Load Balancing Techniques for Efficient Traffic Management in Cloud Environment

Sasidhar, T Tulasi; Havisha, Vani; Koushik, S.; Deep, M. Prudhvi; Reddy, V. Krishna

doi:10.11591/ijece.v6i3.pp963-973

Cited by 10 publications

(10 citation statements)

References 4 publications

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“…Explaining research chronological, including research design, research procedure (in the form of algorithms, Pseudocode or other), how to test and data acquisition [18], [19]. The description of the course of research should be supported references so that the explanation can be accepted scientifically [20], [21].…”

Section: Methodsmentioning

confidence: 99%

Elastic neural network method for load prediction in cloud computing grid

Qaddoum

El-Emam²,

Abualhaj

2019

IJECE

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Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.

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Section: Methodsmentioning

confidence: 99%

Elastic neural network method for load prediction in cloud computing grid

Qaddoum

El-Emam²,

Abualhaj

2019

IJECE

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“…Cloud computing data centers offer thousands of physical servers networked via high bandwidth network infrastructures that communicate with one another to provide highly available and flexible services. 2 Although cloud computing services suggest many benefits, there are also many issues and open research problems to provide these services such as load balancing solutions, [3][4][5][6][7] security challenges, [8][9][10][11] task scheduling, [12][13][14] and high availability/reliability challenges. [15][16][17][18][19] The large-scale heterogeneity nature of cloud services leads to frequent failures in these systems.…”

Section: Introductionmentioning

confidence: 99%

Dependability analysis for characterizing Google cluster reliability

Mesbahi

Rahmani

Hosseinzadeh

2019

Int J Communication

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Cloud solutions are emerging as a new suitable way of transforming traditional IT data centers to highly available and reliable computing resources for hosting critical applications and data. However, software and hardware failures are a common problem in cloud datacenters that can lead to harmful damages. In this paper, we analyze the physical server failures in the Google cloud datacenter. We study the Google cluster properties to investigate the relationship among physical servers' failure rate and jobs failure events. The failure rate of Google cluster executed jobs and servers is taken into consideration during a 29-day period. We present a reliability model for Google cluster physical machines using the continuous time Markov chains according to this observation. We attempt to analyze the obtained model through SHARPE software packages to improve the understanding of failure events in the Google cloud cluster. We also explore the cluster availability based on parameters like steady-state availability, steady-state unavailability, mean time to failure, and mean time to repair in the Google cluster.

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“…The technique which is used to redistribute the workload from loaded servers to idle servers to improve the performance is called Load balancing. Load balancing is one of the crucial issues, which divides the workload dynamically among the servers by improving the performance of the system [2]. The most promising approach to handle popular web sites is to use a distributed architecture which maintains a virtual single interface.…”

Section: Introductionmentioning

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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Load Balancing Techniques for Efficient Traffic Management in Cloud Environment

Cited by 10 publications

References 4 publications

Elastic neural network method for load prediction in cloud computing grid

Elastic neural network method for load prediction in cloud computing grid

Dependability analysis for characterizing Google cluster reliability

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

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