Modeling and performance analysis of cloudlet in Mobile Cloud Computing

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• 作者：Hassan Raei ; ^{h.raei@ut.ac.ir}Author Vitae ; Nasser Yazdani ^{yazdani@ut.ac.ir}Author Vitae ; Reza Shojaee ^{r.shojaee@ut.ac.ir}Author Vitae
• 关键词：Mobile Cloud Computing ; Cloudlet ; Performance evaluation ; Markov reward model ; Fixed-point iteration method
• 刊名：Performance Evaluation
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：107
• 期：Complete
• 页码：34-53
• 全文大小：2020 K
• 卷排序：107

文摘

Mobile Cloud Computing (MCC) is an emerging technology to overcome the resource-constrained nature of mobile devices by offloading the resource-intensive operations to cloud-based data centers. The characteristics of MCC, including the mobility, the instability of 3G/WiFi connections, and the complexity of virtualization, make the prediction of performance difficult. To deal with these issues, an analytical performance model consisting of interacting stochastic sub-models is proposed. Also, the cyclic inter-dependency among these sub-models is resolved by the fixed-point iteration method. Moreover, closed-form solutions of the sub-models are presented whenever achievable. Specifically, this study models a type of MCC known as the Cloudlet in which the mobile devices, via a WiFi connection, receive services from a cloudlet (e.g., coffee shop) as an intermediary node. In this architecture, a dedicated Virtual Machine (VM) is provisioned on a Physical Machine (PM) while the PM can be located as a part of the cloudlet or a public cloud. The performance of such a class of MCC is affected by varied set of parameters, such as connection failure, and workload. The model illustrates the impact of the parameters on two important performance measures: request rejection probability and mean response delay. Using the SHARPE software package, the model is solved and numerical results presented. Furthermore, the analytical results are verified through discrete-event simulation.