Dynamic load balancing algorithm for scalable heterogeneous web server cluster with content awareness

Tiwari, Ajay; Kanungo, Priyesh

doi:10.1109/tisc.2010.5714626

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…Ajay Tiwari et al [22] proposed a dynamic content aware load balancing algorithm for web cluster in heterogeneous environment. This algorithm uses utilization ratio, queue length and server's processing capability as load indices.…”

Section: Workload Classificationmentioning

confidence: 99%

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: 99%

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

Ramana¹,

Ponnavaikko²

2019

IJECE

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“…The EMD method has been proved quite effective in extracting characteristic information from non-linear and non-stationary time series [9]. It is a self-adaptive decomposition method, which assumes that the original data may have many different modes of oscillations coexisting at the same time.…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

Improving Forecasting Accuracy of Annual Runoff Time Series Using RBFN Based on EEMD Decomposition

Ma¹,

Li²

2017

dtetr

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Annual runoff forecasting is one of the most important applications for effective reservoir management. Given the time-varying and non-linear characteristics of river runoff data, a novel hybrid model is proposed to improve the forecasting accuracy. First, the original data of runoff is decomposed into a number of intrinsic mode functions (IMFs) and one residual term using the ensemble empirical mode decomposition (EEMD) method. Then, these sub-series are modeled respectively by radial basis function network (RBFN) model. Finally, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff time series. The proposed hybrid model is examined by predicting the annual runoff of Maduwang station in Bahe River, China. The comparison results indicate that the proposed hybrid model can effectively enhance RBFN approach for annual runoff series forecasting accuracy and it is superior to the commonly used model like auto-regressive integrated moving average (ARIMA) and back-propagation network (BPNN).

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“…Moonian et al [31] modify the RR algorithm to take job deadlines into account by dynamically allocating CPU shares. Tiwari et al [32] extend RR to heterogeneous environments by considering a server's processing power, queue length, and utilization, resulting in a Proportional RR like scheme. Rong et al [33] extend RR to heterogeneous environments and propose a hierarchical routing scheme for heavy traffic.…”

Section: ) Rnd Lbsmentioning

confidence: 99%

HALO: Heterogeneity-Aware Load Balancing

Gandhi

Zhang

Mittal

2015

2015 IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems

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Load Balancers (LBs) play a critical role in managing the performance and resource utilization of distributed systems. However, developing efficient LBs for large, distributed clusters is challenging for several reasons: (i) large clusters require numerous scheduling decisions per second, (ii) such clusters typically consist of heterogeneous servers that widely differ in their computing power, and (iii) such clusters often experience significant changes in load. In this paper we propose HALO, a class of scalable, heterogeneity-aware LBs for cluster systems. HALO LBs are based on simple randomized algorithms that are analytically optimized for heterogeneity. We develop HALO for randomized, Round-Robin, and Power-of-D LBs. We illustrate the benefits of HALO and demonstrate its superiority over other comparable LBs using analytical, simulation, and (Apache-based) implementation results. Our results show that HALO LBs provide significantly lower response times without incurring additional overhead across a wide range of scenarios.

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Dynamic load balancing algorithm for scalable heterogeneous web server cluster with content awareness

Cited by 13 publications

References 7 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

Improving Forecasting Accuracy of Annual Runoff Time Series Using RBFN Based on EEMD Decomposition

HALO: Heterogeneity-Aware Load Balancing

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