Swarm cluster scheduling algorithm based on combined prediction model

A combined prediction and cluster scheduling technology, applied in computing, complex mathematical operations, software simulation/interpretation/simulation, etc., can solve problems such as unbalanced resource utilization, achieve high accuracy, node load balancing, and improve overall resource utilization The effect of rate and node benefit

Active Publication Date: 2019-10-08
HOHAI UNIV CHANGZHOU
View PDF3 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The resource scheduling strategy provided by the Swarm cluster is relatively simple, and there will be unbalanced resource utilization on the nodes. A Swarm cluster scheduling algorithm based on a combined prediction model is provided.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Swarm cluster scheduling algorithm based on combined prediction model
  • Swarm cluster scheduling algorithm based on combined prediction model
  • Swarm cluster scheduling algorithm based on combined prediction model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0044] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0045] Such as figure 1 Shown, the present invention is based on the Swarm cluster dispatching algorithm of combined prediction model and comprises the following steps;

[0046] Step 1) carry out weight calculation to the predicted value of the CPU of node and internal memory according to unary linear regression model;

[0047] Step 2) summing the CPU and the memory prediction value of the node according to the gray model to obtain the node weight;

[0048] Step 3) Assuming that the optimal weights of the unary linear regression model and the gray prediction model are calculated, the two weigh...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a Swarm cluster scheduling algorithm based on a combined prediction model. For the defect that the prediction accuracy of a traditional unary linear regression model and a greymodel is relatively low, the problems existing in a Swarm cluster built-in scheduling strategy are comprehensively considered, the two prediction models are combined, and a prediction model for performing weighted comprehensive calculation on two weights to form combined optimization is established. The combined prediction model is superior to a traditional unary linear regression model and a greymodel, and has high prediction accuracy.

Description

technical field [0001] The invention belongs to the field of cluster scheduling, in particular to a Swarm cluster scheduling algorithm based on a combination prediction model. Background technique [0002] Docker occupies an important position in the cloud ecology, and Swarm also occupies an important position in the PaaS (platform and service) / IaaS (infrastructure and service) layer of the cloud architecture. The main task of Swarm is to run the container on the appropriate node according to the scheduling policy. Due to the difference of the container running on the node, the resource utilization rate is also different. The resource utilization of each node determines the load of the entire cluster. Therefore, the optimization of the cluster scheduling strategy is particularly important. , [0003] There are three built-in scheduling strategies in Swarm, namely Random (random), Spread (diffusion) and Binpack (boxing). The advantage of the Random strategy is that it is s...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/48G06F9/50G06F9/455G06F17/13G06F17/18
CPCG06F9/4881G06F9/5016G06F9/505G06F9/5077G06F9/5083G06F9/45558G06F17/13G06F17/18G06F2009/45583Y02D10/00
Inventor 孙宁万拥王彬李昌澔
Owner HOHAI UNIV CHANGZHOU
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap