Elements of statistical computing: numerical computation
Elements of statistical computing: numerical computation
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ACM Transactions on Information Systems (TOIS)
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
CPU reservations and time constraints: efficient, predictable scheduling of independent activities
Proceedings of the sixteenth ACM symposium on Operating systems principles
MediaBench: a tool for evaluating and synthesizing multimedia and communicatons systems
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Model composition for scheduling analysis in platform design
Proceedings of the 39th annual Design Automation Conference
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Deadline Assignment in a Distributed Soft Real-Time System
IEEE Transactions on Parallel and Distributed Systems
Efficient microarchitecture modeling and path analysis for real-time software
RTSS '95 Proceedings of the 16th IEEE Real-Time Systems Symposium
Modeling Embedded Systems and SoC's: Concurrency and Time in Models of Computation
Modeling Embedded Systems and SoC's: Concurrency and Time in Models of Computation
A Survey of Context-Aware Mobile Computing Research
A Survey of Context-Aware Mobile Computing Research
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Context-aware applications pose new challenges, including a need for new computational models, uncertainty management, and efficient optimization under uncertainty. Uncertainty can arise at two levels: multiple and single tasks. When a mobile user changes environments, the context changes resulting in the possibility of the user requesting tasks which are specific for the new environment. However, as the user moves these requested tasks may no longer be context relevant. Additionally, the runtime of each task is often highly dependent on the input data. We introduce a hierarchical multi-resolution statistical task model that captures relevant aspects at the task and intertask levels, and captures not only uncertainty, but also introduces the notion of utility for the user. We have developed a system of non-parametric statistical techniques for modeling the runtime of a specific task. This model is a framework where we define problems of design and optimization of statistical soft real-time systems (SSRTS). The main algorithmic novelty is a cumulative potential-based task scheduling heuristic for maximizing utility. The heuristic conducts global optimization and induces low runtime overhead. We demonstrate the effectiveness of the scheduling heuristic using a Trimaran-based evaluation platform.