Efficient Metaheuristic Population-Based and Deterministic Algorithm for Resource Provisioning Using Ant Colony Optimization and Spanning Tree

Resource provisioning is the core function of cloud computing which is faced with serious challenges as demand grows. Several strategies of cloud computing resources optimization were considered by many researchers. Optimization algorithms used are still under reckoning and modification so as to enh...

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Veröffentlicht in:International journal of cloud applications and computing Jg. 10; H. 2; S. 1 - 21
Hauptverfasser: Aliyu, Muhammad, Murali M, Gital, Abdulsalam Y, Boukari, Souley
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hershey IGI Global 01.04.2020
Schlagworte:
Algorithms
Ant colony optimization
Cloud computing
Employment
Graph theory
Heuristic methods
Mathematical optimization
Optimization techniques
Pheromones
Provisioning
Resource allocation
Scheduling
Traveling salesman problem
Nigeria
ISSN:2156-1834, 2156-1826
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Zusammenfassung:Resource provisioning is the core function of cloud computing which is faced with serious challenges as demand grows. Several strategies of cloud computing resources optimization were considered by many researchers. Optimization algorithms used are still under reckoning and modification so as to enhance their potentials. As such, a dynamic scheme that can combine several algorithms' characteristics is required. Quite a number of optimization techniques have been reassessed based on metaheuristics and deterministic to map out with the challenges of resource provisioning in the Cloud. This research work proposes to involve the ant colony optimization (ACO) population-based mechanism by extending it to form a hybrid meta-heuristic through deterministic spanning tree (SPT) algorithm incorporation. Extensive experiment conducted in the cloudsim simulator provided an efficient result in terms of faster convergence, and makespan time minimization as compared to other population-based and deterministic algorithms as it significantly improves performance.
Bibliographie:ObjectType-Article-1
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ISSN:2156-1834
2156-1826
DOI:10.4018/IJCAC.2020040101