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Joint Resource Allocation and Multi-Part Collaborative Task Offloading in MEC Systems | IEEE Journals & Magazine | IEEE Xplore

Joint Resource Allocation and Multi-Part Collaborative Task Offloading in MEC Systems


Abstract:

This paper investigates the multi-part collaborative task offloading with multiple servers in mobile edge computing (MEC) systems by considering server overload and long-...Show More

Abstract:

This paper investigates the multi-part collaborative task offloading with multiple servers in mobile edge computing (MEC) systems by considering server overload and long-term system performance. Our goal is to achieve the optimal user experience within the range of the network operator’s affordable cost. To this end, we design a two-layer computation offloading framework based on the multi-part offloading mode and the collaborations among small cell base station (SBS) servers. Furthermore, a multi-object constrained optimization problem is formulated, which jointly optimizes user association, channel allocation, and multi-part collaborative task offloading. To solve this problem, we propose a Genetic and Deep Deterministic Policy Gradient (GADDPG)-based computation offloading scheme, where the long-term system performance is considered. Numerical and simulation results demonstrate that the proposed GADDPG-based computation offloading scheme outperforms other methods in effectively solving the server overload and guarantees long-term system performances while ensuring the best user experience.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 71, Issue: 8, August 2022)
Page(s): 8877 - 8890
Date of Publication: 11 May 2022

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