An Online Algorithm for Computation Offloading in Non-Stationary Environments

• URL: http://arxiv.org/abs/2006.12032v1
• Date: Mon, 22 Jun 2020 07:00:47 GMT
• Title: An Online Algorithm for Computation Offloading in Non-Stationary Environments
• Authors: Aniq Ur Rahman, Gourab Ghatak, Antonio De Domenico
• Abstract summary: We consider the latency problem in a task-offloading scenario, where multiple servers are available to the user equipment for outsourcing computational tasks. To account for the temporally dynamic nature of the wireless links and the availability of the computing resources, we model the server selection as a multi-armed bandit (MAB) problem. We propose a novel online learning algorithm based on the principle of optimism in the face of uncertainty, which outperforms the state-of-the-art algorithms by up to 1s.
• Score: 12.843328612860244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the latency minimization problem in a task-offloading scenario, where multiple servers are available to the user equipment for outsourcing computational tasks. To account for the temporally dynamic nature of the wireless links and the availability of the computing resources, we model the server selection as a multi-armed bandit (MAB) problem. In the considered MAB framework, rewards are characterized in terms of the end-to-end latency. We propose a novel online learning algorithm based on the principle of optimism in the face of uncertainty, which outperforms the state-of-the-art algorithms by up to ~1s. Our results highlight the significance of heavily discounting the past rewards in dynamic environments.

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