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Articles 1 - 7 of 7
Full-Text Articles in Computer Engineering
Work In Progress: Automating Proportion/Period Scheduling, David Steere, Jonathan Walpole, Calton Pu
Work In Progress: Automating Proportion/Period Scheduling, David Steere, Jonathan Walpole, Calton Pu
Computer Science Faculty Publications and Presentations
The recent effort to define middleware capable of supporting real-time applications creates the opportunity to raise the level of abstraction presented to the programmer. We propose that proportion/period is a better abstraction for specifying resource needs and allocation than priorities. We are currently investigating techniques to address some issues that are restricting use of proportion/period scheduling to research real-time prototypes. In particular, we are investigating techniques to automate the task of selecting proportion and period, and that allow proportion/period to incorporate job importance under overload conditions.
Investigation Of Image Feature Extraction By A Genetic Algorithm, Steven P. Brumby, James P. Theiler, Simon J. Perkins, Neal R. Harvey, John J. Szymanski, Jeffrey J. Bloch, Melanie Mitchell
Investigation Of Image Feature Extraction By A Genetic Algorithm, Steven P. Brumby, James P. Theiler, Simon J. Perkins, Neal R. Harvey, John J. Szymanski, Jeffrey J. Bloch, Melanie Mitchell
Computer Science Faculty Publications and Presentations
We describe the implementation and performance of a genetic algorithm which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress.
Fine-Grain Period Adaptation In Soft Real-Time Environments, David Steere, Joshua Gruenberg, Dylan Mcnamee, Calton Pu, Jonathan Walpole
Fine-Grain Period Adaptation In Soft Real-Time Environments, David Steere, Joshua Gruenberg, Dylan Mcnamee, Calton Pu, Jonathan Walpole
Computer Science Faculty Publications and Presentations
Reservation-based scheduling delivers a proportion of the CPU to jobs over a period of time. In this paper we argue that automatically determining and assigning this period is both possible and useful in general purpose soft real-time environments such as personal computers and information appliances. The goal of period adaptation is to select the period over which a job is guaranteed to receive its portion of the CPU dynamically and automatically. The choice of period represents a trade-off between the amount of jitter observed by the job and the overall efficiency of the system. Secondary effects of period include quantization …
Qos Scalability For Streamed Media Delivery, Charles Krasic, Jonathan Walpole
Qos Scalability For Streamed Media Delivery, Charles Krasic, Jonathan Walpole
Computer Science Faculty Publications and Presentations
Applications with real-rate progress requirements, such as mediastreaming systems, are difficult to deploy in shared heterogenous environments such as the Internet. On the Internet, mediastreaming systems must be capable of trading off resource requirements against the quality of the media streams they deliver, in order to match wide-ranging dynamic variations in bandwidth between servers and clients. Since quality requirements tend to be user- and task-specific, mechanisms for capturing quality of service requirements and mapping them to appropriate resource-level adaptation policies are required. In this paper, we describe a general approach for automatically mapping user-level quality of service specifications onto resource …
Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson
Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson
Electrical and Computer Engineering Faculty Publications and Presentations
"Learning Hardware" approach involves creating a computational network based on feedback from the environment (for instance, positive and negative examples from the trainer), and realizing this network in an array of Field Programmable Gate Arrays (FPGAs). Computational networks can be built based on incremental supervised learning (Neural Net training) or global construction (Decision Tree design). Here we advocate the approach to Learning Hardware based on Constructive Induction methods of Machine Learning (ML) using multivalued functions. This is contrasted with the Evolvable Hardware (EHW) approach in which learning/evolution is based on the genetic algorithm only.
Adaptive Resource Management Via Modular Feedback Control, Ashvin Goel, David Steere, Calton Pu, Jonathan Walpole
Adaptive Resource Management Via Modular Feedback Control, Ashvin Goel, David Steere, Calton Pu, Jonathan Walpole
Computer Science Faculty Publications and Presentations
A key feature of tomorrow’s operating systems and runtime environments is their ability to adapt. Current state of the art uses an ad-hoc approach to building adaptive software, resulting in systems that can be complex, unpredictable and brittle. We advocate a modular and methodical approach for building adaptive system software based on feedback control. The use of feedback allows a system to automatically adapt to dynamically varying environments and loads, and allows the system designer to utilize the substantial body of knowledge in other engineering disciplines for building adaptive systems. We have developed a toolkit called SWiFT that embodies this …
Feedback Based Dynamic Proportion Allocation For Disk I/O, Dan Revel, Dylan Mcnamee, Calton Pu, David Steere, Jonathan Walpole
Feedback Based Dynamic Proportion Allocation For Disk I/O, Dan Revel, Dylan Mcnamee, Calton Pu, David Steere, Jonathan Walpole
Computer Science Faculty Publications and Presentations
In this paper we propose to use feedback control to automatically allocate disk bandwidth in order to match the rate of disk I/O to the real-rate needs of applications. We describe a model for adaptive resource management based on measuring the relative progress of stages in a producer-consumer pipeline. We show how to use prefetching to transform a passive disk into an active data producer whose progress can be controlled via feedback. Our progress-based framework allows the integrated control of multiple resources. The resulting system automatically adapts to varying application rates as well as to varying device latencies.