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Intelligent Task Caching in Edge Cloud via Bandit Learning

Published

Author(s)

Hamid Gharavi

Abstract

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 a tasks size and computing amount on the caching strategy. To investigate these issues, in this paper we first formalize the task caching problem in order to minimize task latency. We then design a novel intelligent task caching algorithm based on a multi-armed 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, as well as analyze the bound losses of the M-AUCB algorithm. The results show that, compared with other task caching schemes, the M-AUCB algorithm reduces the average task latency by at least 14.8%.
Citation
IEEE Transactions on Network Science and Engineering
Volume
8
Issue
1

Keywords

Edge Caching, Task Caching, Edge Cloud Computing, Machine learning

Citation

Gharavi, H. (2021), Intelligent Task Caching in Edge Cloud via Bandit Learning, IEEE Transactions on Network Science and Engineering, [online], https://dx.doi.org/10.1109/TNSE.2020.3047417, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930996 (Accessed November 28, 2021)
Created March 19, 2021, Updated April 6, 2021