Spider Mesh Overlay for Task Load Balancing in Cloud Computing

Usurelu, C.-C.; Nita, Mihaela-Catalina; Istrate, Roxana; Pop, Florin; Țăpuș, Nicolae

doi:10.1109/iccp.2015.7312698

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Spider Mesh Overlay (SMO): Usurelu et al [99] showed the various characteristics of a novel, naturally influenced spider mesh overlay that was used for load balancing non-pre-emptive jobs, each of which consumes a substantial amount of energy. Each overlay unit can provide the highest capacity for a specific resource while providing lower capacities for other devices.…”

Section: Ssa and Its Variationsmentioning

confidence: 99%

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

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Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

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Section: Ssa and Its Variationsmentioning

confidence: 99%

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

View full text Add to dashboard Cite

show abstract

“…Catalin-Constantin Usurelu, Mihaela-Catalina Nita, Roxana Istrate, Florin Pop and Nicolae Tapus Et al. [3] They actualized a test system for the datacenter and the bug work overlay, the reproduced assets being examined from a Gaussian dispersion. As per specialist, every hub of the overlay is equipped for offering the best limit with regards to a specific asset while offering littler capacities with regards to alternate assets.…”

Section: Related Workmentioning

confidence: 99%

“…The dynamic load balancing technique adopt the heuristic and meta heuristic functions for the balancing of load in cloud environments. The meta heuristic function such as particle swarm optimization and ant colony optimization algorithm is used in many variants [3,4]. The variants of functions balance the load but faced a problem of certain limitation.…”

Section: Introductionmentioning

confidence: 99%

A Review of Task Deployment in Cloud Environment for Load Balancing using Unsupervised Learning

Hanwat¹,

Sharma²

2018

IJCA

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The task deployment and load balancing is major issue in cloud environments. The deployment of task in cloud environments used data centers. The physical host of data center overloaded due to unexpected transfer of task in environments. Due to in overloading of physical host the processing of data centers is overloaded and the efficiency of cloud environment is decreases. In this paper presents the review of task deployment and load balancing technique used different unsupervised learning algorithm for the balancing of task in cloud environments. The unsupervised learning gives the variety of algorithm such as clustering and some probability based function for the grouping of data and task. In current decade, various cloud scholar used clustering technique for the balancing of load. The clustering technique separates the cloud load in different scenario and load are distributed over other physical component of cloud environments.

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A comprehensive examination of load balancing algorithms in cloud environments: a systematic literature review, comparative analysis, taxonomy, open challenges, and future trends

Prity,

Hossain

2024

Iran J Comput Sci

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Spider Mesh Overlay for Task Load Balancing in Cloud Computing

Cited by 5 publications

References 12 publications

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

A Review of Task Deployment in Cloud Environment for Load Balancing using Unsupervised Learning

A comprehensive examination of load balancing algorithms in cloud environments: a systematic literature review, comparative analysis, taxonomy, open challenges, and future trends

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