Service Placement and Request Routing in MEC Networks With Storage, Computation, and Communication Constraints
Service Placement and Request Routing in MEC Networks With Storage, Computation, and Communication Constraints
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DOI：
10.1109/tnet.2020.2980175
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发表时间：
2020-04
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影响因子：
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通讯作者：
Konstantinos Poularakis;Jaime Llorca;A. Tulino;I. Taylor;L. Tassiulas
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作者：
Konstantinos Poularakis;Jaime Llorca;A. Tulino;I. Taylor;L. Tassiulas
增强现实、网络游戏和自动驾驶等创新移动服务的激增刺激了对低延迟访问计算资源的日益增长的需求，而现有的集中式云系统无法单独满足这些需求。移动边缘计算(MEC)通过在网络边缘、靠近终端用户执行计算任务，有望成为满足低延迟服务需求的有效解决方案。虽然最近的一些研究已经解决了确定服务任务的执行和将用户请求路由到相应的边缘服务器的问题，但重点主要放在计算资源的有效利用上，忽略了需要预先存储大量数据以实现服务执行的事实，以及许多新兴服务表现出不对称的带宽要求。为了填补这一空白，我们研究了多维约束的密集MEC网络中服务放置和请求路由的联合优化问题。我们证明了这个问题推广了几个著名的布局和布线问题，并提出了一个使用随机舍入技术获得接近最优性能的算法。评估结果表明，该方法可以有效地利用可用的存储、计算和通信资源，最大化低延迟边缘云服务器所服务的请求数量。
The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers.