异构移动云计算中多任务应用的动态资源编排

Q. Qi, J. Liao, Jingyu Wang, Qi Li, Yufei Cao
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引用次数: 20

摘要

以无线接入网为传输介质,以移动设备为客户端的移动云计算(mobile cloud computing, MCC)成为云计算的最新发展趋势。当将复杂的多任务应用程序卸载到MCC环境时,每个任务根据自己的计算、存储和带宽需求单独执行。由于用户的移动性,所提供的资源包含可能影响目标选择的不同性能指标。然而,这些异构的MCC资源缺乏整合管理,难以相互协作。因此,如何为多任务选择合适的卸载目的地和编排资源是一个具有挑战性的问题。本文将移动云的资源控制与用户平面解耦,在用户平面中,一个集中的控制器负责资源的编排、卸载和迁移。资源编排是一个包含能耗、成本和可用性指标的多目标优化问题。最后,利用粒子群算法求出近似最优解。仿真结果表明,该方法能在可接受的时间内达到资源调度的帕累托最优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic resource orchestration for multi-task application in heterogeneous mobile cloud computing
The mobile cloud computing (MCC) that takes wireless access network as transmission medium and uses mobile devices as client becomes the newest evolution trends of cloud computing. When offloading the complicated multi-task application to the MCC environment, each task executes individually in terms of its own computation, storage and bandwidth requirement. Due to user's mobility, the provided resources contain different performance metrics that may affect the destination choice. Nevertheless, these heterogeneous MCC resources lack integrated management and can hardly cooperate with each other. Thus, how to choose the appropriate offload destination and orchestrate the resources for multi-task is a challenging problem. This paper decouples resource control of mobile cloud from user plane, where a centralized controller is responsible for resource orchestration, offload and migration. The resource orchestration is formulated as multi-objective optimal problem that contains the metrics of energy consumption, cost and availability. Finally, a particle swarm algorithm is used to obtain the approximate optimal solutions. Simulation results show that the solutions can hit Pareto optimum of resource orchestration in acceptable time.
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