An energy consumption optimization management method for cloud platform based on fusion genetic algorithm and ant colony algorithm

An ant colony algorithm, a technology that integrates genetics, is applied in the field of energy consumption optimization in designing cloud computing platforms, and can solve problems such as high energy consumption

Inactive Publication Date: 2019-01-15
GUANGDONG UNIV OF TECH
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Problems solved by technology

Therefore, when the energy consumption factor is not considered, the mismatched scheduling method wi

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  • An energy consumption optimization management method for cloud platform based on fusion genetic algorithm and ant colony algorithm
  • An energy consumption optimization management method for cloud platform based on fusion genetic algorithm and ant colony algorithm
  • An energy consumption optimization management method for cloud platform based on fusion genetic algorithm and ant colony algorithm

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Embodiment 1

[0074] A cloud platform energy optimization management method based on fusion genetic algorithm and ant colony algorithm, comprising the following steps:

[0075] S1: Define task scale, number of nodes, experimental variables and empirical parameters;

[0076] S2: define the fitness function Fitness (I) and the objective function P (I);

[0077] S3: Select the allocation scheme from the random scheduling sequence for cross mutation;

[0078] S4: Output several groups of optimized solutions;

[0079] S5: Determine whether the number of iterations is less than the maximum number of cycles and the optimization solution keeps evolving, if the result is yes, then return to step S3, if the result is otherwise, enter step S6:;

[0080] S6: Calculate the initial pheromone concentration according to the optimized solution generated by the genetic algorithm;

[0081] S7: Determine the execution status of task i in resource j, and calculate the pheromone increment of the node;

[008...

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Abstract

The invention discloses a cloud platform energy consumption optimization management method based on a fusion genetic algorithm and an ant colony algorithm. The invention adopts a two-level dispatchingmode. The first-level dispatching dispatches a task to a virtual machine for execution according to the preference selection of a user on performance and cost. According to the task attributes and resource load, the second-level scheduling searches for the appropriate cloud resources to allocate to the virtual machine. The invention divides the demand of the user for the service quality into an energy consumption demand and a cost demand. The energy consumption demand reduces the energy consumption by reducing the calculation energy consumption, transmission energy consumption and storage energy consumption of the physical resources, and the cost demand reduces the calculation cost through the comprehensive energy consumption demand and the scheduling cost.

Description

technical field [0001] The present invention designs the field of cloud computing platform energy consumption optimization, more specifically, relates to a cloud platform energy optimization management method based on fusion genetic algorithm and ant colony algorithm. Background technique [0002] At present, cloud computing, as a new type of computing method, has rapidly become a research hotspot in academia and industry due to its advantages of high scalability and high availability. For example, Google launched the Google Application Engine (Google App Engine, GAE for short), IBM launched the blue cloud computing platform, and Amazon launched the elastic computing cloud (elastic compute cloud, EC2 for short). However, there are still many challenges to realize a low-cost, efficient, safe, and easy-to-use cloud computing system, among which high energy consumption is one of the most serious problems in cloud computing systems. For example, the energy consumption generated...

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Application Information

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IPC IPC(8): G06F9/48G06N3/00G06N3/12
CPCG06F9/4893G06N3/006G06N3/126Y02E40/70Y04S10/50Y02D10/00
Inventor 钟光正陈平华
Owner GUANGDONG UNIV OF TECH
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