A review on dynamic load balancing algorithms

Cloud computing is now a widely used technology. Computing resources demand has increased as the demand for cloud is rising. Cloud computing enables the use of shared resources present on the internet. It allows clients to access their data and computing resources whenever and wherever they need. Load balancing has been playing an important role in the effective usage of cloud computing resources. As levels of tasks and the dynamic nature of cloud resources are increasing, load balancing is becoming a consequential issue. This paper reviews some of the current dynamic load-balancing approaches in a cloud environment which are nature inspired. The discussed algorithms are the BAT algorithm, Artificial Bee Colony (ABC) algorithm, Genetic algorithm (GA), Ant Colony Optimization (ACO) algorithm and Dragonfly Optimization algorithm (DOA). The main objective of these approaches is resource utilization, reduction in response time, better makespan time, and finding the optimal solution in a heterogeneous cloud environment. An analysis of the objectives, advantages, and disadvantages of discussed algorithms is presented.