Server load balancing method based on genetic algorithm

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

Active Publication Date: 2016-06-22
ZHEJIANG SCI-TECH UNIV
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Problems solved by technology

The simple genetic algorithm is a heuristic search a

Method used

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  • Server load balancing method based on genetic algorithm
  • Server load balancing method based on genetic algorithm
  • Server load balancing method based on genetic algorithm

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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, and relates to the technical field of load balancing. The server load balancing method comprises the following steps that 1) candidate solutions of space are coded by adopting a decimal system, and a moderate amount of initial string structure data are randomly generated to act as initial populations; 2) iteration is performed on the initial populations by adopting the genetic algorithm, and the algorithm ends when the difference between adaptability of the optimal character string and the minimum adaptability value is less than e or iteration reaches the preset number; 3) assessment detection of resource utilization rate and an execution time adaptability value is performed through a Mean-Variance model; and 4) the character strings of high adaptability are selected according to a roulette method, and crossover and mutation operation is performed on the selected character strings so that new character strings are generated to enter the next round of iteration and the process returns to the step 2. The Mean-Variance model is applied to assessment of adaptability so that the mode of a conventional target function is improved, the adaptability of the character strings can be more effectively measured by the obtained resource use balancing situation and execution time, and thus the load balancing performance can be enhanced.

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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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
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