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%.
Keywords: Bandit learning; edge caching; edge cloud computing; task caching.