Feedback control of data aggregation in sensor networks

Sensor networks have recently emerged as a new paradigm for distributed sensing and actuation. This paper describes fundamental performance trade-offs in sensor networks and the utility of simple feedback control mechanisms for distributed performance optimization. A data communication and aggregation framework is presented that manipulates the degree of data aggregation to maintain specified acceptable latency bounds on data delivery while attempting to minimize energy consumption. An analytic model is constructed to describe the relationships between timeliness, energy, and the degree of aggregation, as well as to quantify constraints that stem from real-time requirements. Feedback control is used to adapt the degree of data aggregation dynamically in response to network load conditions while meeting application deadlines. The results illustrate the usefulness of feedback control in the sensor network domain. 1

Customizing Component Middleware for Distributed Real-Time Systems with Aperiodic and Periodic Tasks

Many distributed real-time applications must handle mixed aperiodic and periodic tasks with diverse requirements. However, existing middleware lacks flexible configuration mechanisms needed to manage end-to-end timing easily for a wide range of different applications with both aperiodic and periodic tasks. The primary contribution of this work is the design, implementation and performance evaluation of the first configurable component middleware services for admission control and load balancing of aperiodic and periodic tasks in distributed real-time systems. Empirical results demonstrate the need for, and the effectiveness of, our configurable component middleware approach in supporting different applications with aperiodic and periodic tasks

Roadmap Analysis of Protein-Protein Interactions. Master\u27s Thesis, August 2007

The ability to effectively model the interaction between proteins is an important and open problem. In molecular biology it is well accepted that from sequence arises form and from form arises function but relating structure to function remains a challenge. The function of a given protein is defined by its interactions. Likewise a malfunction or a change in protein-protein interactions is a hallmark of many diseases. Many researchers are studying the mechanisms of protein-protein interactions and one of the overarching goals of the community is to predict whether two proteins will bind, and if so what the final conformation will be. Attention is seldom paid to the association pathways that allow two proteins to bind. Evidence has shown that the information in the association pathways can play a vital role in understanding the interaction itself. This thesis presents a novel and scalable approach to computing association pathways between two proteins using the Probabilistic Roadmap (PRM) framework. We will discuss the challenges in extending PRM to the domain of protein-protein interactions such as performing structural mappings in a reduced space of flexibility, and sampling high dimensional conformation spaces. We will present analysis of individual association pathways as well as methods for estimating collective properties of the energy landscape. Our results indicate that these methods can discriminate between true and false protein binding interfaces. Finally, we will present condensing methods such as pathway clustering and visualization using dimensionality reduction that can be be applied to create compact representations of the interaction space

A feasible region for meeting aperiodic end-to-end deadlines in resource pipelines

This paper generalizes the notion of utilization bounds for schedulability of aperiodic tasks to the case of distributed resource systems. In the basic model, aperiodically arriving tasks are processed by multiple stages of a resource pipeline within end-to-end deadlines. The authors consider a multi-dimensional space in which each dimension represents the instantaneous utilization of a single stage. A feasible region is derived in this space such that all tasks meet their deadlines as long as pipeline resource consumption remains within the feasible region. The feasible region is a multi-dimensional extension of the single-resource utilization bound giving rise to a bounding surface in the utilization space rather than a scalar bound. Extensions of the analysis are provided to non-independent tasks and arbitrary task graphs. We evaluate the performance of admission control using simulation, as well as demonstrate the applicability of these results to task schedulability analysis in the total ship computing environment envisioned by the US navy. Keywords: Real-time scheduling, schedulability analysis, utilization bounds, aperiodic tasks, total ship computing environment.

A Feasible Region for Meeting Aperiodic End-to-end Deadlines in Resource Pipelines

This paper generalizes the notion of utilization bounds for schedulability of aperiodic tasks to the case of distributed resource systems. In the basic model, aperiodically arriving tasks are processed by multiple stages of a resource pipeline within end-to-end deadlines. The authors consider a multi-dimensional space in which each dimension represents the instantaneous utilization of a single stage. A feasible region is derived in this space such that all tasks meet their deadlines as long as pipeline resource consumption remains within the feasible region. The feasible region is a multi-dimensional extension of the single-resource utilization bound giving rise to a bounding surface in the utilization space rather than a scalar bound. Extensions of the analysis are provided to non-independent tasks and arbitrary task graphs. We evaluate the performance of admission control using simulation, as well as demonstrate the applicability of these results to task schedulability analysis in the total ship computing environment envisioned by the US navy