MrLBA: multi-resource load balancing algorithm for cloud computing using ant colony optimization

Cloud computing is a new paradigm of computing. This paradigm delivers services over the internet and eliminates requirements for local data storage. Instead of purchasing hardware and software, cloud computing enables users to use storage or applications as a service. Scheduling is the process of allocating the available resources in cloud environment. Scientific workflows consist of a large number of tasks. Workflow scheduling is a critical issue in cloud computing that targets to complete workflow execution by considering different parameters such as execution time, user deadlines, execution cost, and Quality of Service (QoS), etc. In this article, we present a Multi-resource Load Balancing Algorithm (MrLBA) cloud computing environment. The algorithm is based on Ant Colony Optimization (ACO). The proposed algorithm targets makespan, cost while keeping a well load-balanced system. The algorithm is validated with experimental results on benchmark workflows. The results show that MrLBA reduces both execution time and cost and efficiently utilizes available resources by maintaining balanced load among resources.