Mobile Cloud Computing (MCC) helps increasing performance of intensive mobile applications by offloading heavy tasks to cloud computing infrastructures. The first step in this procedure is partitioning the application into small tasks and identifying those that are better suited for offloading. The method call partitioning strategy splits the code into a set of method calls that are offloaded to remote servers. Quite often, many applications need to make use of multiple servers for parallel processing of intensive computational operations. Predicting the behavior of such parallelizable applications is not an easy task. Deciding the number of remote servers determines the performance of the applications and the costs of the cloud usage. On one hand, users are interested in improving the performance of their applications, so they would like to use as many servers as possible, but on the other hand, they would also like to reduce their costs by using fewer cloud resources. In this paper, we propose a Stochastic Petri Net (SPN) modeling strategy to represent method call executions of mobile cloud systems. This approach enables a designer to plan and optimize MCC environments in which SPNs represent the system behavior and estimate the execution time of parallelizable applications. © 2002-2012 IEEE.

Mobile cloud performance evaluation using stochastic models / Silva, Francisco Airton; Kosta, Sokol; Rodrigues, Matheus; Oliveira, Danilo; Maciel, Teresa; Mei, Alessandro; Maciel, Paulo Martins. - In: IEEE TRANSACTIONS ON MOBILE COMPUTING. - ISSN 1536-1233. - STAMPA. - 17:5(2018), pp. 1134-1147. [10.1109/TMC.2017.2749577]

Mobile cloud performance evaluation using stochastic models

Silva, Francisco Airton;Kosta, Sokol;Mei, Alessandro;
2018

Abstract

Mobile Cloud Computing (MCC) helps increasing performance of intensive mobile applications by offloading heavy tasks to cloud computing infrastructures. The first step in this procedure is partitioning the application into small tasks and identifying those that are better suited for offloading. The method call partitioning strategy splits the code into a set of method calls that are offloaded to remote servers. Quite often, many applications need to make use of multiple servers for parallel processing of intensive computational operations. Predicting the behavior of such parallelizable applications is not an easy task. Deciding the number of remote servers determines the performance of the applications and the costs of the cloud usage. On one hand, users are interested in improving the performance of their applications, so they would like to use as many servers as possible, but on the other hand, they would also like to reduce their costs by using fewer cloud resources. In this paper, we propose a Stochastic Petri Net (SPN) modeling strategy to represent method call executions of mobile cloud systems. This approach enables a designer to plan and optimize MCC environments in which SPNs represent the system behavior and estimate the execution time of parallelizable applications. © 2002-2012 IEEE.
2018
Mobile Cloud Computing; Offloading; Stochastic Petri Nets; Software; Computer Networks and Communications; Electrical and Electronic Engineering
01 Pubblicazione su rivista::01a Articolo in rivista
Mobile cloud performance evaluation using stochastic models / Silva, Francisco Airton; Kosta, Sokol; Rodrigues, Matheus; Oliveira, Danilo; Maciel, Teresa; Mei, Alessandro; Maciel, Paulo Martins. - In: IEEE TRANSACTIONS ON MOBILE COMPUTING. - ISSN 1536-1233. - STAMPA. - 17:5(2018), pp. 1134-1147. [10.1109/TMC.2017.2749577]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1019837
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