Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: A cyber-physical approach

Qinghui Tang, Sandeep Gupta, Georgios Varsamopoulos

Research output: Contribution to journalArticlepeer-review

403 Scopus citations


High Performance Computing data centers have been rapidly growing, both in number and in size. Thermal management of data centers can address dominant problems associated with cooling such as the recirculation of hot air from the equipment outlets to their inlets, and the appearance of hot spots. In this paper, we are looking into assigning the incoming tasks to machines of a data center in such a way so as to affect the heat recirculation and make cooling more efficient. Using a low complexity linear heat recirculation model, we formulate the problem of minimizing the peak inlet temperature within a data center through task assignment, consequently leading to minimal cooling power consumption. We also provide two methods to solve the formulation, one that uses a genetic algorithm and the other that uses sequential quadratic programming. We show through formalization that minimizing the peak inlet temperature allows for the lowest cooling power needs. Results from a simulated, small-scale data center show that solving the formulation leads to an inlet temperature distribution that is 2 °C to 5 °C lower compared to other approaches, and achieves about 20 to 30% cooling energy savings at moderate data center utilization rates. Moreover, our algorithms consistently outperform MinHR, a recirculation-reducing placement algorithm in the literature.

Original languageEnglish (US)
Pages (from-to)1458-1472
Number of pages15
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number11
StatePublished - 2008


  • Energy-aware systems
  • Evaluation
  • Measurement
  • Modeling
  • Modeling techniques
  • Performance analysis and design
  • Simulation of multiple-processor systems

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


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