An efficient load balancing system using adaptive dragonfly algorithm in cloud computing

Neelima, P.; Reddy, A. Rama Mohan

doi:10.1007/s10586-020-03054-w

Cited by 74 publications

(66 citation statements)

References 15 publications

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“…Three variations of ALO and GWO were presented to be used as Task Schedulers to reduce the makespan. Inspired by the dynamic and static swarming behaviours of artificial dragonflies in nature Neelima and Reddy [42] John proposed an efficient load balancing system using a commutable dragonfly algorithm (DFA) in cloud. An efficient multi-objective-based LB approach is developed to overcome the problem of unbalanced load, which leads to maximum time and cost.…”

Section: General Load Balancing Techniquesmentioning

confidence: 99%

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

2021

IJATCSE

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The Internet has become a fundamental necessity of daily activity. It has a more significant impact on modernizing the digital world. Therefore, cloud computing has been one of the most promising technological advances in recent days. It aims to provide millions of consumers around the globe with smooth computing services. In workload distributions and system behavior, the cloud environment is extremely dynamic, leading to load imbalances across data center resources. Balancing load across the systems is a crucial component of cloud computing, avoiding some nodes' overloading while others have little or no work to do. The problem of load balancing in the cloud has been linked to a large amount of research, recognizing its role and impact. This paper bestows a thorough investigation of the new load balancing algorithms for the cloud environment. This study's primary focus is the concept of load balancing, a literature review on load balancing and task scheduling techniques, and different measurement parameters

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Section: General Load Balancing Techniquesmentioning

confidence: 99%

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

2021

IJATCSE

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“…An efficient load balancing system using an adaptive dragonfly algorithm in cloud computing was demonstrated by Neelima et al (2019). Execution cost, execution time and makespan were the simulation measures employed for evaluation.…”

Section: Previous Literature Reviewsmentioning

confidence: 99%

Optimal Load Balancing in Cloud using ANFIS and MGWO based Polynomial Neural Network

Chourasia

Silakari

2021

Preprint

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In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. Here providing an appropriate service is considered a major challenge in the heterogeneous environment. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. In order to provide optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. In addition to this, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. In addition to this, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

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“…The authors in References 8,9 proposed the deadline constraint scheduler which was designed to satisfy the user's constraint in terms of latency. This scheduler aimed to provide the users an immediate feedback on whether their jobs would be completed within the given deadline or not and proceed with the execution if the deadline can be met.…”

Section: Related Workmentioning

confidence: 99%

“…Cloud computing is a paradigm that offers an on‐demand service represented by applications, hardware as a pool of IT resource and system software delivered over the Internet. Those service models can be classified as Software‐as‐a‐Service (SaaS), Platform‐as‐a‐Service (PaaS), and Infrastructure‐as‐a‐Service (IaaS) 8 …”

Section: Cloud Computingmentioning

confidence: 99%

Multicellular 4G and load balancing over cloud computing

Hamd

Yahiya

Kirci

2021

Concurrency and Computation

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Summary LTE‐based Multicellular 4G is nowadays gaining an enormous success thanks to its flexible core network features and its large data rate. As the number of users increases, along with the different services provided by the telcos, it is becoming a challenging issue to cope with the contextual increase in the backhaul traffic size: a congestion problem. In this perspective, when combining multicellular LTE with Cloud Computing (CC), this would increase the core network load and the subsequent load on the servers located in the CC that delivers services to the LTE users. To address this issue, we introduce an architecture combining both multicellular LTE and CC, and we investigate the performance of different load balancer mechanisms, namely static and semi‐dynamic. Extensive simulation scenarios using different algorithms were set up. The obtained results were analyzed considering the Quality of Service (QoS) and computational resources for a deployed Web application.

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An efficient load balancing system using adaptive dragonfly algorithm in cloud computing

Cited by 74 publications

References 15 publications

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

Optimal Load Balancing in Cloud using ANFIS and MGWO based Polynomial Neural Network

Multicellular 4G and load balancing over cloud computing

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