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Computation Offloading for Service Workflow in Mobile Cloud Computing

机译:移动云计算中服务工作流的计算分流

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The development of cloud computing and virtualization techniques enables mobile devices to overcome the severity of scarce resource constrained by allowing them to offload computation and migrate several computation parts of an application to powerful cloud servers. A mobile device should judiciously determine whether to offload computation as well as what portion of an application should be offloaded to the cloud. This paper considers a mobile computation offloading problem where multiple mobile services in workflows can be invoked to fulfill their complex requirements and makes decision on whether the services of a workflow should be offloaded. Due to the mobility of portable devices, unstable connectivity of mobile networks can impact the offloading decision. To address this issue, we propose a novel offloading system to design robust offloading decisions for mobile services. Our approach considers the dependency relations among component services and aims to optimize execution time and energy consumption of executing mobile services. To this end, we also introduce a mobility model and a trade-off fault-tolerance mechanism for the offloading system. A genetic algorithm (GA) based offloading method is then designed and implemented after carefully modifying parts of a generic GA to match our special needs for the stated problem. Experimental results are promising and show near-optimal solutions for all of our studied cases with almost linear algorithmic complexity with respect to the problem size.
机译:云计算和虚拟化技术的发展通过允许移动设备卸载计算并将应用程序的多个计算部分迁移到功能强大的云服务器,从而克服了稀缺资源的严重性。移动设备应明智地确定是否要卸载计算以及应将应用程序的哪一部分卸载到云。本文考虑了一个移动计算卸载问题,在该问题中可以调用工作流中的多个移动服务以满足其复杂的需求,并确定是否应卸载工作流的服务。由于便携式设备的移动性,移动网络的不稳定连接会影响卸载决策。为了解决这个问题,我们提出了一种新颖的卸载系统,可以为移动服务设计可靠的卸载决策。我们的方法考虑了组件服务之间的依赖关系,旨在优化执行时间和执行移动服务的能耗。为此,我们还介绍了用于卸载系统的移动性模型和权衡的容错机制。然后,在仔细修改了通用GA的各个部分以适应我们针对所述问题的特殊需求之后,便设计并实现了一种基于遗传算法(GA)的卸载方法。实验结果是有希望的,并显示出我们所有研究案例的最佳解决方案,并且相对于问题的大小,算法的复杂度几乎呈线性。

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