Improved Ant Colony Load Balancing Algorithm in Cloud Computing

Cloud computing mainly deals with networking, software, data access and storage services that may not require end-user knowledge of the physical location and configuration of the system that is delivering the services. In the cloud storage, load balancing is a key issue. Load balancing is one of the main challenge in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is over loaded. It helps in proper utilization of resources and hence in enhancing the performance of the system. A few existing algorithms can maintain load balancing and provide better strategies through efficient scheduling and resource allocation techniques as well. Here, we proposed an algorithm for load distribution of workloads among nodes of a cloud by the use of Improved Ant Colony Optimization (ACO). This is a modified approach of ant colony optimization that has been applied from the perspective of cloud or grid network systems with the main aim of load balancing of nodes. This modified algorithm has an edge over the original approach in which each ant build their own individual result set and it is later on built into a complete solution. Resource scheduling management design on Cloud computing is an important problem. Scheduling model, cost, quality of service, time, and conditions of the request for access to services are factors to be focused. A good task scheduler should adapt its scheduling strategy to the changing environment and load balancing Cloud task scheduling policy. Therefore, in this paper, Artificial Bee Colony (ABC) is applied to optimize the scheduling of Virtual Machine (VM) on Cloud computing. The main contribution of work is to analyze the difference of VM load balancing algorithm and to reduce the make span of data processing time. The scheduling strategy was simulated using Cloud Sim tools. Experimental results indicated that the combination of the proposed ABC algorithm, scheduling based on the size of tasks, and the Longest Job First (LJF) scheduling algorithm performed a good performance scheduling strategy in changing environment and balancing work load which can reduce the make span of data processing time.