A multi-queue off-peak scheduling model and method based on task classification in cloud computing

Abstract

The invention discloses a multi-queue peak-alternation scheduling model and a multi-queue peak-alteration scheduling method based on task classification in cloud computing. The multi-queue peak-alternation scheduling model is characterized by comprising a task manager, a local resource manager, a global resource manager and a scheduler. The multi-queue peak-alternation scheduling method comprises the following steps: firstly, according to demand conditions of a task to resources, dividing tasks into a CPU (central processing unit) intensive type, an I / O (input / output) intensive type and a memory intensive type; sequencing the resources according to the CPU, the I / O and the memory load condition, and staggering a resource using peak during task scheduling; scheduling a certain parameter intensive type mask to a resource with relatively light index load, scheduling the CPU intensive type mask to the resource with relatively low CPU utilization rate. According to the multi-queue peak-alternation scheduling model and the multi-queue peak-alternation scheduling method disclosed by the invention, load balancing can be effectively realized, scheduling efficiency is improved and the resource utilization rate is increased.

Description

technical field [0001] The invention relates to a multi-queue peak shift scheduling model and method based on task classification in cloud computing. Background technique [0002] The application tasks in cloud computing are diverse, and the requirements for resources are also very different. Some have large storage requirements, some have requirements for computing power, that is, CPU, and some are data-intensive, that is, their I / O is more obvious. The diversity of tasks and the difference in demand for resources will cause load imbalance. On the other hand, resources and their loads have many dynamic and uncertain factors. For example, the loads of resource nodes change dynamically over time, and resource requests will also change with the changes of years, seasons, and holidays; and resources themselves have many Changes, and resources may join or withdraw at any time, which will cause a series of problems. If the load of too many resource nodes is too low, it will u...

AI Technical Summary

Problems solved by technology

[0017] In order to solve the deficiencies in the prior art, the present invention provides a multi-queue off-peak scheduling model and method based on task classification in cloud computing, which solves the dynamicity of resources and the diversity of tasks in cloud computing and its demand for resources Differences may lead to load imbalance, affecting scheduling efficiency and resource utilization

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