The design, implementation and evaluation of SMART: a scheduler for multimedia applications
Proceedings of the sixteenth ACM symposium on Operating systems principles
Dynamic Power Management for Nonstationary Service Requests
IEEE Transactions on Computers
An Evaluation of Linear Models for Host Load Prediction
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
A Cross-Layer Approach for Power-Performance Optimization in Distributed Mobile Systems
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 10 - Volume 11
GRACE-1: Cross-Layer Adaptation for Multimedia Quality and Battery Energy
IEEE Transactions on Mobile Computing
Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Towards a general framework for cross-layer decision making in multimedia systems
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive Linear Prediction for Resource Estimation of Video Decoding
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Real time cross layer design using particle swarm optimization
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
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In our recent work, we proposed a systematic cross-layer framework for dynamic multimedia systems, which allows each layer to make autonomous and foresighted decisions that maximize the system's long-term performance, while meeting the application's real-time delay constraints. The proposed solution solved the cross-layer optimization offline, under the assumption that the multimedia system's probabilistic dynamics (e.g. the application's rate-distortion-complexity behavior) were known a priori, by modeling the system as a layered Markov decision process. In practice, however, these dynamics are unknown a priori and therefore must be learned online. In this paper, we address this problem by allowing the multimedia system layers to learn, through repeated interactions with each other, to autonomously optimize the system's long-term performance at run-time. We propose two reinforcement learning algorithms for optimizing the system under different design constraints: the first algorithm solves the cross-layer optimization in a centralized manner, and the second solves it in a decentralized manner. We analyze both algorithms in terms of their required computation, memory, and inter-layer communication overheads. In our experiments, we demonstrate that decentralized learning can perform equally as well as centralized learning, while enabling the layers to act autonomously. Additionally, we show that existing myopic learning algorithms deployed in multimedia systems perform significantly worse than our proposed foresighted learning methods.