Resource Allocation Algorithm Based on Edge Server Task Migration

doi:10.12068/j.issn.1005-3026.2024.12.003

Abstract

Abstract:

IIoT （industrial Internet of Things） devices send tasks that cannot be computed locally to edge servers for processing. However， different device densities result in imbalanced computational workloads among various edge servers， leading to significant computation latency. To solve this problem， a task migration algorithm based on modified deep deterministic policy gradient （MDDPG） is proposed. The algorithm has a mechanism of priority empirical replay and random weight averaging based on depth deterministic strategy gradient to find the best migration strategy and reduce the computation delay of the task. Experimental results show that MDDPG algorithm has a better performance than the traditional algorithms.

Key words: industrial Internet of Things, policy gradient, task migration, priority experience replay, random weight averaging

CLC Number:

TN 929.5

Jing-jing WU, Zi-xuan ZHANG. Resource Allocation Algorithm Based on Edge Server Task Migration[J]. Journal of Northeastern University(Natural Science), 2024, 45(12): 1688-1695.

Figures/Tables 9

Fig.1 Architecture diagram of task migration network

Fig.2 Architecture of DDPG algorithm

Fig.3 Impact of learning rate on convergence performance

Fig.4 Impact of discount factor on convergence performance

Fig.5 Impact of exploration rate on convergence performance

Fig.6 Convergence performance comparison of different algorithms

Fig.7 Impact of task data size on delay

Fig.8 Impact of computing capacity in the server on delay

Fig.9 Impact of servers quantity on delay

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