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A Server Load Balancing Method Based on Genetic Algorithm

A technology of load balancing and genetic algorithm, applied in transmission systems, electrical components, etc., can solve problems such as imperfect optimization theory

Active Publication Date: 2019-01-08
ZHEJIANG SCI-TECH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The simple genetic algorithm is a heuristic search algorithm, and the optimization theory is not perfect

Method used

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  • A Server Load Balancing Method Based on Genetic Algorithm
  • A Server Load Balancing Method Based on Genetic Algorithm
  • A Server Load Balancing Method Based on Genetic Algorithm

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Experimental program
Comparison scheme
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Embodiment Construction

[0065] The technical solutions of the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0066] The invention uses the genetic algorithm to schedule the tasks of the server, and combines the mean-variance model to calculate the fitness function, obtains excellent character strings through selection, crossover and mutation, and performs iterations. A server load balancing method based on genetic algorithm, such as figure 1 shown, including the following steps:

[0067] The current system state. For the current information of each processor at time t, when there is time, a new round of task assignment is required.

[0068] The initial queue is set using a sliding window technique. The window size is fixed, and the number of tasks in the queue is equal to the window size, so the tasks in the sliding window can be allocated. When GA performs task scheduling, the tasks in the sliding window are allocated. ...

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Abstract

The invention discloses a server load balancing method based on a genetic algorithm. It involves the field of load balancing technology, including the following steps: 1) Use decimal to encode the candidate solutions of the space, and randomly generate an appropriate amount of initial string structure data as the initial population; 2) The genetic algorithm iterates with the initial population, and when the optimal string When the difference between the fitness and the minimum fitness value is less than e or the iteration reaches the preset algebra, the algorithm terminates; 3) The resource utilization rate and the execution time fitness value are evaluated and detected through the Mean‑Variance model; 4) Press the roulette The betting method selects a string with strong adaptability, performs crossover and mutation operations on the selected string, generates a new string for the next round of iteration, and returns to step 2. The present invention applies the Mean-Variance model to the evaluation of the fitness, improves the mode of the traditional objective function, and obtains resource utilization balance and execution time to measure string adaptability more effectively and improves load balancing performance.

Description

technical field [0001] The invention relates to the field of load balancing, mainly improving the load strategy based on the genetic algorithm, improving the utilization rate of server resources and reducing the execution time of the total task. Background technique [0002] In recent years, with the development of mobile Internet and cloud computing, online information has exploded. The network traffic and load of some major areas such as enterprise network, campus network and wide area network have far exceeded the processing capacity of a single server. Server cluster technology is used to solve the bottleneck problem of a single server's processing capacity. It replaces a single high-performance server with a server cluster, thereby improving the efficiency of the server system's response to user requests, shortening the waiting delay for user requests, and improving user experience. In the server cluster, the user's request needs to be distributed to the background ser...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/08
CPCH04L67/1004
Inventor 包晓安魏雪张娜吴彪熊子健
Owner ZHEJIANG SCI-TECH UNIV