Thermal-Aware and DVFS-Enabled Big Data Task Scheduling for Data Centers - 2018


Big Data has received considerable attentions lately as a result of of huge data volumes in multifarious fields. Considering various “V” options, Big Data tasks are usually highly complicated and computational intensive. These tasks are generally performed in parallel in knowledge centers resulting in huge energy consumption and Inexperienced House Gases emissions. Therefore, economical resource allocation considering the synergy of the performance and energy efficiency is one of the crucial challenges today. In this Project, we aim to achieve maximum energy efficiency by combining thermal-aware and dynamic voltage and frequency scaling (DVFS) techniques. This Project proposes: (a) a thermal-aware and power-aware hybrid energy consumption model synchronously considering the computing, cooling, and migration energy consumption; (b) a tensor-based mostly task allocation and frequency assignment model for representing the link among totally different tasks, nodes, time slots, and frequencies; and (c) a huge information Task Scheduling algorithm primarily based on Thermal-aware and DVFS-enabled techniques (TSTD) to attenuate the overall energy consumption of information centers. The experimental results demonstrate that the proposed TSTD algorithm significantly outperforms the state-of-the-art energy economical algorithms from total, computing, and cooling energy consumption perspectives, also cooling energy consumption proportion and total energy consumption savings.

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