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Meta Computing is a new computing paradigm, which aims to solve the problemof computing islands in current edge computing paradigms and integrate all theresources on a network by incorporating cloud, edge, and particularlyterminal-end devices. It throws light on solving the problem of lackingcomputing power. However, at this stage, due to technical limitations, it isimpossible to integrate the resources of the whole network. Thus, we create anew meta computing architecture composed of multiple meta computers, each ofwhich integrates the resources in a small-scale network. To make meta computingwidely applied in society, the service quality and user experience of metacomputing cannot be ignored. Consider a meta computing system providingservices for users by scheduling meta computers, how to choose from multiplemeta computers to achieve maximum Quality-of-Experience (QoE) with limitedbudgets especially when the true expected QoE of each meta computer is notknown as a priori? The existing studies, however, usually ignore the costs andbudgets and barely consider the ubiquitous law of diminishing marginal utility.In this paper, we formulate a resource scheduling problem from the perspectiveof the multi-armed bandit (MAB). To determine a scheduling strategy that canmaximize the total QoE utility under a limited budget, we propose an upperconfidence bound (UCB) based algorithm and model the utility of service byusing a concave function of total QoE to characterize the marginal utility inthe real world. We theoretically upper bound the regret of our proposedalgorithm with sublinear growth to the budget. Finally, extensive experimentsare conducted, and the results indicate the correctness and effectiveness ofour algorithm.

An Online Resource Scheduling for Maximizing Quality-of-Experience in Meta Computing