]: FðtÞ Z PðX% tÞ Z 1Kt eKst ;
t% 0;
where tZ2/(C2C1), sZtn and X is the interevent time random variable (rv). {1/n, C2} are the mean and the Squared Coefficient of Variation (SCV), the ratio of the variance to the square of the mean, of the interevent time distribution X, respectively. Raad et al. [30] states that all distributions can have a SCV higher than 1 except the exponential distribution which has a coefficient of 1. SVC provides an important measure of the variability of the distribution [27] (Fig. 1). The counting process of the GE distribution is a Compound Poisson Process (CPP) with parameter 2n/(C2C1) and a geometrically distributed batch sizes with mean l/t, (C2C 1)/2 and SCV, (C2K1)/(C2C1). GE distribution is useful for modelling random variables with SCV greater than 1. The GE distribution has a memory-less property [28] like Poisson distribution. The GE distribution is considered to be the most appropriate distribution to model the inter-arrival times of handover and new requests as it supports simultaneous arrivals, unlike Poisson distribution. In this context, the
3.1. Traffic models As discussed above, one of the key parameters that evaluates the performance of the system is the HO and NR
Fig. 1. The GE distribution with parameters t and s (0%t% ).
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Fig. 2. The proposed priority queuing handover scheme with HoL policy and threshold in finite buffer.
burstiness of the arrival process is characterised by the SCV of the inter-arrival time [28]. 4. Model description In this paper, the proposed GE/GE/C/N/HoL queuing system (cf. Section 3) has been used to model a cell with HO and NR inter-arrival and service times at each channel having GE distribution (cf. Fig. 2). C represents the total number of channels available in the cell and N is the total capacity of the queue to temporarily hold the incoming HO requests and NR. Free channels are assigned to the requests in the queue according to HoL scheduling discipline. This adds to the unique features of the model from the existing ones. Each cell is considered to be a queue and each channel as a server. Restricted number of requests capacity in the queue mimics the limited number of channels available due to scarce frequency spectrum availability. Let, i represents the classes of requests (handover and new requests), li and mi represent the mean arrival rate and service rate, Cai2 and Csi2 are the SCVs of the inter-arrival and service times for the ith class, respectively. We consider that the values of mi and Csi2 for all types of requests are the same as we assume that all channels have the same service rates. When a GE arrival process with rate li is sampled with probability tZ 2=ðCai2 C 1Þ, the GE arrival rate (sZtli) of the request will be 2li ðCai2 C 1Þ and, the GE inter-arrival time is ðCai2 C 1Þ=2li . Likewise, for the GE service process with rate mi, the probability that the request will receive service is 2mi ðCsi2 C 1Þ and, the GE inter-service time is ðCsi2 C 1Þ=2mi .
Handover Mechanism where several users want to make flight reservation and book accommodation for the Football World Cup using their mobile phones. Request from these users can be of handover type if generated in other cells or fresh ones. Different sets of experiments have been carried out. In each set, either the test criteria or the environment is changed. Firstly, the outputs of the experiments performed on the proposed enhanced priority queuing system with HoL policy and buffer threshold are captured and analysed. Then, the performance of the proposed system is compared with the conventional handover, non-priority queuing scheme under FIFO policy, and priority queuing scheme with finite complete buffer sharing (CBS) scheme under HoL policy (cf. Table 1). The performance evaluation of the system under consideration is conducted at two levels. The first level models the number of channels in the cell as the varying input parameter and the second level models the handover mean arrival rate as a varying input parameter (cf. Table 2). Both determine the performance of the system on the basis of Mean Queue Length Table 1 Simulation experiments: major scenarios Scenario
Description
Comments
I
Conventional handover scheme
II
Non-priority queuing handover scheme Priority queuing handover scheme—without threshold
Only handover requests are queued. New requests are blocked if no channels are available during channel request Both requests are queued in finite buffer with FIFO policy Both requests are queued in shared finite buffer with HoL policy but no threshold Both requests are queued in shared finite buffer with threshold and HoL policy
III
5. Numerical results IV
The simulation model developed QNAP-2 [17] is used to experiment with various scenarios applicable to Queuing
Priority queuing handover scheme—with threshold
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Table 2 Simulation experiments: system input parameters System parameters
SET A—variation in number of channels
SET B—variation in handover traffic load
Number of channels Queue capacity Threshold value (only applicable for scenario IV) Handover arrival rate SCV for handover arrival requests Handover service rate SCV for handover service rate New requests arrival rate SCV for new arrival requests New requests service rate SCV for new requests service rate
1–8 20 15
3 20 15
3 3
1.0–4.5 3.0
8.0 3.0 7.0 3.0 8.0 3.0
8.0 3.0 7.0 3.0 8.0 3.0
(MQL), Response Time, and loss probability for HO and NR. The proposed model has been tested for various scenarios listed in Table 1. The following input parameters have been used in all simulation experiments. Each experiment is subdivided into two sets: SET A: number of channels is varied from 1 to 8 SET B: handover traffic load is varied from 1.0 to 4.5. In residential areas, most of the traffic is generally assumed to be of type new requests. Hence, the handover arrival rate in each set of experiments is less than the new requests arrival. All the channels provide the same service to either of the requests. Therefore, the service rate for both types of requests is kept the same. SCV for both types of requests are put higher than 1 to model burstiness of the traffic. To minimize system congestion, arrival rate of both types of requests are left lower than the service rate. The simulation results for the modelled system is analysed in two steps: Step 1. Performance evaluation of the proposed Model Step 2. Comparative performance evaluation of the proposed scheme with existing queuing handover techniques (cf. Table 1).
