An Extension-Based Dynamic Load Balancing Model of Heterogeneous Server Cluster

Zhang, Xiayu; Yang, Yongquan; Chen, Baixing; Ye, Feng; Tan, Xiao

doi:10.1109/grc.2007.4403185

Cited by 3 publications

(3 citation statements)

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“…Zhang The IOLB algorithm provides better memory and CPU utilization under memory and CPU rigorous workload circumstances. This algorithm is able to deliver the similar level of performance as two already existing memory and CPU aware load balancing approaches [21].…”

Section: Workload Classificationmentioning

confidence: 78%

AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster

Ramana¹,

Ponnavaikko²

2019

IJECE

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With the rising popularity of web-based applications, the primary and consistent resource in the infrastructure of World Wide Web are clusterbased web servers. Overtly in dynamic contents and database driven applications, especially at heavy load circumstances, the performance handling of clusters is a solemn task. Without using efficient mechanisms, an overloaded web server cannot provide great performance. In clusters, this overloaded condition can be avoided using load balancing mechanisms by sharing the load among available web servers. The existing load balancing mechanisms which were intended to handle static contents will grieve from substantial performance deprivation under database-driven and dynamic contents. The most serviceable load balancing approaches are Web Server Queuing (WSQ), Server Content based Queue (QSC) and Remaining Capacity (RC) under specific conditions to provide better results. By Considering this, we have proposed an approximated web server Queuing mechanism for web server clusters and also proposed an analytical model for calculating the load of a web server. The requests are classified based on the service time and keep tracking the number of outstanding requests at each webserver to achieve better performance. The approximated load of each web server is used for load balancing. The investigational results illustrate the effectiveness of the proposed mechanism by improving the mean response time, throughput and drop rate of the server cluster.

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Section: Workload Classificationmentioning

confidence: 78%

AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster

Ramana¹,

Ponnavaikko²

2019

IJECE

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“…Zhang Xiayu et al consider CPU, Memory, Bandwidth, Disk I/O and Buffer pool slice rate to compute the load index in a cluster. Their work employ the operation of extension set, matterelement theory and dependent function which are presented in extension theory (Zhang et al, 2007).…”

Section: Workload Classificationmentioning

confidence: 99%

A Multi-Class Load Balancing Algorithm (MCLB) for Heterogeneous Web Cluster

Ramana¹,

Ponnavaikko²

2018

STUD INFORM CONTROL

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Faced with increasing demand for network services by a huge number of users, requests to the web servers have significantly skyrocketed. Consequently, most of these servers need to run twenty-four hours a day, seven days a week with a high reliability and availability. Thus, the tremendous growth of the Internet has led the requirement of web server cluster management in order to deal with these issues effectively. Without using efficient mechanisms, an overloaded web server cannot provide great performance. In clusters, this overloaded condition can be avoided using load balancing mechanisms by sharing the load among available web servers. The existing load balancing mechanisms which were intended to handle static contents will be deprived from substantial performance under database-driven and dynamic contents. The most serviceable load balancing approaches to provide better results under specific conditions are Weighted Round Robin (WRR) and Client Aware Policy (CAP). By considering this, a Multi-Class Load Balancing algorithm (MCLB) was proposed for web server clusters and also an analytical model was proposed for calculating the load of a web server. The requests are classified based on the service time and keep tracking the number of outstanding requests at each webserver to achieve better performance. The service time of each request class type is used for load balancing. The experimental results demonstrate the effectiveness of the proposed approach by improving the average response time, error rate and throughput of the web server cluster.

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“…Extecnis [2][3]is based on the extension of matter. It establishes a set of formal method and solves the contradictory problem in data-based way and is widely used in many fields [4][5][6]. Extension detection based on extenics solves how to detect the character and value of non-detectable matter-element by transforming the detectable matter-element and solve detecting every kind of information which cannot to be detected or is difficult to be detected.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Detecting Technology Based on Extension Fuzzy Method

Wang¹,

Yang²,

Ge³

et al. 2016

Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

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Abstract-This paper studies a new kind of intelligent detecting technology which includes key detectable matterelements distilling, adaptive degree analyzes, focusing and reasoning mechanism construction, focusing processing. This paper researches on the focusing and reasoning mechanism construction, which is to construct the reasoning mechanism and use it to get the focusing. The focusing is actually to work out the core element of the detectable matter-element according to the adaptive degree distributing. This paper puts forward the methods to solve all types of focusing: to the one-dimensional and two-dimensional focusing, connects the adaptive degree with the dependent function to figure out the focusing; to the threedimensional and multidimensional focusing, constructs an extension fuzzy -based knowledge reasoning expert system and uses the extension reasoning and fuzzy rules to work out the focus.

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An Extension-Based Dynamic Load Balancing Model of Heterogeneous Server Cluster

Cited by 3 publications

References 0 publications

AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster

AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster

A Multi-Class Load Balancing Algorithm (MCLB) for Heterogeneous Web Cluster

Research on Intelligent Detecting Technology Based on Extension Fuzzy Method

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