Workload Allocation Mechanism for Minimum Service Delay in Edge Computing-Based Power Internet of Things

Abstract

As a novel computing technology closer to business ends, edge computing has become an effective solution for delay sensitive business of power Internet of Things (IoT) and promotes the application and development of the IoT technology in smart grids. However, the inherent characteristics of a single edge node with limited resources may fail to meet the delay requirements for access ubiquitous IoT businesses of massive access. Multiple edge nodes are needed to cooperate with each other to optimize workload allocation to provide lower delay services. To this end, this paper proposes a workload allocation mechanism, orienting edge computing-based power IoT, which minimizes service delay. The workload optimization allocation model is established, and the optimal workload allocation oriented on delay among multiple edge nodes is further realized on the basis of computing resource optimization within the single edge node. The balanced initialization, resource allocation, and task allocation (BRT) algorithm are proposed. Based on the balanced initialization of workload within edge nodes, the particle swarm algorithm modified by the pheromone strategy is used to solve the problem of the computing resources' allocation inside edge nodes. Finally, the task allocation among multiple edge servers is converted into a semi-definite programming problem. The simulation results show that the proposed BRT algorithm reduces the service delay by 9.1%, 16.9%, and 26.4%, and the service delay growth rate by 24.6%, 34.5%, and 38.7%, respectively, compared with the simulated annealing algorithm (SAA), LoAd Balancing (LAB), and Latency-awarE workloAd offloaDing (LEAD) algorithms. INDEX TERMS Edge computing, multiple business, power Internet of Things, service delay, workload allocation.

QoS-Aware Workload Distribution in Hierarchical Edge Clouds: A Reinforcement Learning Approach

“…• Generic edge clouds [15], [16], [19], [21], [29], [30] • Hierarchical clouds [13], [14], [20], [22]- [24], [26] • Fog clouds [18], [25], [27], [28] • Other [17] Decision variables…”

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“…• Problem relaxation [13], [14], [17], [19], [20], [22], [25], [29] • Approximation [15], [18], [21], [23], [24], [26], [30] • Heuristic based on machine learning [27], [28] • Game theory [16] QoS awareness…”

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