A Technique Based on Ant Colony Optimization for Load Balancing in Cloud Data Center
Ekta Gupta, Vidya Deshpande
Abstract
Ekta Gupta, Vidya Deshpande
Abstract
As a large number of requests are submitted to the data center, load balancing is one of the main challenges in Cloud Data Center. Existing load Balancing techniques mainly focus on improving the quality of services, providing the expected output on time etc. Therefore, there is a need to develop load balancing technique that can improve the performance of cloud computing along with optimal resource utilization. The proposed technique of load balancing is based on Ant Colony Optimization which detects overloaded and under loaded servers and thereby performs load balancing operations between identified servers of Data Center. The proposed technique ensures availability, achieves efficient resource utilization, maximizes number of requests handled by cloud and minimizes time required to serve multiple requests. The complexity of proposed algorithm depends on datacenter network architecture.
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As a large number of requests are submitted to the data center, load balancing is one of the main challenges in Cloud Data Center. Existing load Balancing techniques mainly focus on improving the quality of services, providing the expected output on time etc. Therefore, there is a need to develop load balancing technique that can improve the performance of cloud computing along with optimal resource utilization. The proposed technique of load balancing is based on Ant Colony Optimization which detects overloaded and under loaded servers and thereby performs load balancing operations between identified servers of Data Center. The proposed technique ensures availability, achieves efficient resource utilization, maximizes number of requests handled by cloud and minimizes time required to serve multiple requests. The complexity of proposed algorithm depends on datacenter network architecture.
Key concepts: Load balancing (electrical power), Computer science, Data center, Server, Cloud computing, Ant colony optimization algorithms, Distributed computing, Load management