Affiliations: [a] Department of Computer and Communication Engineering, Manipal University Jaipur Dehmi Kalan, Near GVK Toll Plaza, Jaipur, Rajasthan, India
| [b] Department of Computer Science and Engineering, Sharda University, Greater Noida, Uttar Pradesh, India | [c] Department of Information Technology, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan, India
| [d] Department of Electronics and Communication Engineering, Amity University, Noida, Uttar Pradesh, India
Correspondence:
[*]
Corresponding author. Ankit Mundra, School of Computing and IT, Manipal University Jaipur, Rajasthan, India. E-mail: [email protected].
Abstract: Technology has enabled us to carry the world on our tips. Cloud computing has majorly contributed to this by providing infrastructure services on the go using pay per use model and with high quality of services. Cloud services provide resources through various distributed datacenters and client requests been fulfilled over these datacenters which act as resources. Therefore, resource allocation plays an important role in providing a high quality of service like utilization, network delay and finish time. Biogeography-based optimization (BBO) is an optimization algorithm that is an evolutionary algorithm used to find optimized solution. In this work BBO algorithm is been used for resource optimization problem in cloud environment at infrastructure as a service level. In past several task scheduling algorithms are being proposed to find a global best schedule to achieve least execution time and high performance like genetic algorithm, ACO and many more but as compared to GA, BBO has high probability to find global best solution. Existing solutions aim toward improving performance in term of power execution time, but they have not considered network performance and utilization of the systems performance parameters. Therefore, to improve the performance of cloud in network-aware environment we have proposed an efficient nature inspired BBO algorithm. Further, the proposed approach takes network overhead and utilization of the system into consideration to provide improved performance as compared to ACO, Genetic algorithm as well as with PSO.
