Cloudlet-Assisted Heuristic Offloading for Mobile Interactive Applications

G. Shu, Xiaojie Zheng, Hua Xu, Jing Li
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引用次数: 2

Abstract

Offloading the computation-intensive task of mobileinteractive applications into cloudlets has become a popularway to reduce the response time in mobile cloud computingarea. In response to the rapid change of mobile environment, researchers have proposed online offloading decisions for variousapplications. However, the pruning method they used wouldperform poorly for the non-linear topology of applications. Inthis paper, we propose a heuristic to search out the near-optimaloffloading scheme for the non-linear topology under the real-timeconstraint. In order to reduce the searching space, wefirst leverage Partial Stochastic Path (PSP) analysis to randomlydivide the topological graph into a set of linear paths. Thenfor each linear path, One-climb Policy is applied to obtainthe optimal offloading decision as there is at most one-timeoffloading to the cloudlet if ever. In addition, among the multiplerandom divisions, the one with the most superior performance isselected as the final division. Lastly, we can get the approximateoptimal solution with the minimum completion time in real-time. Simulation results illustrate that our heuristic exhibits anexcellent performance for non-linear topology and outperformsthe state-of-the-art approaches.
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移动交互应用的cloudlet辅助启发式卸载
在移动云计算领域,将移动交互应用的计算密集型任务卸载到云计算中已经成为减少响应时间的一种流行方法。针对移动环境的快速变化,研究人员提出了各种应用程序的在线卸载决策。然而,他们使用的修剪方法对于应用程序的非线性拓扑表现不佳。本文提出了一种启发式算法,用于在实时约束下搜索非线性拓扑的近最优卸载方案。为了减少搜索空间,我们首先利用部分随机路径(PSP)分析将拓扑图随机划分为一组线性路径。然后,对于每个线性路径,应用一次爬升策略来获得最优卸载决策,因为如果有的话,最多只能对云进行一次卸载。此外,在多个随机分区中,选择表现最优的一个作为最终分区。最后,我们可以在实时情况下以最小的完成时间得到近似最优解。仿真结果表明,我们的启发式算法在非线性拓扑中表现出优异的性能,并且优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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