Miao Yiming, Hao Yixue, Chen Min, Gharavi Hamid, Hwang Kai
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Department of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China, and also with the Wuhan National Laboratory for optoelectronics, Wuhan 430074, China.
IEEE Trans Netw Sci Eng. 2021;8(1). doi: 10.1109/tnse.2020.3047417.
Task caching, based on edge cloud, aims to meet the latency requirements of computation-intensive and data-intensive tasks (such as augmented reality). However, current task caching strategies are generally based on the unrealistic assumption of knowing the pattern of user task requests and ignoring the fact that a task request pattern is more user specific (e.g., the mobility and personalized task demand). Moreover, it disregards the impact of task size and computing amount on the caching strategy. To investigate these issues, in this paper, we first formalize the task caching problem as a non-linear integer programming problem to minimize task latency. We then design a novel intelligent task caching algorithm based on a multiarmed bandit algorithm, called M-adaptive upper confidence bound (M-AUCB). The proposed caching strategy cannot only learn the task patterns of mobile device requests online, but can also dynamically adjust the caching strategy to incorporate the size and computing amount of each task. Moreover, we prove that the M-AUCB algorithm achieves a sublinear regret bound. The results show that, compared with other task caching schemes, the M-AUCB algorithm reduces the average task latency by at least 14.8%.
基于边缘云的任务缓存旨在满足计算密集型和数据密集型任务（如增强现实）的延迟要求。然而，当前的任务缓存策略通常基于知道用户任务请求模式这一不切实际的假设，而忽略了任务请求模式更具用户特定性这一事实（例如，移动性和个性化任务需求）。此外，它忽略了任务大小和计算量对缓存策略的影响。为了研究这些问题，在本文中，我们首先将任务缓存问题形式化为一个非线性整数规划问题，以最小化任务延迟。然后，我们基于多臂赌博机算法设计了一种新颖的智能任务缓存算法，称为M自适应上置信界（M-AUCB）。所提出的缓存策略不仅可以在线学习移动设备请求的任务模式，还可以动态调整缓存策略以纳入每个任务的大小和计算量。此外，我们证明了M-AUCB算法实现了次线性遗憾界。结果表明，与其他任务缓存方案相比，M-AUCB算法将平均任务延迟降低了至少14.8%。
