arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2103.11220cs.ITeess.SPmath.IT
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面向位置感知多用户移动边缘计算的联合资源分配和缓存放置

Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile Edge Computing

Jiechen Chen, Hong Xing, Xiaohui Lin, Arumugam Nallanathan, Suzhi Bi

摘要:

随着对延迟关键型和计算密集型物联网(IoT)服务的需求不断增长,面向IoT的网络架构,移动边缘计算(MEC),已经成为一种有前途的技术,以增强资源受限的IoT设备的计算能力。为了充分利用网络边缘的类云功能,实现了服务缓存以重用计算任务输入/输出数据,从而有效减少了数据重传和重复执行同一任务所带来的延迟。在多用户缓存辅助MEC系统中,用户对不同类型服务的偏好(可能取决于其位置)在通信、计算和服务缓存的联合设计中起着重要作用。在本文中,我们考虑多个代表性的位置,在同一位置的用户共享相同的偏好配置文件为一组给定的服务。具体而言,通过利用位置感知用户的偏好配置文件,我们提出了联合优化的二进制缓存放置,边缘计算资源和带宽分配,以最大限度地减少预期的总能耗,受带宽和计算的限制,以及服务延迟的约束。为了有效地解决混合整数非凸问题,我们提出了一种基于深度学习(DL)的离线缓存放置方案,使用一种新的基于随机量化的离散动作生成方法。所提出的混合学习框架主张从无模型DL方法和基于模型的优化中获益。仿真结果表明,与贪婪缓存和流行缓存相比,所提出的基于DL的方案分别节省了大约33%和6.69%的能耗,同时实现了高达99.01%的最佳性能。

英文摘要:

With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile edge computing (MEC), has emerged as a promising technique to reinforce the computation capability of the resource-constrained IoT devices. To exploit the cloud-like functions at the network edge, service caching has been implemented to reuse the computation task input/output data, thus effectively reducing the delay incurred by data retransmissions and repeated execution of the same task. In a multi-user cache-assisted MEC system, users' preferences for different types of services, possibly dependent on their locations, play an important role in joint design of communication, computation and service caching. In this paper, we consider multiple representative locations, where users at the same location share the same preference profile for a given set of services. Specifically, by exploiting the location-aware users' preference profiles, we propose joint optimization of the binary cache placement, the edge computation resource and the bandwidth allocation to minimize the expected sum-energy consumption, subject to the bandwidth and the computation limitations as well as the service latency constraints. To effectively solve the mixed-integer non-convex problem, we propose a deep learning (DL)-based offline cache placement scheme using a novel stochastic quantization based discrete-action generation method. The proposed hybrid learning framework advocates both benefits from the model-free DL approach and the model-based optimization. The simulations verify that the proposed DL-based scheme saves roughly 33% and 6.69% of energy consumption compared with the greedy caching and the popular caching, respectively, while achieving up to 99.01% of the optimal performance.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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