Abstract
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.
Highlights
With the rise of edge computing, the convergence of cloud computing and edge computing has become a major focus [1,2,3]
There are few studies that achieve the least total time based on the delay of meeting each task. erefore, based on the research of genetic algorithms, this paper raises a task scheduling algorithm called catastrophic genetic algorithm (CGA) based on cataclysm strategy [29], which mainly considers the time delay to achieve the minimum total execution time
For tasks that need to be processed in the cloud, we used CloudSim 3.0 to implement the algorithms, by adding the bindCloudletToVM method in the DAtacenterBroker class; the CGA algorithm based on the catastrophe genetic algorithm is added to carry out the simulation experiment
Summary
With the rise of edge computing, the convergence of cloud computing and edge computing has become a major focus [1,2,3]. Edge computing is more suitable for local, real-time, short-cycle data processing and analysis. Edge computing can better support real-time intelligent decision making and execution of local business. If data analysis and processing are all implemented in the cloud, it is sometimes difficult to meet the real-time requirements of the service. It seriously affects the business experience of end customers. How to effectively cooperate with the intersection and mutation operations, make the convergence faster, and jump out of the local optimum in the solution process is a valuable research content of the current genetic algorithm. Is paper proposes a task scheduling strategy for edgecloud collaborative computing based on disaster genetic algorithm.
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