Cloud computing task scheduling method based on response time optimization

Abstract

The invention relates to a cloud computing task scheduling method based on response time optimization. The method includes the steps that a cloud computing system based on response time optimization is constructed, the transmission time of task fragments is calculated, the average processing time of the task fragments at computational nodes is calculated, the total processing time of the task fragments is calculated, task response time is calculated, an objective function of the cloud computing task scheduling problem is constructed, and the task fragmentation scheme of schedulers is calculated. Starting from parallel processing of the task fragments, the cloud computing task scheduling method based on response time optimization uses the longest execution time of task fragmentation as the task response time, the minimum task response time of each scheduler is used for target modeling, and a novel task scheduling method is obtained. The cloud computing task scheduling method based on response time optimization can more reflect the characteristic of task parallel processing. Experiments show that whether the response time value of each scheduler or the objective function value, the cloud computing task scheduling method is superior to both a game playing algorithm and a balance scheduling algorithm, and when the scale of the system is increased or the load is increased, the cloud computing task scheduling method based on response time optimization has obvious advantages compared with the other two algorithms as well.

Description

technical field [0001] The invention relates to the field of cloud computing, in particular to a cloud computing task scheduling method based on response time optimization. Background technique [0002] Cloud computing is an Internet-based computing model further developed from distributed computing, parallel computing and grid computing. The goal of cloud computing task scheduling is to achieve optimal scheduling of tasks submitted by users, and try to improve the overall throughput of cloud computing systems. [0003] At present, the research on cloud computing task scheduling algorithms at home and abroad mainly aims at the earliest completion time, service quality, load balancing, economic principles and so on. In terms of task balance scheduling, Yi Kan et al. modeled the m×n grid environment, and took the average completion time of each scheduling node to schedule task fragments as the optimization goal, and proposed the Nash balance problem of task scheduling; Takin...

AI Technical Summary

Problems solved by technology

The above task scheduling research work adopts different ideas, uses different mathematical tools, and has achieved good research results, but there is one thing in common: task scheduling is based on processing time, and tasks are decomposed into task slices before processing. The execution on the server is parallel, but related work implies the assumption of serial execution of task slices. Obviously, the parallel execution of task slices is more in line with the nature and characteristics of cloud computing to provide powerful parallel processing capabilities.

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