A survey on scheduling and load balancing techniques in cloud computing environment

Shaw, Subhadra Bose; Singh, Anil Kumar

doi:10.1109/iccct.2014.7001474

Cited by 63 publications

(24 citation statements)

References 22 publications

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“…With the assistance of cloud computing definition, elasticity is defined as the creation of numbers of virtual sensor instances depending on user's demand. Therefore, in order to fulfill the user demands cloud must provide the high performance gain and at the same time must be beneficial for the Cloud Service Provider (CSP) (Shaw & Singh, 2014). Virtualization offers the potential for on-the-fly and on demand configuration of physical sensors to run various tasks and virtual sensors (Espadas et al, 2013).…”

Section: Cloud Computingmentioning

confidence: 99%

WITHDRAWN: Towards sensor-cloud integration: A survey of enabling technologies and architectures

Liaqat

Chang

Gani

et al. 2016

International Journal of Information Management

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Section: Cloud Computingmentioning

confidence: 99%

WITHDRAWN: Towards sensor-cloud integration: A survey of enabling technologies and architectures

Liaqat

Chang

Gani

et al. 2016

International Journal of Information Management

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“…Their method achieves global load balancing through local server action. It works well with heterogeneous resources, however, unable to increase the performance when more servers are added to clusters . Moreover, it is not supported by any concrete mathematical analysis.…”

Section: Related Workmentioning

confidence: 95%

“…It has been suggested that the underutilized server should move its workload to another server so that it can be turned off . As discussed in , after migration, the destination server might be underutilized. Therefore, this may initiate other migrations that causes extra cost and performance degradation.…”

Section: Related Workmentioning

confidence: 99%

A self‐organized load balancing mechanism for cloud computing

Khani

Yazdani

Mohammadi

2016

Concurrency and Computation

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Summary The growth in computer and networking technologies over the past decades established cloud computing as a new paradigm in information technology. The cloud computing promises to deliver cost‐effective services by running workloads in a large scale data center consisting of thousands of virtualized servers. The main challenge with a cloud platform is its unpredictable performance. A possible solution to this challenge could be load balancing mechanism that aims to distribute the workload across the servers of the data center effectively. In this paper, we present a distributed and scalable load balancing mechanism for cloud computing using game theory. The mechanism is self‐organized and depends only on the local information for the load balancing. We proved that our mechanism converges and its inefficiency is bounded. Simulation results show that the generated placement of workload on servers provides an efficient, scalable, and reliable load balancing scheme for the cloud data center. Copyright © 2016 John Wiley & Sons, Ltd.

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“…It represents the ability to transfer some amount of processing to another system that will execute the request. The task of load balancing is divided into two subtasks [9]:…”

Section: Load Balancingmentioning

confidence: 99%

Energy Efficient Resource Allocation for Heterogeneous Workload in Cloud Computing

Malik

Saini

Rani

2017

Advances in Intelligent Systems and Computing

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Cloud computing is an internet based technology that provisions the resources automatically on the pay per use basis. With the development of cloud computing, the amount of customers and requirement of resources increases exponentially. In order to balance the load, the tasks must be equally distributed among multiple computing servers thereby, fulfilling Quality of Service (QoS) with maximum profit to cloud service providers. In addition, cloud servers consume huge amount of electrical energy leading to increased expenditure and environment degradation. Therefore, certain solutions are needed that results in efficient resource utilization while minimizing the environmental influence. In the paper, we present a survey of load balancing algorithms along with their limitations and propose a framework for an energy efficient resource allocation and load balancing for heterogeneous workload in cloud computing along with the validation of the framework using CloudSim toolkit.

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A survey on scheduling and load balancing techniques in cloud computing environment

Cited by 63 publications

References 22 publications

WITHDRAWN: Towards sensor-cloud integration: A survey of enabling technologies and architectures

WITHDRAWN: Towards sensor-cloud integration: A survey of enabling technologies and architectures

A self‐organized load balancing mechanism for cloud computing

Energy Efficient Resource Allocation for Heterogeneous Workload in Cloud Computing

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