基于学习自动机的移动云任务卸载决策算法

P. V. Krishna, S. Misra, D. Nagaraju, V. Saritha, M. Obaidat
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引用次数: 20

摘要

近年来,移动云计算(MCC)被视为物联网的重要推动因素之一。MCC是从移动计算和云计算的混合发展而来的。大多数MCC研究人员的工作重点是通过利用云技术来降低移动设备中应用程序的成本。移动环境的局限性,如计算能力、电池电量和有限的内存,导致了云技术的融合。通过使用云技术实现任务卸载,移动环境的性能得到了改善。提出了一种基于学习自动机的决策算法(LADMA)的任务卸载模型。该算法在将任务分配到云中合适的虚拟机时,考虑了任务的完成时间和能耗。该模型在Amazon EC2和Android x86平台上进行了测试。
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Learning automata based decision making algorithm for task offloading in mobile cloud
In recent years, mobile cloud computing (MCC) is treated as one of the important enablers of Internet of Things. MCC has evolved from a mix of mobile computing and cloud computing. Most of the researcher works on MCC focus on the reduction of cost of applications in mobile devices by leveraging cloud technology. The limitations of mobile environment such as computation capacity, battery power and limited memory lead to the integration of cloud technology. The performance of the mobile environment improves by the implementation of task offloading using cloud technology. In this paper, a model for task offloading using learning automata based decision making algorithm (LADMA) is proposed. The algorithm considers the completion time and energy consumption of the tasks during the allocation of the tasks to the suitable VMs in the cloud. The proposed model is tested on Amazon EC2 and Android X 86 Platforms.
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