  Meta Computing is a new computing paradigm, which aims to solve the problem
of computing islands in current edge computing paradigms and integrate all the
resources on a network by incorporating cloud, edge, and particularly
terminal-end devices. It throws light on solving the problem of lacking
computing power. However, at this stage, due to technical limitations, it is
impossible to integrate the resources of the whole network. Thus, we create a
new meta computing architecture composed of multiple meta computers, each of
which integrates the resources in a small-scale network. To make meta computing
widely applied in society, the service quality and user experience of meta
computing cannot be ignored. Consider a meta computing system providing
services for users by scheduling meta computers, how to choose from multiple
meta computers to achieve maximum Quality-of-Experience (QoE) with limited
budgets especially when the true expected QoE of each meta computer is not
known as a priori? The existing studies, however, usually ignore the costs and
budgets and barely consider the ubiquitous law of diminishing marginal utility.
In this paper, we formulate a resource scheduling problem from the perspective
of the multi-armed bandit (MAB). To determine a scheduling strategy that can
maximize the total QoE utility under a limited budget, we propose an upper
confidence bound (UCB) based algorithm and model the utility of service by
using a concave function of total QoE to characterize the marginal utility in
the real world. We theoretically upper bound the regret of our proposed
algorithm with sublinear growth to the budget. Finally, extensive experiments
are conducted, and the results indicate the correctness and effectiveness of
our algorithm.
