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

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

Muhammad Aliyu, Murali M, Abdulsalam Y. Gital, Souley Boukari
Copyright: © 2020 |Pages: 21
DOI: 10.4018/IJCAC.2020040101
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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.
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2. Background

ACO is a member of the metaheuristic algorithm that follows real ant colony foraging behavior principle. Ant’s behavioral means of communication is by through pheromone (chemical substance) that enables them to find shortest path from their nest to food source. Secreted pheromone at the cause of searching their food is used for exchanging information while trailing for the shortest path. Path with the highest concentration of the substance is regarded as the shortest path. ACO has flourished in areas for solving job shop scheduling problem (Hadi and Reza, 2011), multidimensional knapsack problem (Ines et al., 2004), Traveling Salesman Problem (TSP) (Harshitha and Beena, 2017), scheduling of tasks in grid and Cloud (Tawfeeq et al., 2013), quadratic assignment problem (Rainer, 2013) and many more. The algorithm performs exigently in solving problems having to do with discrete optimization that needs to discover short routes to goals. Despite the plethora adoptions of ACO in such fields, the algorithm still needs some improvements in handling transition loops that leads to longer convergence time, initial population generation and pheromone evaporation. Best performing techniques make intensive use of its optional local search phase and proper initial solutions generation (Dorigo, 2006).

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