This paper studies a problem of jointly optimizing two important operations in mobile edge computing without knowing future requests, namely service caching, which determines which services to be hosted at the edge, and service routing, which determines which requests to be processed locally at the edge. We aim to address several practical challenges, including limited storage and computation capacities of edge servers and unknown future request arrival patterns. To this end, we formulate the problem as an online optimization problem, in which the objective function includes costs of forwarding requests, processing requests, and reconfiguring edge servers. By leveraging a natural timescale separation between service routing and service caching, namely, the former happens faster than the latter, we propose an online two-stage algorithm and its randomized variant. Both algorithms have low complexity, and our fractional solution achieves sublinear regret. Simulation results show that our algorithms significantly outperform other state-of-the-art online policies.
翻译:本文研究的是联合优化移动边缘计算中两个重要操作而不知道未来请求的难题,即:服务缓冲,确定在边缘的托管服务;服务路由,确定在边缘的本地处理哪些请求;我们的目标是应对若干实际挑战,包括边缘服务器的有限存储和计算能力以及未知的未来请求抵达模式。为此,我们将这一问题描述为在线优化问题,其中的目标功能包括传送请求、处理请求和重新配置边缘服务器的成本。通过利用服务路线和服务缓冲之间的自然时间尺度分离,即前者比后者快,我们提议了在线两阶段算法及其随机变量。这两种算法都比较复杂,我们的分数解决方案都取得了亚线性遗憾。模拟结果表明,我们的算法大大优于其他最先进的在线政策。