Fig. 3. Loss probability of HO and NR in priority queuing handover with buffer threshold.
Table 3 Simulation results: experiment 1, SET A summary Scenario
IV
SET A Loss probability for handover requests
Loss probability for new requests
5.188!10K2–3.347!10K6
3.464!10K1–1.687!10K4
represents the system performance during scarce spectrum resource (cf. Table 3). Another experiment on priority queuing scheme with HoL policy and buffer threshold (as shown in Fig. 4) demonstrate the impact of HO traffic load on the loss probabilities for HO and NR. It is understood from the graph that the loss probability for HO and NR requests increases with the increase in the HO traffic load. It can be seen that for higher traffic load the difference between the loss probabilities of NR and HO requests increases. The loss probability for HO requests is much less than that of new requests (cf. Table 4). Experiment 2—MQL. The results obtained from the experiment for MQL with the increasing number of channels are presented in Fig. 5. The MQL for HO requests are lower than that of NR. The difference in the MQL between HO and NR decreases with the increase in the number of available channels in the system. The graph proves that for minimum channel resources, MQL for handover is less than that for NR (cf. Table 5).
5.1. Performance study Experiment 1—loss probability. Fig. 3 shows the results from the experiment when HO and NR perform the priority queuing scheme with HoL policy and buffer threshold in increasing number of channels. The graph highlights the loss probability of HO and NR. Initially, the loss probability for HO requests is much less than that of the new requests but the loss probability of high priority (HO) and low priority (NR) requests decreases with the increase in the number of available channels. The high loss probabilities of NR and low loss probability of HO when there is minimum channel resource
Fig. 4. Loss probability of HO and NR in priority queuing handover with buffer threshold.
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Table 4 Simulation results: experiment 2, SET B summary
Table 6 Simulation results: experiment 2, SET B summary
Scenario
Scenario
IV
SET B Loss probability for handover requests
Loss probability for new requests
6.99!10K5–1.003!10K3
2.927!10K3–1.406!10K2
Fig. 5. MQL of HO and NR in priority queuing handover with buffer threshold.
Fig. 6 demonstrates the impact of increasing HO traffic load on MQL for NR and HO requests. The graph illustrates that the increase in HO traffic load in the system is directly proportional to the increase in MQL of the requests. Through HoL policy and buffer threshold technique, the model promises lower MQL for HO requests than of NR though the queues as the HO traffic load increases (cf. Table 6). Experiment 3—response time (delay). Fig. 7 represents the system response time for both types of requests against various number of channels. The delay for NR is comparatively very high to HO when channel resource is less. The given graph shows the inverse relationship of increasing number of channels in the system to Response Time. When the channel number in Table 5 Simulation results: experiment 2, SET A summary
MQL for handover requests IV
IV
MQL for new requests
1.765–3.738!10K1
8.466–8.773!10K1
Fig. 6. MQL of HO and NR in priority queuing handover with buffer threshold.
–8.059!10
K1
MQL for new requests 1.185–1.484
minimum, response time for HO requests is less than that for the NR. The graph shows that the Response for HO is very less as compared to NR (cf. Table 7). Another experiment has been carried out to see the behaviour of the system’s response for increasing HO requests. The graph (cf. Fig. 8) shows a gradual increment of response time for HO and NR when the HO traffic load increases. The response time for HO requests is lower than that for NR. The increasing traffic load does not make significant impact on Response Time of HO and NR unlike in experiment 2, SET B (cf. Fig. 6, Table 8).
Table 7 Simulation results: experiment 3, SET A summary
SET A MQL for handover requests
1.544!10
K1
Fig. 7. Mean response time of HO and NR in priority queuing handover with buffer threshold.
Scenario Scenario
SET B
IV
SET A Response time for handover requests
Response time for new requests
6.193!10K1–1.251!10K1
1.851–1.254
Fig. 8. Mean response time of HO and NR in priority queuing handover with buffer threshold.
