Technical gazette, Vol. 29 No. 5, 2022.
Original scientific paper
https://doi.org/10.17559/TV-20220518055833
Task Scheduling Based on Grey Wolf Optimizer Algorithm for Smart Meter Embedded Operating System
Wang Shuang
; Institute of Metrology, Chinese Academy of Electricity Power, Beijing 100192, China
Duan Xiaomeng
; Institute of Metrology, Chinese Academy of Electricity Power, Beijing 100192, China
Zhao Ting
; Institute of Metrology, Chinese Academy of Electricity Power, Beijing 100192, China
Wang Xiaodong
; Institute of Metrology, Chinese Academy of Electricity Power, Beijing 100192, China
Abstract
In recent years, with the rapid development of electric power informatization, smart meters are gradually developing towards intelligent IOT. Smart meters can not only measure user status, but also interconnect and communicate with cell phones, smart homes and other cloud devices, and these core functions are completed by the smart meter embedded operating system. Due to the dynamic heterogeneity of the user program side and the system processing side of the embedded system, resource allocation and task scheduling is a challenging problem for embedded operating systems of smart meters. Smart meters need to achieve fast response and shortest completion time for user program side requests, and also need to take into account the load balancing of each processing node to ensure the reliability of smart meter embedded systems. In this paper, based on the advanced Grey Wolf Optimizer, we study the scheduling principle of the service program nodes in the smart meter operating system, and analyze the problems of the traditional scheduling algorithm to find the optimal solution. Compared with traditional algorithms and classical swarm intelligence algorithms, the algorithm proposed in this paper avoids the dilemma of local optimization, can quickly allocate operating system tasks, effectively shorten the time consumption of task scheduling, ensure the real-time performance of multi task scheduling, and achieve the system tuning balance. Finally, the effectiveness of the algorithm is verified by simulation experiments.
Keywords
gray wolf algorithm; heterogeneous multicore; load balancing; real time
Hrčak ID:
281678
URI
Publication date:
15.10.2022.
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