Temperature and energy aware scheduling of heterogeneous processors using machine learning
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SubjectResearch Subject Categories::TECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering; Naive Bayesian Classifiers; Heterogeneous computing; Computer scheduling; Heterogeneous computing -- Energy consumption; TEDLS; Directed Acyclic Graph (DAG)
In the past 20-some years, the entire lifetime of Data Center, the hymn computer engineers and end users have chanted in harmony has been "faster. . .smaller. . . cheaper. . . lower power. . . ," with the most recently added "and lower temperature. . ." significantly complicating the whole scenario. The trade offs among performance, complexity, cost, power and temperature have created exciting challenges and opportunities. All modern data centers face the widespread problem "High performance without trading energy, power and most important temperature". Previous research on scheduling algorithms of processors have focused on static implementation to minimize energy consumption and heat dissipation, but never used Machine Learning to dynamically apply the algorithm. We use Naive Bayesian Classifiers (NBCs) to select the processor combination for the Temperature and Energy Aware Dynamic Level Scheduling algorithm that satisfies a particular user defined condition such as a deadline, energy or temperature budget. Our simulation results exhibit significant energy and temperature savings at a reasonable increase in overall execution time, the learning algorithm selects the desired processors significantly faster than random selection.
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