Joint Optimization Across Timescales: Resource Placement and Task Dispatching in Edge Clouds
Joint Optimization Across Timescales: Resource Placement and Task Dispatching in Edge Clouds
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DOI：
10.1109/tcc.2021.3113605
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发表时间：
2023-01
影响因子：
6.5
通讯作者：
Ieee A B M Xinliang Wei Student Member;Mohaimenur Rahman;Ieee Dazhao Cheng Member;Ieee Yu Wang Fellow;Xinliang Wei
中科院分区：
文献类型：
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作者：
Ieee A B M Xinliang Wei Student Member;Mohaimenur Rahman;Ieee Dazhao Cheng Member;Ieee Yu Wang Fellow;Xinliang Wei
物联网(IoT)数据和创新移动服务的激增促进了对数据和计算服务等资源的低延迟访问的需求日益增长。移动边缘计算通过在移动用户附近的边缘云上放置资源和调度任务，已经成为满足低延迟访问需求的有效计算范式。这种解决方案的关键挑战是如何在边缘云中高效地放置资源和调度任务，以满足移动用户的服务质量或最大化平台的效用。本文研究了边缘服务器处于动态状态下，移动边缘云中多时间尺度的资源分配和任务调度的联合优化问题。首先提出了一种两阶段迭代算法来解决不同时间尺度下的联合优化问题，该算法能够处理边资源和/或任务的动态变化。然后，我们提出了一种基于强化学习(RL)的算法，该算法利用深度确定性策略梯度(DDPG)技术的学习能力来处理网络的变化和动态。跟踪驱动的仿真结果表明，这两种方法都能有效地在两个时间尺度上分配资源和调度任务，以最大化所有调度任务的总效用。
The proliferation of Internet of Things (IoT) data and innovative mobile services has promoted an increasing need for low-latency access to resources such as data and computing services. Mobile edge computing has become an effective computing paradigm to meet the requirement for low-latency access by placing resources and dispatching tasks at the edge clouds near mobile users. The key challenge of such solution is how to efficiently place resources and dispatch tasks in the edge clouds to meet the QoS of mobile users or maximize the platform’s utility. In this article, we study the joint optimization problem of resource placement and task dispatching in mobile edge clouds across multiple timescales under the dynamic status of edge servers. We first propose a two-stage iterative algorithm to solve the joint optimization problem in different timescales, which can handle the varieties among the dynamic of edge resources and/or tasks. We then propose a reinforcement learning (RL) based algorithm which leverages the learning capability of Deep Deterministic Policy Gradient (DDPG) technique to tackle the network variation and dynamic as well. The results from our trace-driven simulations demonstrate that both proposed approaches can effectively place resources and dispatching tasks across two timescales to maximize the total utility of all scheduled tasks.