HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers

Reducing energy consumption in cloud data centers is one of the prime issue in the cloud community. It reduces energy related costs and increases lifespan of high performance computing resources deployed in cloud data centers and it also helps in reducing carbon emissions. Along with energy efficiency, problem of task scheduling is also one of the important problem considered in cloud data centers and it belongs to NP-class problems. With the energy consumption consideration, problem of task scheduling becomes more complex to solve. Metaheuristic algorithms are proven to generate near optimal solutions for task scheduling problem but their scheduling overhead increases vastly as the number of tasks or number of resources increases. In addition to this, metaheuristic-based scheduling algorithms lives most of time in local-optimal region of solution space and it is possible that it can finalizes the solution in the local-optimal region only. Primary motivation behind this work is from genetic algorithm itself, we have experimented with genetic algorithm in many ways to solve the problem of energy efficient task scheduling and come up with new hybrid scheme to solve it. In this work, we tackles the problem of energy efficient task scheduling on modern cloud data center architecture and proposes a novel hybrid metaheuristic scheme harmony-inspired genetic algorithm (HIGA). It also addresses issues associated with metaheuristic algorithm. HIGA combines the exploration capability of genetic algorithm and exploitation capability of harmony search by which it intelligently senses local as well as global optimal region without wasting time (iterations) in local or global optimal region and provides quick convergence. Our primary objectives in this work are to reduce makespan and computing energy and secondary objectives are to reduce the energy consumed by the resources other than computing resources and reduce execution overhead associated with scheduler. Collectively these objectives guides HIGA for better energy efficiency and performance while reducing the number of required resources (i.e. active racks). It indirectly also reduces cooling energy as we can switch off the rack components (air blower, cooling inlets) once racks become idle. Simulation analysis has been performed over independent task applications as well as real-world scientific applications like CyberShake, Epigenomics and Montage. The result clearly manifests that proposed HIGA provides by up to 33% of energy savings and 47% of improvement in application performance (makespan) that too with 39% less execution overhead. Keywords: Cloud computing, Task scheduling, Energy and rack awareness, Genetic algorithm, Harmony search, Hybrid metaheuristic

Abstract licence: CC BY-NC-ND

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