An Upper Bound on the Loss from Approximate Optimal-Value Functions
Machine Learning
A hierarchial CPU scheduler for multimedia operating systems
OSDI '96 Proceedings of the second USENIX symposium on Operating systems design and implementation
Information and Computation
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Formal Construction of the Mathematically Analyzed Separation Kernel
ASE '00 Proceedings of the 15th IEEE international conference on Automated software engineering
HLS: A Framework for Composing Soft Real-Time Schedulers
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
Evolving real-time systems using hierarchical scheduling and concurrency analysis
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Design and Performance of Configurable Endsystem Scheduling Mechanisms
RTAS '05 Proceedings of the 11th IEEE Real Time on Embedded Technology and Applications Symposium
Reinforcement Learning for Autonomic Network Repair
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Improving host security with system call policies
SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Middleware Support for Aperiodic Tasks in Distributed Real-Time Systems
RTAS '07 Proceedings of the 13th IEEE Real Time and Embedded Technology and Applications Symposium
A secure environment for untrusted helper applications confining the Wily Hacker
SSYM'96 Proceedings of the 6th conference on USENIX Security Symposium, Focusing on Applications of Cryptography - Volume 6
Scheduling Design and Verification for Open Soft Real-Time Systems
RTSS '08 Proceedings of the 2008 Real-Time Systems Symposium
Adaptive job routing and scheduling
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
Scheduling the execution of multiple concurrent tasks on shared resources such as CPUs and network links is essential to ensuring the reliable operation of many autonomic systems. Well-known techniques such as rate-monotonic scheduling can offer rigorous timing and preemption guarantees, but only under assumptions (i.e. a fixed set of tasks with well-known execution times and invocation rates) that do not hold in many autonomic systems. New hierarchical scheduling techniques are better suited to enforce the more flexible execution constraints and enforcement mechanisms that are required for autonomic systems, but a rigorous and efficient foundation for verifying and enforcing concurrency and timing guarantees is still needed for these approaches. This paper summarises our previous work on addressing these challenges, on Markov decision process-based scheduling policy design and on wrapping repeated structure of the scheduling state spaces involved into a more efficient model, and presents a new algorithm called expanding state policy iteration (ESPI), that allows us to compute the optimal policy for a wrapped state model.