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Recent publications

Fuzzy Scheduling of Real-Time Ensemble Systems (HPCS2014)

P. Rattanatamrong* and J. A.B. Fortes

 

ABSTRACT: This paper addresses the problem of resource scheduling in real-time ensemble systems. An ensemble system uses multiple simple computational models (called “experts”) to produce its outputs. Real system requirements of ensemble systems (e.g., size, weight, power and cost constraints) often lead to limited availability of computational resources required to support concurrent execution of all their experts. In practical systems, uncertainties in execution time and resource utilization complicate even further the scheduling of these experts. We propose a fuzzy-logic feedback-based resource scheduler (FuzzyFES) that can provide real-time execution of all relevant experts while minimizing the impact of limited resources and uncertainties on the system performance. FuzzyFES consists of a fuzzy-logic controller (FZ), a task utilization adaptor (TUA) and a real-time task scheduler (RTS) working harmoniously in a closed loop with an ensemble system to be scheduled. By considering the uncertainties that may be present in the systems and deployment environments, FZ determines the total allowable CPU utilization for the ensemble system. TUA then calculates the amount of resource utilization to be allocated to each expert not exceeding the total allowable utilization. The assigned utilization from TUA ensures that critical experts achieve their best performance while guaranteeing minimum execution time needed by others. RTS creates a real-time schedule for the experts to execute on multiple processors according to the allotted utilization. Our performance evaluation of a case-study ensemble system with limited resources demonstrates that FuzzyFES can schedule experts to produce outputs closely similar to those of the same system with sufficient resources, although the limited-resource system has up to 40% fewer resources. The results also confirm FuzzyFES’s efficiency and show that execution-time imprecision and occasional fluctuation of resource availability can be tolerated by at least 45% more than when the experts are scheduled in an open-loop manner.

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Dynamic Scheduling of Real-Time Mixture-of-Experts Systems on Limited Resources

P. Rattanatamrong* and J. A.B. Fortes

 

ABSTRACT A Mixture-of-Experts (MoE) system generates an output in each operating cycle by combining results of multiple models (the "experts"). The contribution of any given expert to a final solution depends on a parameter called responsibility, which can vary from cycle to cycle. When resources are insufficient to run all experts, two problems arise: (1) how much utilization is to be allocated to experts and (2) how can a schedule be created based on these allocations. Problem (1) can be formulated as a succession of optimization problems, each of which calculates experts' allocations in a cycle. Explicit mappings from responsibilities to allocation weights are needed to solve each of these problems in every cycle using a technique called "task compression (TC)". We refer to this baseline approach as TT-TC. Two other proposed heuristics TT-TC* and TT-Top reduce TC's execution time to O(N) for N experts. To address (2), the proposed EPOC scheduler converts the heuristics' allocations into schedules that satisfy capacity, execution and learning constraints across cycles. Simulations demonstrate that our approaches enable real-time computation and significantly decrease the average percentage error of limited-resource outputs (i.e., 0.2-40% and 0.3-0.5% when scheduled with TT-TC* and TT-Top, respectively, versus 0.2-97% when using TT-TC).

2014

  • P. Rattanatamrong and J. A. B. Fortes, "Fuzzy Scheduling of Real-Time Ensemble Systems", accepted at the International Conference on High Performance Computing & Simulation (HPCS), Bologna, Italy, 2014.

 
2013
 
2012

  • P. Rattanatamrong and J. A. B. Fortes, Improved Real-Time Scheduling for Periodic Tasks on Multiprocessors, Concurrency and Computation Practice and Experience (Impact Factor: 0.85). 12/2012; DOI:10.1002/cpe.2969.

 
2011

  • P. Rattanatamrong and J. A. B. Fortes, “Mode Transition for Online Scheduling of Adaptive Real-Time Systems on Multiprocessors”, 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Toyama, Japan, 2011.

  • P. Rattanatamrong and J. A. B. Fortes, “Mode Transition for Online Scheduling of Adaptive Real-Time Systems on Multiprocessors”, Technical Report TR-ACIS-11-001, 2011.

  • P. Rattanatamrong and J. A. B. Fortes, “Improved Real-Time Scheduling for Periodic Tasks on Multiprocessors”, International Conference on High Performance Computing & Simulation (HPCS), Istanbul, Turkey, 2011.

  • P. Rattanatamrong, A. Matsunaga,   A. Brockmeier, J. C. Sanchez, J. Principe and J. A. B. Fortes, “Towards Closed-Loop Brain-Machine Experiments across Wide-Area Networks “, 5th International IEEE EMBS Conference on Neural Engineering, Cancun, Mexico, 2011.

 
2010

  • P. Rattanatamrong, A. Matsunaga,  J. A. B. Fortes, “BMI CyberWorkstation: a Cyberinfrastructure for Collaborative Experimental Research on Brain-Machine Interfaces”, 6th International ICST Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2010), October 2010.

  • P. Rattanatamrong, P. Raiturkar, M. Zhao, B. Mahmoudi, J. DiGiovanna, J. Principe, R. Figueiredo, J. C. Sanchez, and J. A. B. Fortes, “Model Development, Testing and Experimentation in a CyberWorkstation for Brain-Machine Interface Research”, 32nd Annual International IEEE EMBS Conference (EMBC 2010), September 2010.

  • P. Rattanatamrong, J. A. B. Fortes, “Real-time Scheduling of Mixture-of-Experts Systems with Limited Resources.”, 13th International Conference on Hybrid Systems: Computation and Control (HSCC 2010) April 12-16, 2010, Stockholm, Sweden.

 

2009

  • J. DiGiovanna, P. Rattanatamrong, M. Zhao, B. Mahmoudi, L. Hermer-Vazquez, R. Figueiredo, J. Principe, J. Fortes and J. Sanchez, Cyber-Workstation for Computational Neuroscience, Frontiers in Neuroengineering, vol. 2, no. 17, 2009.

 
2008

  • M. Zhao, P. Rattanatamrong, J. DiGiovanna, B. Mahmoudi, R. Figueiredo, J. Sanchez, J. Principe, J. A. B. Fortes, “BMI cyberworkstation: Enabling dynamic data-driven brain-machine interface research through cyberinfrastructure”, 30th Annual International IEEE EMBS Conference (EMBC 2008), September 2008.

 
2007

  • J. DiGiovanna, L. Marchal, P. Rattanatamrong, M. Zhao, S. Darmanjian, B. Mahmoudi, J. Sanchez, J. Príncipe, L. Hermer-Vazquez, R. Figueiredo and J. A. B. Fortes, “Towards Real-Time Distributed Signal Modeling for Brain Machine Interfaces,” DDDAS Workshop held jointly with the International Conference on Computational Science (ICCS 2007), Graduate University of the Chinese Academy of Sciences, Beijing, China, May 27-30, 2007.

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