Adaptive data-aware utility-based scheduling in resource-constrained systems

Journal of Parallel and Distributed Computing

Volumn 70, Issue 9, 2010, Pages 871-879

  Vengerov, David ^a   Mastroleon, Lykomidis ^b   Murphy, Declan ^a   Bambos, Nick ^b  

^a SUN MICROSYSTEMS   (United States)

^b Stanford University   (United States)

Author keywords

Co evolution; Data requirement; Real time scheduling; Reinforcement learning; Utility accrual

Indexed keywords

CO-EVOLUTION; CO-EVOLUTIONARY; DATA REQUIREMENTS; DYNAMIC SCHEDULING; OPTIMAL SCHEDULING; PER UNIT; REAL TIME SCHEDULING; RESOURCE-CONSTRAINED; SCHEDULING JOBS; SCHEDULING POLICIES; SIMULATION RESULT; SOLUTION METHODOLOGY; UTILITY ACCRUALS; UTILITY FUNCTIONS; UTILITY-BASED SCHEDULING;

PROGRAM PROCESSORS; REINFORCEMENT; REINFORCEMENT LEARNING;

SCHEDULING ALGORITHMS;

EID: 77955518881     PISSN: 07437315     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jpdc.2009.08.006     Document Type: Article

References (11)

1. H.R. Berenji, D. Vengerov, J. Ametha, Co-evolutionary perception-based reinforcement learning for sensor allocation in autonomous vehicles, in: Proceedings of the 12th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE, 2003, pp. 125130
2. R.H. Crites, and A.G. Barto Elevator group control using multiple reinforcement learning agents Machine Learning 33 23 1998 235 262
3. E. Jensen, C. Locke, H. Tokuda, A time driven scheduling model for real-time operating systems, in: Proceedings of the IEEE Real-Time Systems Symposium, 1985, pp. 112122
4. T. Kosar, A new paradigm in data intensive computing: Stork and the data-aware schedulers, in: Proceedings of Challenges of Large Applications in Distributed Environments, (CLADE 2006) Workshop, Paris, France, June 2006, in conjunction with HPDC 2006, 2006, pp. 512
5. P. Li, H. Wu, B. Ravindran, and E.D. Jensen A utility accrual scheduling algorithm for real-time activities with mutual exclusion resource constraints IEEE Transactions on Computers 55 4 2006 454 469
6. J. Mitchell, Sun's hpcs architecture and technologies, in: Proceedings of High Performance Computing, Networking and Storage Conference SC2004, Pittsburgh, PA, 2004
7. B. Ravindran, E.D. Jensen, P. Li, On recent advances in time/utility function real-time scheduling and resource management, in: Proceedings of the Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing, ISORC, 2005, pp. 5560
8. R. Sutton, and A.G. Barto Reinforcement Learning: An Introduction 1998 MIT Press
9. J.N. Tsitsiklis, and B.V. Roy Average cost temporal-difference learning Automatica 18 11 1999 1799 1808
10. D. Vengerov A reinforcement learning framework for utility-based scheduling in resource-constrained systems Future Generation Computer Systems 25 7 2009 728 736
11. H. Wu, B. Ravindran, E.D. Jensen, U. Balli, Utility accrual scheduling under arbitrary time/utility functions and multi-unit resource constraints, in: Proceedings of 10th International Conference on Real-Time and Embedded Computing Systems and Applications, RTCSA, 2004, pp. 8098