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399
Table 8 Simulation results: experiment 3, SET B summary Scenario
IV
SET B Response time for handover requests
Response time for new requests
1.54!10K1–1.802!10K1
1.701!10K1–2.162!10K1
5.1.1. Summary for STEP 1 of simulation Experiments 1–3, validates that the proposed priority queuing handover mechanism in finite queue provides QoS by lower loss probability, MQL and mean Response Time of HO to NR. This means the scheme takes into account that HO requests are of higher priority and the blocking them can result in poor service. As previously discussed, the QoS for the modelled system is defined as low handover blocking probability and minimized new request blocking probability. In both the instances, in experiment 1, the loss probability of HO is less than that of NR. Graphs produced by experiment 2 justifies that the MQL for HO request is lower than that of NR. Likewise, response time for NR requests is higher than the HO requests in the proposed system. This simulation model proves that the proposed system minimizes the trade-off of handover blocking probability with high request blocking probability. To validate further that the performance of the proposed queuing mechanism is better than the existing queuing handover schemes, comparative results are generated. These results are analysed in Section 5.2. 5.2. Performance comparison Experiment 4—throughput. Throughput shows the efficiency of the system; the higher the value the better the system’s efficiency. Figs. 9 and 10 represent the results obtained from the experiments when HO and NR performed under different scenarios (cf. Table 1). It is clear from the
Fig. 10. Throughput of new requests under different scenarios in increasing number of channels.
graphs that for both types of requests, the throughput increases by increasing the number of channels. Minimum availability of channels shows that the throughput of HO and NR differs distinctly in different scenarios. Though the conventional queuing mechanism shows highest throughput of HO in (Fig. 9), its lowest throughput for NR (cf. Fig. 10) penalises its efficiency as it decreases request-to-traffic. Non-priority with FIFO policy and priority with CBS shows better performance than conventional queuing but the proposed priority queuing with buffer threshold outperforms by increasing the throughput of HO greatly with slight decrease in the throughput of NR (cf. Table 9). Experiment 5—loss probability. A similar experiment to one discussed above, is carried out to study the implication on loss probabilities of HO and NR with the changes in input parameters for the HO traffic rate. The results are summarised in Figs. 11 and 12. The graphs illustrate that the loss probabilities for HO and NR increases by increasing the traffic load for HO requests. The loss probability of NR for Conventional Queuing is lowest at all times. This is penalised with the high loss probability of NR. Priority with buffer threshold balances the new request blocking probability and loss probability for HO requests (cf. Table 10). Non-priority-FIFO and priority-CBS have low loss probability for new requests but the loss probabilities are high for HO. When HO are requests of high priority, high HO blocking Table 9 Simulation results: experiment 4, summary
Fig. 9. Throughput of HO requests under different scenarios in increasing number of channels.
Scenario
Throughput for handover requests
Throughput for new requests
I II III IV
2.98671!10K1–2.99408!10K1 2.28073!10K1–2.98364!10K1 2.29499!10K1–2.98364!10K1 2.84989!10K1–2.98803!10K1
2.36758!10K1–6.97863!10K1 5.30436!10K1–6.98922!10K1 5.29663!10K1–6.98922!10K1 4.57486!10K1–6.99390!10K1
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Fig. 11. Loss probability of HO requests under different scenarios in increasing traffic.
A handover could fail due to unavailability of sufficient bandwidth (free channels) in the destination cell. Such failure of ongoing e-commerce requests is highly undesirable and can cause serious problems to the e-commerce users and the service providers. This paper investigated the existing queuing handover mechanisms and proposes an enhanced priority queuing based handover scheme in order to ensure a seamless connectivity of e-commerce requests for their successful completion. It focuses on the performance anaylsis of the proposed scheme. Experimental study demonstrates that the enhanced priority queuing scheme with buffer threshold outperforms the existing queuing schemes and provides QoS with low connection failure and mean response time of handover e-commerce requests. References
Fig. 12. Loss probability of NR under different scenarios in increasing traffic. Table 10 Simulation results: experiment 5, summary Scenario
I II III IV
SET A Loss probability for handover requests
Loss probability for new requests
1.901!10K3–0 2.419!10K1–0 2.383!10K1–0 5.188!102–3.347!10K6
6.608!10K1–3.15!10K3 2.419!10K1–2.862!10K6 2.398!10K1–2.862!10K6 3.464!10K1–1.687!10K4
probabilities means bad system performance. Contrast to this, priority queuing with buffer threshold provides low priority blocking and acceptable slight increase in request blocking probability. 6. Conclusion and future prospects Mobile computing is encouraging users to get maximum benefits from e-commerce systems from their mobile devices whilst roaming between different wireless networks. Due to movement of users during e-commerce requests, one of the most important QoS factors is related to handover of requests from one cell to another.
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