The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
An Analysis of Swarm Intelligence based Load Balancing Algorithms in a Cloud Computing Environment
Open accessAbstract
The current advances in cloud computing layout well established research in Distributed computing, virtualization, web services, utility computing, have provided many advantages in scalability, cost and efficiency for cloud service users.These advantages are further expected to satisfy the demands of cloud users for cloud services in efficient and effective manner.This brings the issue of fault tolerance, scalability, efficiency, high availability.Central to these problems require the dynamic and efficient load balancing techniques.In this paper, we survey a special group of Swarm intelligence based load balancing algorithm and discuss the advantages and issues of these algorithms for cloud computing environment.
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Cites
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“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…”
MethodIntelligent cloud algorithms for load balancing problems: A survey
Farrag, A. A. S., Mahmoud, S. A., & El-Horbaty, E.-S. M. (2015) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/intelcis.2015.7397223 · Cited by 52
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Zavvar, M. H., Garavand, S., Sabbagh, E., Rezaei, M., Khalili, H., Zavvar, M. H., & Motameni, H. (2017) · International Conference on Web Research (ICWR) · doi:10.1109/icwr.2017.7959309 · Cited by 6
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OPTIMIZATION OF LOAD BALANCING IN CLOUD USING SWARM INTELLIGENCE: A SURVEY
Revathi, S., Rizwan, A., & Anusha, N. (2019) · International Journal of Current Engineering and Scientific Research · doi:10.21276/ijcesr.2019.6.6.29 · Cited by 1
Broker Scheduler based on ACO for Federated Cloud-based scientific experiments
Pacini, E., Mateos, C., & Garino, C. G. G. (2016) · IEEE Biennial Congress of Argentina (ARGENCON) · doi:10.1109/argencon.2016.7585239
Review paper on Various Dynamic Load Balancing Algorithms in Cloud computing environment
Parida, S. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21596599
Prediction of Web Service Execution QoS Parameters with Utilization of Machine Learning Methods
Falas, Ł., Sztukowski, A., Warzyński, A., & Schauer, P. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27470-1_13
Metadata
- Authors
- Singhal, U., & Jain, S.
- Affiliations
- Madhav Institute of Technology & Science (IN)
- ORCID
- Jain, Sanjeev
- Venue
- International Journal of Hybrid Information Technology
- Publisher
- Global Vision Press
- Published
- 2015-01-31
- Volume
- 8
- Issue
- 1
- Pages
- 249-256
- ISSN
- 1738-9968 (ISSN-L)
- OpenAlex
- W2183733357
- Cited by
- 10
- References
- 10
- Citations by year
- 2023: 1 · 2022: 1 · 2019: 1 · 2018: 1 · 2017: 2 · 2016: 1 · 2015: 3
- FWCI
- 6.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Hybrid Information Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Keywords
- swarm intelligence, load balancing, cloud computing, load balancing algorithms, fault tolerance, scalability, high availability, distributed computing
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