Performance-aware Partial Task Offloading in Multi-access Edge Computing

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Darchini Tabrizi Mohsen; Entezari Maleki Reza

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Journal Paper

Paper Information

Journal: JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT) Year:2024 | Volume:13 | Issue:2 Page(s): 55-68

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Information Journal Paper

Title

Performance-aware Partial Task Offloading in Multi-access Edge Computing

Author(s)

Darchini Tabrizi Mohsen | Entezari Maleki Reza | Issue Writer Certificate

Keywords

Task Offloading

Multi-access Edge Computing

Resource Allocation

performance

Reinforcement Learning

Abstract

The proliferation of new applications has led to new challenges in energy consumption, task processing, and data storage. Multi-access Edge Computing (MEC) is a new computational paradigm that can transfer workloads from users' devices to powerful servers in the same location with the least possible time and energy overhead to improve the QoS and performance measures. Since joint Task Offloading and Resource Allocation in MEC is one of the main concerns of performance-aware applications, this paper explores a fine-grained view to this problem under the dynamic and time-varying conditions of the entire system. The main goal of this paper is to reduce the normalized cost of the system, which is the weighted sum of the completion time and the consumed energy, by formulating the problem and proposing a new algorithm based on the Reinforcement Learning approach. The results of the simulations performed under different scenarios corresponding to the real-world systems show the improvement in the completion time and energy consumption of the tasks compared to other existing methods and leads to an average reduction of 22 and 24 percent in the scenarios related to the evaluation of the normalized cost of the system.

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