Reducing Response Time with Dynamic Load Balancing and Agent Pool in Multi-Agent System

2011 ◽  
Author(s):  
Hyung Do Lee ◽  
Sung Soo Moon ◽  
Suk Jung Yong ◽  
Hee Yong Youn
Keyword(s):  
Load Balancing ◽  
Response Time ◽  
Dynamic Load ◽  
Dynamic Load Balancing ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals Implementasi Sistem Load Balancing Menggunakan Metode Least Time First Byte Dan Multi-Agent System Pada Lingkungan Virtual

2019 ◽  
Vol 4 (2) ◽  
pp. 63-70
Author(s):  
Dyah Ayu Wiranti ◽  
Kurnia Siwi Kinasih ◽  
Shinta Rizki Firdina Sugiono
Keyword(s):  
Information System ◽  
Load Balancing ◽  
Response Time ◽  
Service Provider ◽  
The Internet ◽  
Multi Agent System ◽  
Agent System ◽  
Time Value ◽  
Multi Agent ◽  
Modern Era

In this modern era, the technology is growing rapidly, the Internet is misled. This condition will be related to the service provider or commonly referred to as a server. Increasing the number of clients, the server also has to work heavier so that it often occurs overload. The Load Balancing mechanism uses the Least Time First Byte and Multi Agent system methods. This mechanism allows the server to overcome the number of users who perform service requests so that the load from the server can be resolved. This solution is considered efficient and effective because the request process on the information system will be shared evenly on multiple server back ends. The results of the research that can be proved if using this mechanism the server can work well when the request is from a user or client dating, this method successfully distributes the balancer evenly through the server backend. So the server is no longer experiencing overload. This can be proved when a system that has used the load balancing method with 300 connections generates a throughput of 123.1 KB/s as well as response time value of 4.72 MS and a system that does not use the load balancing method has a throughput of 108.4 KB/s as well as a response time value of 120.3 Ms. Therefore by implementing load balancing the performance of the system can always be improved.

A multi-agent system performing one-to-many negotiation for load balancing of electricity use

2002 ◽  
Vol 1 (2) ◽  
pp. 208-224 ◽  
Author(s):  
Frances M.T. Brazier ◽  
Frank Cornelissen ◽  
Rune Gustavsson ◽  
Catholijn M. Jonker ◽  
Olle Lindeberg ◽  
...  
Keyword(s):  
Load Balancing ◽  
Multi Agent System ◽  
Agent System ◽  
Electricity Use ◽  
Multi Agent

scholarly journals Multi-agent grid system Agent-GRID with dynamic load balancing of cluster nodes

2017 ◽  
Vol 7 (1) ◽  
pp. 485-490 ◽  
Author(s):  
M.N. Satymbekov ◽  
I.T. Pak ◽  
L. Naizabayeva ◽  
Ch.A. Nurzhanov
Keyword(s):  
Load Balancing ◽  
Dynamic Load ◽  
Virtual Machines ◽  
Dynamic Load Balancing ◽  
Grid System ◽  
Multi Agent

AbstractIn this study the work presents the system designed for automated load balancing of the contributor by analysing the load of compute nodes and the subsequent migration of virtual machines from loaded nodes to less loaded ones. This system increases the performance of cluster nodes and helps in the timely processing of data. A grid system balances the work of cluster nodes the relevance of the system is the award of multi-agent balancing for the solution of such problems.

Research on dynamic load balancing of data flow under big data platform

2021 ◽  
pp. 2150014
Author(s):  
Junlin Sun ◽  
Yi Zhang
Keyword(s):  
Big Data ◽  
Load Balancing ◽  
Response Time ◽  
Dynamic Load ◽  
Loss Rate ◽  
Data Flow ◽  
Experimental Results ◽  
Dynamic Load Balancing ◽  
Data Platform ◽  
Flow Loss

In the big data platform, because of the large amount of data, the problem of load imbalance is prominent. Most of the current load balancing methods have problems such as high data flow loss rate and long response time; therefore, more effective load balancing method is urgently needed. Taking HBase as the research subject, the study analyzed the dynamic load balancing method of data flow. First, the HBase platform was introduced briefly, and then the dynamic load-balancing algorithm was designed. The data flow was divided into blocks, and then the load of nodes was predicted based on the grey prediction GM(1,1) model. Finally, the load was migrated through the dynamic adjustable method to achieve load balancing. The experimental results showed that the accuracy of the method for load prediction was high, the average error percentage was 0.93%, and the average response time was short; under 3000 tasks, the response time of the method designed in this study was 14.17% shorter than that of the method combining TV white space (TVWS) and long-term evolution (LTE); the average flow of nodes with the largest load was also smaller, and the data flow loss rate was basically 0%. The experimental results show the effectiveness of the proposed method, which can be further promoted and applied in practice.

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