Cost-Aware Ant Colony Optimization for Resource Allocation in Cloud Infrastructure

Background: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm & Round Robin improve the performance but these are not cost efficient at the same time. Objective: In early proven task scheduling algorithms network cost are not included but in this proposed ACO network overhead or cost is taken into consideration which thus improves the efficiency of the algorithm as compared to the previous algorithm. Proposed algorithm aims to improve in term of cost and execution time and reduces network cost. Methods: The proposed task scheduling algorithm in cloud uses ACO with network cost and execution cost as a fitness function. This work tries to improve the existing ACO that will give improved result in terms of performance and execution cost for cloud architecture. Our study includes a comparison between various other algorithms with our proposed ACO model. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The network cost and user requests measures the performance of the proposed model. Conclusion: The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost, network cost).