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Cloud computing QoS guarantee scheduling optimization method based on hybrid computing mode

A technology of hybrid computing and optimization methods, applied in computing, genetic models, genetic rules, etc., can solve problems such as high difficulty in rational allocation of resources, complex task execution, etc., and achieve effective results in solving scheduling optimization problems

Inactive Publication Date: 2021-04-27
哈尔滨航天恒星数据系统科技有限公司
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Problems solved by technology

[0005] In order to solve the problems of complex task execution and high difficulty in rational allocation of resources in the cloud computing environment, the present invention provides a cloud computing QoS guarantee scheduling optimization method based on a hybrid computing model. The technical solution of the method is:

Method used

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  • Cloud computing QoS guarantee scheduling optimization method based on hybrid computing mode
  • Cloud computing QoS guarantee scheduling optimization method based on hybrid computing mode
  • Cloud computing QoS guarantee scheduling optimization method based on hybrid computing mode

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specific Embodiment approach 1

[0032] Specific implementation mode one: as figure 1 As shown, it is a composition diagram of each step, and then a cloud computing QoS guarantee scheduling optimization method based on a hybrid computing mode includes the following steps:

[0033] Step 1, index constraint conversion, to obtain a single index constraint problem;

[0034] Step 2, in the calculation link of the first stage, the fuzzy result is solved by the genetic algorithm mode;

[0035] Step 3, the calculation link of the second stage, takes the calculation result of the first stage as input, and solves the final result by the ant colony algorithm mode.

specific Embodiment approach 2

[0036] Specific embodiment two: according to the cloud computing QoS guarantee scheduling optimization method based on the hybrid computing mode described in specific embodiment one, its optimization step can also complete the service target through the model building process, and its implementation steps are as follows:

[0037] First establish a model, define that there are M virtual resource pools in the cloud computing environment, then the whole of the virtual resource pool can be expressed as a set VM={VM 1 , VM 2 ,...,VM m}; The service to be executed is composed of N tasks, and the service to be executed can be expressed as a set T={t 1 , t 2 ,...,t n}; The resource set composed of K kinds of resource capabilities can be expressed as a set R={R 1 , R 2 ,...,R k}; a resource R k perform tasks i The required capacity can be expressed as R(t i , R k ); the ability of a virtual resource pool to provide a resource to perform tasks is limited to S(VM i , R k ); c...

specific Embodiment approach 3

[0050] Specific implementation mode three: In addition to the steps described in implementation mode one, it can also be refined as:

[0051] For step 1, the transformation of the index constraints described is to simplify the three index constraints of service execution cycle, service energy cost, and service migration consumption into single-dimensional index constraints, and the transformation process is the membership degree based on the constraint weight ratio transform. According to the target model in the specific implementation 1, the converted single-dimensional target is obtained:

[0052]

[0053] where X time enforce time constraints for services, Its degree of membership; X boss To serve the energy cost constraint, Its degree of membership; X work Consume constraints for service migration, for its membership.

[0054] For step 2, the first phase of the calculation link, the genetic algorithm mode is used to solve the fuzzy results, and the improvement o...

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Abstract

The invention relates to a cloud computing QoS guarantee scheduling optimization method based on a hybrid computing mode, belongs to the field of cloud computing QoS guarantee scheduling optimization, and aims to solve the problems of complex task execution and high difficulty in reasonable resource allocation in a cloud computing environment; the method includes converting the problem into a single index through dimensionality reduction, and solving a fuzzy result in a genetic algorithm mode; taking an operation result as input, solving a final result in an ant colony algorithm mode, comprehensively considering three index constraints of a service execution period, service energy cost and service migration consumption in a cloud computing environment, and performing hierarchical application on a genetic method and an ant colony algorithm method in a hybrid computing mode; according to the invention, the emerging of the shortages of the existing algorithm is avoided, the complementary advantages are realized, the scheduling optimization problem solving is more efficient, the task execution is complicated, and the resource reasonable distribution difficulty is greatly reduced.

Description

technical field [0001] The invention relates to a cloud computing QoS guarantee scheduling optimization method based on a hybrid computing mode, in particular to an optimization method under the hybrid computing mode, and belongs to the field of cloud computing QoS guarantee scheduling optimization. Background technique [0002] QoS (Quality of Service) refers to a network that can use various basic technologies to provide better service capabilities for designated network communications. It is a security mechanism for the network and is used to solve problems such as network delay and congestion. a technique. QoS guarantee is very important for networks with limited capacity, especially for streaming multimedia applications, such as VoIP and IPTV, because these applications often require a fixed transmission rate and are sensitive to delay. [0003] In the context of cloud computing, the QoS guarantee for services is particularly important, especially the QoS guarantee for...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/48G06F9/50G06N3/12
CPCG06F9/4881G06F9/5027G06N3/126
Inventor 周含笑李宗鑫邵文杰谢雨王建勋于雷刘源姜宇董丽娜刘京京
Owner 哈尔滨航天恒星数据系统科技有限公司
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