An advanced algorithm for load balancing in cloud computing using fuzzy technique

Velde, Venkateshwarlu; Rama, B.

doi:10.1109/iccons.2017.8250624

Cited by 17 publications

(10 citation statements)

References 6 publications

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“…In our proposed method each host's CPU utilization has been estimated so as to detect whether the host is overloaded or not. In majority of the existing works [11][12][13][14][15][20][21][22], authors have applied static threshold to identify overloaded node. However, in practice, cloud infrastructure, especially IaaS undergoes dynamic load condition and therefore static threshold based overload node identification can be an optimal solution.…”

Section: Dual-level Dynamic Thresholding Assisted Host Overload Detecmentioning

confidence: 99%

“…Literatures [21] reveal that migrating VM to a suitable host or PM is a NP-hard problem, which can be significantly solved using meta-heuristics models such as Evolutionary Computing (EC). Towards this objective a few efforts have been made to use PSO [11][12][13][14][15], ACS [20][21][22], GA [16][17][18][19], HBF [23], CS [24], etc., where these approaches have been applied mainly for identifying the sub-optimal solution (i.e., suitable host). Undeniably, these approaches require certain Objective Functions (OF) to maintain optimal migration scheduling, for which CPU availability has been a most-used approach, though, in this paper we intend to use dynamic node (host) parameters such as bandwidth availability as well as CPU utilization at VM to perform migration decision (say, OF).…”

Section: Enhanced Aga Based Vm Migration For Sla-centric Load Balancingmentioning

confidence: 99%

“…To solve such issues approaches like overload detection and distributed computing approaches have been explored in a number of researches. To solve this problem numerous meta-heuristic approaches like Particle Swarm Optimization (PSO) [11][12][13][14][15], Genetic Algorithm (GA) [16][17][18][19], Ant Colony System (ACS) [20][21][22], Cuckoo Search Optimization (CSO) [24], Honey Bee Foraging (HBF) [7,10,23,25], etc., have been proposed. Undeniably, these approaches have made effort to augment VM placement policy however lacks dealing with local minima and convergence, which is predominant in major meta-heuristic approaches.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Evolutionary Computing Assisted Robust SLA-Centric Load Balancing System for Mega Cloud Data Centers

Govardhan

Srinivasan

2019

Cybernetics and Information Technologies

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Considering significance of a robust and Quality of Service (QoS) centric cloud computing, virtualization assisted load-balancing has been found a potential solution. However, assuring optimal Virtual-Machine (VM) migration with minimum violation of Service-Level-Agreement (SLA) and QoS degradation has been the challenge for academia-industries. VM allocation or scheduling being an NP-hard problem has been solved by numerous heuristic approaches such as classical Genetic Algorithm (GA), Ant Colony Optimization (ACO), etc. However they have been found confined due to local minima and convergence issues, especially for Mega Data Centres (MDCs). To alleviate such issues, in this paper an enhanced Evolutionary Computing algorithm named Adaptive Re-sampling GA (ARGA) algorithm has been developed that in conjunction with a stochastic prediction based dynamic load–measurement and Maximum Correlation (MC) assisted VM selection perform optimal load balancing over IaaS MDC infrastructures. The proposed ARGA VM allocation model with dual-level dynamic threshold assisted load estimation and MC based VM selection has exhibited lower SLA violation, performance degradation, downtime and minimum VM migration as compared to classical ACO based load balancing.

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Section: Dual-level Dynamic Thresholding Assisted Host Overload Detecmentioning

confidence: 99%

Section: Enhanced Aga Based Vm Migration For Sla-centric Load Balancingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced Evolutionary Computing Assisted Robust SLA-Centric Load Balancing System for Mega Cloud Data Centers

Govardhan

Srinivasan

2019

Cybernetics and Information Technologies

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“…IoT data are stored and processed in cloud data centres. But, cloud computing is only suitable for tasks requiring high latency and less service availability [109]. The growing IoT devices require a platform that reduces data transmission and energy consumption by cloud data centres.…”

Section: Introductionmentioning

confidence: 99%

A systematic study of load balancing approaches in the fog computing environment

Kaur

Aron

2021

J Supercomput

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Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal proportion. It has become a necessity in the fog layer to equally, and equitably distribute all the workload among the existing VMs in the segment. Till now, many load balancing techniques have been proposed for fog computing. An empirical study of existing methods in load balancing have been conducted, and taxonomy has been presented in a hierarchical form. Besides, the article contains the year-wise comprehensive review and summary of research articles published in the area of load balancing from 2013 to 2020. Furthermore, article also contains our proposed fog computing architecture to resolve load balancing problem. It also covers current issues and challenges that can be resolved in future research works. The paper concludes by providing future directions.

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“…Big data centers in clouds are provided with a dynamical load balance scheduling approach that increases network throughput and dynamically balances workload. This process implements two representative OpenFlow architectures, namely fully populated networks and fat-tree networks, which are dynamically migrated flows that require a high bandwidth in congested servers [13,14]. A stochastic load balancing scheme is used to reduce VM migration by considering the distance between source and destination physical machines [14].…”

mentioning

confidence: 99%

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

et al. 2019

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Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

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An advanced algorithm for load balancing in cloud computing using fuzzy technique

Cited by 17 publications

References 6 publications

Enhanced Evolutionary Computing Assisted Robust SLA-Centric Load Balancing System for Mega Cloud Data Centers

Enhanced Evolutionary Computing Assisted Robust SLA-Centric Load Balancing System for Mega Cloud Data Centers

A systematic study of load balancing approaches in the fog computing environment

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

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