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Service Capacity Enhanced Task Offloading and Resource Allocation in Multi-Server Edge Computing Environment

机译:多服务器边缘计算环境中的服务容量增强的任务分载和资源分配

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An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce service latency and power consumption experienced by the clients. A critical issue that has yet to be properly addressed is how to allocate edge computing resources to achieve two optimization objectives: 1) minimize the service cost measured by the service latency and the power consumption experienced by service clients; and 2) maximize the service capacity measured by the number of service clients that can offload their computation tasks in the long term. This paper formulates this long-term problem as a stochastic optimization problem and solves it with an online algorithm based on Lyapunov optimization. This NPhard problem is decomposed into three sub-problems, which are then solved with a suite of techniques. The experimental results show that our approach significantly outperforms two baseline approaches.
机译:边缘计算环境具有多个边缘服务器和多个服务客户端。在这种环境下,移动服务提供商可以将客户端计算任务从服务客户端的设备上转移到边缘服务器上,以减少客户端的服务延迟和功耗。一个尚待适当解决的关键问题是如何分配边缘计算资源以实现两个优化目标:1)最小化由服务等待时间和服务客户​​端所经历的功耗所衡量的服务成本; 2)最大化服务容量,该服务容量是根据可以长期卸载其计算任务的服务客户端的数量来衡量的。本文将该长期问题表述为随机优化问题,并使用基于Lyapunov优化的在线算法对其进行求解。将此NPhard问题分解为三个子问题,然后使用一套技术对其进行求解。实验结果表明,我们的方法明显优于两种基线方法。

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