Energy and cost trade-off for computational tasks offloading in mobile multi-tenant clouds
Energy and cost trade-off for computational tasks offloading in mobile multi-tenant clouds
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DOI：
10.1007/s10586-020-03226-8
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发表时间：
2021-01
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通讯作者：
Yashwant Singh Patel;M. Reddy;R. Misra
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作者：
Yashwant Singh Patel;M. Reddy;R. Misra
移动的云计算通过将计算卸载到云来增强智能电话的计算能力。最近的工作只考虑了移动的设备的节能，而忽略了被卸载的任务所产生的成本。我们可能会卸载几个任务，以最大限度地减少移动的设备的总能耗;然而，这可能会产生巨大的金钱成本。此外，这些问题在考虑多租户云时变得更加复杂，这在文献中没有得到充分解决。因此，为了平衡移动的设备的货币成本和能耗之间的权衡，我们需要决定是将任务卸载到云还是在本地运行。在本文中，首先，我们制定了一个“MinEMC”优化问题，以最大限度地减少移动的设备的能源和货币成本。最小电磁兼容性问题被证明是NP难的。我们制定了一个特殊的情况下，每个任务的多项式时间的解决方案提出了等量的资源需求。进一步提出了各种策略，云可以用来解决一般情况。在此基础上，提出了一种基于分布式稳定匹配的高效启发式算法Off-Mat，该算法的解决定了在多约束条件下任务是否被卸载。我们还分析了这个启发式算法的复杂性。最后，通过仿真实验进行性能评估，结果表明，Off-Mat算法在计算任务卸载和扩展方面具有较高的性能，并且随着租户数量的增加，其性能也得到了提高。
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.