GTFTTS: A Generalized Tit-for-Tat Based Corporative Game for Temperature-Aware Task Scheduling in Multi-core Systems

Temperature-constraint computing environments are emerging those years, especially in embedding computing. A game theoretic temperature-aware scheduling algorithm for multi-core systems is proposed in this paper, namely, GTFTTS (Generalized Tit-For-Tat Temperature-aware Scheduling). GTFTTS is designed to work in a resource-rich environment where resources always compete for tasks. A generalized Tit-for-Tat based method, where whether a core will corporate or not is decided by a hardness factor, is considered in this paper. The algorithm is implemented in our TASS simulator. Simulations results show that the proposed game can reduce the temperature difference between different group of cores which effectively avoids the local hotspot of a processor.

[1]  Sanjay Ranka,et al.  Using game theory for scheduling tasks on multi-core processors for simultaneous optimization of performance and energy , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[2]  Rami G. Melhem,et al.  Dynamic and aggressive scheduling techniques for power-aware real-time systems , 2001, Proceedings 22nd IEEE Real-Time Systems Symposium (RTSS 2001) (Cat. No.01PR1420).

[3]  David Porter,et al.  Combinatorial auction design , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Kirk Pruhs,et al.  Dynamic speed scaling to manage energy and temperature , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[5]  Azzedine Boukerche,et al.  Web-based e-learning in 3D large scale distributed interactive simulations using HLA/RTI , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[6]  Frank Bellosa,et al.  Balancing power consumption in multiprocessor systems , 2006, EuroSys.

[7]  Jonathan A. Winter,et al.  Scheduling algorithms for unpredictably heterogeneous CMP architectures , 2008, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN).

[8]  T. N. Vijaykumar,et al.  Heat-and-run: leveraging SMT and CMP to manage power density through the operating system , 2004, ASPLOS XI.

[9]  Steve Goddard,et al.  A dynamic voltage scaling algorithm for sporadic tasks , 2003, RTSS 2003. 24th IEEE Real-Time Systems Symposium, 2003.

[10]  Marek Chrobak,et al.  Algorithms for Temperature-Aware Task Scheduling in Microprocessor Systems , 2008, AAIM.

[11]  Kevin Skadron,et al.  HotSpot: a compact thermal modeling methodology for early-stage VLSI design , 2006, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[12]  M. Rothkopf,et al.  Why Are Vickrey Auctions Rare? , 1990, Journal of Political Economy.

[13]  Vangelis Metsis,et al.  Energy-Constrained Scheduling of DAGs on Multi-core Processors , 2009, IC3.

[14]  Kevin Skadron,et al.  Temperature-aware microarchitecture: Modeling and implementation , 2004, TACO.

[15]  Yufeng Wang,et al.  Economic-Inspired Truthful Reputation Feedback Mechanism in P2P Networks , 2007, 11th IEEE International Workshop on Future Trends of Distributed Computing Systems (FTDCS'07).

[16]  Pierre Michaud ATMI manual , 2008 .

[17]  Krishnendu Chakrabarty,et al.  Real-time task scheduling for energy-aware embedded systems , 2001, J. Frankl. Inst..

[18]  Michael L. Scott,et al.  Profile-based dynamic voltage and frequency scaling for a multiple clock domain microprocessor , 2003, ISCA '03.

[19]  Yiannakis Sazeides,et al.  A study of thread migration in temperature-constrained multicores , 2007, TACO.

[20]  Nicholas R. Jennings,et al.  Market-Based Task Allocation Mechanisms for Limited-Capacity Suppliers , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[21]  Kirk Pruhs,et al.  Speed scaling to manage energy and temperature , 2007, JACM.

[22]  Fadi N. Sibai Simulation and Performance Analysis of Multi-core Thread Scheduling and Migration Algorithms , 2010, 2010 International Conference on Complex, Intelligent and Software Intensive Systems.

[23]  Shekhar Y. Borkar,et al.  Design challenges of technology scaling , 1999, IEEE Micro.

[24]  Margaret Martonosi,et al.  Techniques for Multicore Thermal Management: Classification and New Exploration , 2006, 33rd International Symposium on Computer Architecture (ISCA'06).

[25]  Krste Asanovic,et al.  Reducing power density through activity migration , 2003, ISLPED '03.

[26]  Aloysius K. Mok,et al.  An integrated approach for applying dynamic voltage scaling to hard real-time systems , 2003, The 9th IEEE Real-Time and Embedded Technology and Applications Symposium, 2003. Proceedings..

[27]  Kang G. Shin,et al.  Real-time dynamic voltage scaling for low-power embedded operating systems , 2001, SOSP.