Online optimal task offloading with one-bit feedback
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Task offloading is an emerging technology in fog-enabled networks. It allows users transmit tasks to neighbor fog nodes so as to utilize the computing resource of the networks. In this paper, we investigate a stochastic task offloading model and propose a multi-armed bandit framework to formulate this model. We consider different helper nodes prefer different kinds of tasks and feedback one-bit information to task node named happiness of nodes. The key challenge of this problem is an exploration-exploitation tradeoff. Thus we implement a UCB-type algorithm to maximize the long-term happiness metric. Further more, we prove that this UCB-type algorithm is asymptotically optimal. Numerical simulations are given in the end of the paper to corroborate our strategy.
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