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Design and Simulation of a Green Broker with Imprecise Computation Scheduling for Energy-efficient Large Scale Computing in Clusters

Cluster computing has attracted attention for large scale computing using idle CPU cycles of personal computers connected in local area network. We propose a green broker with imprecise computation scheduling for large scale computing in clusters. Imprecise computation techniques provide scheduling flexibility by trading off result quality to meet computation deadlines. We provide a technique to enhance QoS for real-time systems and improve the energy efficiency for large scale computing in clusters with lower carbon emission. Measurements of simulation on a large number of task sets show that imprecise computation improves the system reliability when scheduling intensive workloads. With less schedule faults, CPU cycles are saved and energy-efficiency is improved for large scale computing in clusters.