The numerical results show that the MR strategy cannot always perform well. First, there is a worse case situation when φ is large and β is small, the average query response time of the MR strategy is larger than that of the SR strategy. Further, the number of replicas cannot be too large to achieve a reasonable query response time. Therefore, we propose an adaptive multiple replication (AMR) protocol based on the MR strategy to solve these problems. The protocol is composed of two components.
The first part of the AMR protocol is to prevent the MR strategy from getting into the worse case situation. Therefore, we designed the first part as follows.
When φ is larger than a threshold value (denoted by Tφ) and β is smaller than another threshold value (denoted by Tβ), we use the SR strategy to replicate the profile of the
mobile user. In other situations, we use the MR strategy to increase the number of replicas.
First, we need to estimate the threshold Tφ. Tφ can be derived using the assumption that TqSRis always larger than TqMRno matter how large β is. For example, Tφ is about 0.414 in Figure 9. Then we derive the threshold Tβ that can be calculated from the Equations (34) and (83) when TqSR= TqMR. TqSRand TqMRare functions of parameters φ, α, γ , etc. If we measure these parameters, we can obtain Tβ easily. However, the measurement and computation of these parameters at once are not simple tasks; thus, we suggest that they should be performed periodically. In this manner, the average query response time is equal to the query time of the SR strategy when β is less than Tβ, and equal to the query time of the MR strategy when β is larger than Tβ.
The second part of the AMR protocol involves avoiding the number of replicas exceeding an optimized value. When the query rate of a foreign region for a callee increases, the database in that region may attempt to replicate the callee’s profile to reduce the query response time.
However, an increase in replicas makes the propagating load increase; thus, the query time also increases. Additionally, an increase in the query rate will also increase both the traffic load and the query time; thus, it will reduce the replication benefit. These factors will affect the allowable maximum number of replicas. Therefore, we use a 2-phase methodology com-bining a distributed request scheme and a centralized decision scheme to decide the number of replicas. We describe the second part of the protocol including the two phases as follows.
− Phase one: The distributed request scheme. If the call rate of a foreign region for a callee is high, the replicated database of the region will send a request message to RDBu to require attaching a replica of the user profile. If the request is accepted, the RDBnr of that region becomes an RDBr.
− Phase two: The centralized decision scheme. The replicated database (RDBu) of the for-eign region where a callee visits will estimate the allowable maximum number of replicas (denoted by Tk) for the callee’s profile according to the total traffic load. When RDBu
receives a request message, it compares the number of replicas for the profile and the Tk. If the number of replicas is less than Tk, the RDBu will allow the profile propagating to the requesting region; otherwise, the RDBuwill reject the request. Additionally, when the total traffic load increases, the RDBu can decide to discard a replica from other foreign regions if the number of replicas is larger than a newly estimated Tk.
6. Conclusions
We modeled the single-replica (SR) and multiple-replica (MR) strategies of mobility data-bases for PCS networks. The two strategies are based on a partial replication scheme, and a primary copy method is our assumption to maintain the consistency of all replicas. In the SR strategy, only one replica is created along with the movement of the callee, while in the MR strategy, the replica allocation is dynamic according to the query rate of the callers. We compared the two strategies in a global mobility environment in which the query rates of other service areas for a callee may be high.
The numerical results show some important phenomena in our performance study. First, the MR strategy prefers interactive processing for the call queries, whereas the SR strategy prefers batch processing. Therefore, we can easily design an interactive mobile system using the MR strategy. Moreover, we find that the MR strategy will get the benefit of partial replication more
than the SR strategy does. Second, the MR strategy performs well than the SR strategy in most situations except that the probability of a mobile user visiting a foreign region is high and the query rates from other foreign regions are low. The worse case situation for the MR strategy may possibly happen for some callees. The third phenomenon is that the number of replicas in the MR strategy should be compact in order to achieve a reasonable query response time. This is because the propagation overhead will overtake the replication benefit which reduces the traffic load for queries and updates. Consequently, a mechanism is necessary for controlling the number of replicas for a callee. The decision for the number of replicas depends upon the traffic load, the propagating load and the query rate. We proposed an adaptive multiple replication (AMR) protocol to solve the above problems. The AMR protocol measures the distribution of the callee and the query rates of other service areas for the callee in choosing the SR strategy or the MR strategy. This protocol combines a distributed request scheme and a centralized decision scheme to obtain the optimized number of replicas.
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Gwo-Chuan Lee is currently a Ph.D. candidate in the Department of Computer Science and Information Engineering at National Chiao-Tung University, Hsin-Chu, Taiwan. He received his B.Sc. degree in computer science and information engineering from National Chiao-Tung University, Hsin-Chu, Taiwan, in 1988; M.Sc. degree in computer science from National Tai-wan University, Taipei, TaiTai-wan, in 1990. His research interests include mobile computing, wireless Internet, and computer networks.
Tsan-Pin Wang is currently an associate professor in the Department of Computer Sci-ence and Information Management at ProvidSci-ence University, Shalu, Taiwan. He received the B.Sc. degree in applied mathematics; M.Sc. and Ph.D. in computer science and information engineering, from National Chiao Tung University, Taiwan, ROC, in 1990, 1992, and 1997, re-spectively. From 1992 to 1993, he was a system engineer in the R&D Division of Taiwan NEC Ltd. From 1997 to 2001, he was an assistant professor at Providence University. His research interests include mobile computing, mobile communications, and computer networks.
Chien-Chao Tseng is currently a professor in the Department of Computer Science and Information Engineering at National Chiao-Tung University, Hsin-Chu, Taiwan. He received his B.Sc. degree in industrial engineering from National Tsing-Hua University, Hsin-Chu, Taiwan, in 1981; M.Sc. and Ph.D. degrees in computer science from the Southern Methodist University, Dallas, Texas, U.S.A., in 1986 and 1989, respectively. His research interests include mobile computing, and wireless Internet.