Delay-sensitive task offloading and efficient resource allocation in intelligent edge-cloud environments: A discretized differential evolution-based approach

doi:10.1016/j.asoc.2024.111637

2024-07-01

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DOI：10.1016/j.asoc.2024.111637

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The number of smart wireless devices (WDs) has enormously increased over the last few years due to the advancement of 5G/B5G networks. The advanced applications of such smart WDs, e.g., augmented reality, virtual reality, online gaming, etc., demand excessive resources. Although the WDs are equipped with limited resources, the evolution of edge computing and offloading techniques enables the WDs to offload their resourceintensive tasks to the nearby edge node. These edge nodes might experience higher loads and delays when WDs generate a huge number of tasks. Moreover, the wireless channel bandwidth and transmission data rate of the wireless channels are also limited. Therefore, optimizing the use of available bandwidth as a valuable resource and reducing latency emerge as crucial objectives while offloading tasks. In this paper, a delay -aware resource -constrained offloading problem for edge-cloud systems is mathematically formulated as a 0-1 integer linear programming, and it is shown to be NP -complete. Then, a delay -aware resourceconstrained offloading algorithm based on a discretized differential evolution (DARC-DE) is designed. The objectives of the DARC-DE are to maximize the utilization of the resources as bandwidth and minimize the delay. The vectors are efficiently encoded along with the decoding technique. The fitness function is designed by considering execution, offloading, queuing, transmission delay, and bandwidth utilization. The DARC-DE is shown to be executed in polynomial time. To evaluate DARC-DE, extensive simulation is performed in two different scenarios with varying numbers of tasks and edges. Simulation results demonstrate that the proposed DARC-DE can minimize total delay by 15% to 40% in comparison to particle swarm optimization, genetic algorithm, and bees algorithm, respectively. Simulation results also indicate a significant improvement in bandwidth utilization. Taguchi method and alternative average convergence rate are conducted. The statistical tests-analysis of variance, post -hoc, and Friedman tests-are also performed.

Keyword:

Intelligent edge computing

Resource

Task offloading

Delay

Differential evolution