Cloud Computing (CC) uses load balancing and scheduling in cloud infrastructure for file sharing. In CC, these two constraints must be optimized for file sharing in an optimal way. Scalable traffic management (STM) for traffic balance and service quality is developed recently in data centre. However, a challenge still remains to reduce latency during multidimensional assignment of resources. Efficient resource planning to ensure load optimization in the cloud is therefore required. In this paper, we develop an integrated algorithm for load balancing and resource planning in order to provide effective cloud services. The method creates a multidimensional resource planning model based in Glowworm Swarm Optimisation (GSO) to achieve resource planning efficiency in cloud infrastructure. A dynamically selected request gets from a class with a Multidimensional Load Optimization algorithm that increases the usage of virtual machines (VMs) in a balanced and effective load balance. A load balancing algorithm is implemented to avoid the usage of resources that can lead to increase in time of latency. Simulations are conducted to assess the efficiency of proposed model on Clouds in simulators. The results show that the proposed method has higher success rates, efficient in planning the resource and reduced response time.
Volume 11 | 08-Special Issue
Pages: 2471-2477