A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Task scheduling plays a critical role in the performance of the edge-cloud collaborative. Whether the task is executed in the cloud and how it is scheduled in the cloud is an important issue. On the basis of satisfying the delay, this paper will schedule tasks on edge devices or cloud and present a...

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Bibliographic Details
Published in:	Scientific programming 2020-03, Vol.2020, p.1-9
Main Authors:	Wang, Shudong, Li, Yanqing, Pang, Shanchen, Lu, Qinghua, Wang, Shuyu, Zhao, Jianli
Format:	Article
Language:	eng
Subjects:	Cloud computing Clouds Collaboration Completion time Crossovers Data processing Energy consumption Evolutionary algorithms Genetic algorithms Heuristic Internet of Things Mutation Optimization Problems Schedules Scheduling Strategy Task scheduling
Online Access:	Request full text
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Summary:	Task scheduling plays a critical role in the performance of the edge-cloud collaborative. Whether the task is executed in the cloud and how it is scheduled in the cloud is an important issue. On the basis of satisfying the delay, this paper will schedule tasks on edge devices or cloud and present a task scheduling algorithm for tasks that need to be transferred to the cloud based on the catastrophic genetic algorithm (CGA) to achieve global optimum. The algorithm quantifies the total task completion time and the penalty factor as a fitness function. By improving the roulette selection strategy, optimizing mutation and crossover operator, and introducing cataclysm strategy, the search scope is expanded. Furthermore, the premature problem of the evolutionary algorithm is effectively alleviated. The experimental results show that the algorithm can address the optimal local issue while significantly shortening the task completion time on the basis of satisfying tasks delays.
ISSN:	1058-9244 1875-919X