Cluster Computing ( IF 4.4 ) Pub Date : 2021-01-07 , DOI: 10.1007/s10586-020-03226-8 Yashwant Singh Patel , Manoj Reddy , Rajiv Misra
Mobile cloud computing augments smart-phones with computation capabilities by offloading computations to the cloud. Recent works only consider the energy savings of mobile devices while neglecting the cost incurred to the tasks which are offloaded. We might offload several tasks to minimize the total energy consumption of mobile devices; however, this could incur a huge monetary cost. Furthermore, these issues become more complex in considering the multi-tenant cloud, which is not addressed in literature adequately. Thus, to balance the trade-off between monetary cost and energy consumption of the mobile devices, we need to decide whether to offload the task to the cloud or run it locally. In this article, first, we have formulated a ‘MinEMC’ optimization problem to minimize both the energy as well as the monetary cost of the mobile devices. The ‘MinEMC’ problem is proven to be NP-hard. We formulate a special case with an equal amount of resource requirement by each task for which a polynomial-time solution is presented. Further various policies are proposed, the cloud can employ to solve the general case. Then we proposed an efficient heuristic named ‘Off-Mat’ based on distributed stable matching, the solution for which determines whether the tasks are to be offloaded or not under multi-constraints. We also analyze the complexity of this proposed heuristic algorithm. Finally, performance evaluation through simulation results demonstrates that the Off-Mat algorithm attains high-performance in computational tasks offloading and scale well as the number of tenants increases.
中文翻译:
移动多租户云中的计算任务卸载所需的能源和成本折衷
移动云计算通过将计算卸载到云上来增强智能电话的计算能力。最近的工作只考虑了移动设备的节能,而忽略了卸载任务所产生的成本。我们可能会分担几项任务,以最大程度地减少移动设备的总能耗。但是,这可能会导致巨大的货币成本。此外,在考虑多租户云时,这些问题变得更加复杂,这在文献中没有得到充分解决。因此,为了在货币成本和移动设备能耗之间取得平衡,我们需要决定是将任务转移到云还是在本地运行。在本文中,首先,我们提出了“ MinEMC”优化问题,以最大程度地减少移动设备的能源和货币成本。“ MinEMC”问题被证明是NP难题。我们通过提出多项式时间解的每个任务来制定一个具有相等资源需求量的特例。提出了进一步的各种策略,云可以用来解决一般情况。然后,我们提出了一种基于分布式稳定匹配的有效启发式算法,名为“ Off-Mat”,该解决方案可确定在多约束条件下是否要卸载任务。我们还分析了该提议的启发式算法的复杂性。最后,通过仿真结果进行性能评估表明,随着租户数量的增加,Off-Mat算法在计算任务的卸载和扩展方面取得了高性能。我们通过提出多项式时间解的每个任务来制定一个具有相等资源需求量的特例。提出了进一步的各种策略,云可以用来解决一般情况。然后,我们提出了一种基于分布式稳定匹配的有效启发式算法,名为“ Off-Mat”,该解决方案可确定在多约束条件下是否要卸载任务。我们还分析了该提议的启发式算法的复杂性。最后,通过仿真结果进行的性能评估表明,随着租户数量的增加,Off-Mat算法在计算任务的分载和扩展方面取得了高性能。我们通过提出多项式时间解的每个任务来制定一个具有相等资源需求量的特例。提出了进一步的各种策略,云可以用来解决一般情况。然后,我们提出了一种基于分布式稳定匹配的有效启发式算法,名为“ Off-Mat”,该解决方案可确定在多约束条件下是否要卸载任务。我们还分析了该提议的启发式算法的复杂性。最后,通过仿真结果进行性能评估表明,随着租户数量的增加,Off-Mat算法在计算任务的卸载和扩展方面取得了高性能。然后,我们提出了一种基于分布式稳定匹配的有效启发式算法,名为“ Off-Mat”,该解决方案可确定在多约束条件下是否要卸载任务。我们还分析了该提议的启发式算法的复杂性。最后,通过仿真结果进行性能评估表明,随着租户数量的增加,Off-Mat算法在计算任务的卸载和扩展方面取得了高性能。然后,我们提出了一种基于分布式稳定匹配的有效启发式算法,名为“ Off-Mat”,该解决方案可确定在多约束条件下是否要卸载任务。我们还分析了该提议的启发式算法的复杂性。最后,通过仿真结果进行的性能评估表明,随着租户数量的增加,Off-Mat算法在计算任务的分载和扩展方面取得了高性能。