This project addresses the challenges in developing predictive and autonomic thermal-aware and energy-efficient task scheduling algorithms for heterogeneous High-Performance Computing datacenters. Energy savings of software controlled job management in large-scale datacenters has not been adequately explored. Existing scheduling and power management algorithms are mostly reactive in nature. This research will investigate the trade-off between the schedule efficacy and the runtime complexity of producing a spatio-temporal task schedule (with start times of each task and its computing node assignment or placement), with an objective to develop algorithms that can be applied in real-world situations. Novelty of this research lies in viewing datacenters as a cyber-physical entity and proactively configuring and controlling both the cooling and computing systems in a coordinated manner. Previous research results show that energy savings are very limited unless the cooling is dynamically adjusted to the computing load. Outcomes of this research will include: a) study of the trade-off between computational complexity and efficiency of the scheduling algorithm, b) proactive algorithms that produce task, power and cooling control schedules in an unified manner, c) software architecture that will encompass the power control of the computing equipment, as well as dynamic configuration of the cooling systems, and d) test results from real-world data centers that show the applicability and practicality of the developed schemes. Results will be publicly available at IMAPCT Lab's website (http://impact.asu.edu/), and scientific findings will be included in multi-disciplinary courses with cyber-physical focus. Practical benefits of this research will be demonstrated in academic and corporate datacenters.
