The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Load Balancing Based Task Scheduling with ACO in Cloud Computing
Abstract
Scheduling in Cloud computing infrastructure contain several challenging issues like computation time, budget, load balancing etc. Out of them, load balancing is one the major challenges for Cloud platform. Load balancing basically balances the load to achieve higher throughput and better resource utilization. Since scheduling task is NP-complete problem, so heuristic and meta heuristic approaches are preferred options to solve the same. In this paper, we chose a meta-heuristic approach of Ant colony optimization algorithm to solve the task scheduling problem in cloud environment focussing mainly on two objectives, i.e., minimizing the makespan/ computation time and better load balancing. Comparative analysis shows that proposed load balancing ant colony optimization algorithm (LB-ACO) provides better results than NSGA-II algorithm by providing better load balancing and less makespan. Simulations have been carried out using CloudSim Toolkit.
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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 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 [91]
Citations
Cites
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An Ant Colony Optimization For Dynamic Jobscheduling In Grid Environment
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A Multiobjective Resources Scheduling Approach Based on Genetic Algorithms in Grid Environment
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Multi-Objective Ant Colony Optimization for Task Scheduling in Grid Computing
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Self-citation
Research works printed but not linked: 2
- jamaludin, 2007, A Background Study on Ant Colony Optimization Metaheuristic and its Application Principles in Resolving Three Combinatorial Optimization Problem
- 10.1109/CIMA.2005.1662313 · DOI
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 inspi…”
Method
Metadata
- Authors
- Gupta, A., & Garg, R.
- Affiliations
- National Institute of Technology Kurukshetra (IN)
- Venue
- 2017 International Conference on Computer and Applications (ICCA)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 174-179
- OpenAlex
- W2766515570
- Cited by
- 88
- References
- 21
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 12 · 2023: 14 · 2022: 17 · 2021: 10 · 2020: 8 · 2019: 10 · 2018: 9
- FWCI
- 5.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer and Applications (ICCA) (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
- task scheduling, load balancing, ant colony optimization, cloud computing, makespan minimization, resource utilization, metaheuristic optimization, NSGA-II comparison, CloudSim
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.