A Survey of Load Balancing Techniques for Data Intensive Computing

Sui, Zhiquan; Pallickara, Shrideep

doi:10.1007/978-1-4614-1415-5_6

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Also, the algorithm usually does not work as efficiently in the beginning as it is in the learning phase. [9][31]…”

Section: Dynamic Load Balancing Schemes In Stream Based Scenariosmentioning

confidence: 99%

Comparative Study Load Balance Algorithms for Map Reduce Environment

Hefny¹,

Khafagy²,

Wahdan³

2014

IJCA

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MapReduce is a famous model for data-intensive parallel computing in shared-nothing clusters. One of the main issues in MapReduce is the fact of depending its performance mainly on data distribution. MapReduce contains simple load balance technique based on FIFO job scheduler that serves the jobs in their submission order but unfortunately it is insufficient in real world cases as it missed many factors that impact the performance such as heterogeneity factor and data skewness, so Load balancing is important to make all resources utilized evenly and more efficiently. There are two main schemes in load balancing a-Static Load Balancing Schemes b-Dynamic load balancing. The main aim of this work is to study and compare existing Load Balance algorithms also to illustrate the features of Load Balance algorithms

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“…Also, the algorithm usually does not work as efficiently in the beginning as it is in the learning phase. [9][31]…”

Section: Dynamic Load Balancing Schemes In Stream Based Scenariosmentioning

confidence: 99%

Comparative Study Load Balance Algorithms for Map Reduce Environment

Hefny¹,

Khafagy²,

Wahdan³

2014

IJCA

View full text Add to dashboard Cite

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L3B: Low level load balancer in the cloud

et al. 2013

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Load Balancing in Heterogeneous Cluster Cloud Computing

Nirmalan¹,

Gokulakrishnan

Nayahi

2019

Advances in Computer and Electrical Engineering

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Cloud computing is a modern exemplar to provide services through the internet. The development of cloud computing has eliminated the need of manpower, which is mainly used for the management of resources. During the cloud computing process, the term cloud balancing is a vital one. It deals with distribution of workloads and computing resources. The load balancing allows the company to balance the load according to the demands by the allocation of the resources to multiple servers or networks. The quality of service (QoS) metrics, including cost, response time, performance, throughput, and resource utilization are improved by means of load balancing. In this chapter, the authors study the literature on the load-balancing algorithms in heterogeneous cluster cloud environment with some of its classification. Additionally, they provide a review in each of these categories. Also, they provide discernment into the identification of open issues and guidance for future research work.

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A Survey of Load Balancing Techniques for Data Intensive Computing

Cited by 3 publications

References 11 publications

Comparative Study Load Balance Algorithms for Map Reduce Environment

Comparative Study Load Balance Algorithms for Map Reduce Environment

L3B: Low level load balancer in the cloud

Load Balancing in Heterogeneous Cluster Cloud Computing

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