The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Particle swarm optimization based load balancing in cloud computing
Abstract
Cloud computing is a technology that facilitates tasks by allocating virtual machine (VM) dynamically. Users charge resources as they use based on their demands. There are so many challenges faced by cloud provider. One of the most challenges for him is load balancing. There are so many algorithms are available for proper load balancing but here we are focusing on particle swarm based algorithm that can balance the load in cloud computing so that resources are easily available for users. Our aim is to develop an efficient load balancing algorithm using particle swarm based to minimize performance paramaters like make span.
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In the citing paper
“There are various nature inspired algorithms like Swarm behaviour-based algorithm [90]: Ant colony optimization [91], Particle Swarm Optimization (PSO) [92], Artificial bee colony (ABC) optimization [93], modified Particle swarm optimization (MPSO) and improved Q-learning algorithm [94], Bat Algorithm [95], Crow inspired metaheuristic algorithm [96], Shuffled Frog Leaping Algorithm [97], Honey bee”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [92]
Citations
Cites
Calheiros, R. N., Ranjan, R. K., Beloglazov, A., Rose, C. A. F. D., & Buyya, R. (2010) · Software Practice and Experience · doi:10.1002/spe.995 · Cited by 4,961
A Review of Load Balancing Technique in Cloud Computing
Gupta, E. M., & Sharma, G. (2016) · International journal of advance research and innovative ideas in education · Cited by 4
Research works printed but not linked: 6
- chaczko, 0, Availibilty and load balancing in cloud computing, International Conference on Computer and Software Modeling
- kennedy, 1995, Particle Swarm Optimization IEEE International Conference on Neural Networks, 4
- acharya, 2016, An Artificial Bee Colony based Load balancing in cloud computing, National conference In-novative & Emerging Technologies
- sharm, 2016, A Particle Swarm Optimization based Technique for Scheduling Workflow in Cloud DataCenter, International Journal of Engineering Trends and Applications (I J ETA), 3
- 10.1002/spe.995 · DOI
- kaur, 2014, Load Balancing in the Cloud
Cited by
Energy efficiency in cloud computing data centers: a survey on software technologies
Katal, A., Dahiya, S., & Choudhury, T. (2023) · Cluster Computing · doi:10.1007/s10586-022-03713-0 · Cited by 516
“There are various nature inspired algorithms like Swarm behaviour-based algorithm [90]: Ant colony optimization [91], Particle Swarm Optimization (PSO) [92], Artificial bee colony (ABC) optimization [93], modified Particle swarm optimization (MPSO) and improved Q-learning algorithm [94], Bat Algorithm [95], Crow inspire…”
Method
Metadata
- Authors
- Acharya, J., Mehta, M., & Saini, B.
- Venue
- 2016 International Conference on Communication and Electronics Systems (ICCES)
- Publisher
- IEEE
- Published
- 2016-10
- Pages
- 1-4
- OpenAlex
- W2599567069
- Cited by
- 22
- References
- 2
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 2 · 2019: 4 · 2018: 3 · 2017: 2
- FWCI
- 2.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Communication and Electronics Systems (ICCES) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- load balancing, cloud computing, particle swarm optimization, virtual machine placement, makespan minimization, resource allocation
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