Energy aware Load Balancing Techniques for Cloud Computing

Abstract

Cloud computing, a form of distributed computing, promises to deliver reliable services through next-generation data centers, built on virtualized compute and storage technologies. Cloud providers rely on large data centers to o er resources required by users but the energy consumed by cloud infrastructures has lately become a key environment and economic concern. Lot of energy is wasted in these data centers due to the under-utilized resources and therefore these under-utilized resources should be e ciently utilized to conserve energy. Another important concern in cloud computing is load balancing. Load Balancing is essential for distributing dynamic workloads over multiple nodes so that it is ensured that no single node is overwhelmed. It thus helps in avoiding hot-spots and enhancing resource utility levels thereby, reducing energy consumption. As energy e ciency has appeared as the utmost essential design requirement for current computing systems, it determines the need of energy-aware load balancing in clouds to overcome the issue of energy-e ciency. This research work proposes two Energy-aware Load Balancing (ELB) techniques. These techniques are based on the proposed Energy-aware Resource Utilization (ERU) model and the Fire y Optimization based Energy-aware Virtual Machine Migration (FFO-EVMM) technique. The proposed ERU model e ciently manages cloud resources and enhances their utilization by allocating the jobs to the appropriate resources, using the Arti cial Bee Colony (ABC) optimization approach. It also maximizes the energy-e ciency of the cloud data centers through optimal resource usage, without degrading the system performance. Thereafter, the FFO-EVMM technique has been proposed that migrates the maximally-loaded VM to the least energy-consuming node, to reduce the consumed energy in the cloud data centers at run-time. The proposed technique intends to enhance the energy-e ciency through optimum migration of VMs, thereby improving resource utilization levels.