David Fleeman

Quality-based adaptive resource management architecture (QARMA): a CORBA resource management service

Summary form only given. We describe the quality-based adaptive resource management architecture, QARMA, a framework for resource management within CORBA. QARMA consists of three major components: the system repository service, the resource management service, and the enactor service. QARMA serves as a basis for integration of existing CORBA services and management mechanisms into a single, coherent framework for resource management. QARMA supports the management of a wide variety of applications developed using various development paradigms, easily integrates with other management and infrastructure components that already exist as CORBA services, and is easily extended to allow the use of new resource management mechanisms as they become available.

An optimization framework for dynamic, distributed real-time systems

The paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that illustrates how to use the model for producing feasible, optimal resource allocations.

Integrated CORBA scheduling and resource management for distributed real-time embedded systems

Integration of middleware scheduling and resource management services enables open distributed real-time embedded (DRE) applications to meet end-to-end quality of service (QoS) requirements in highly variable operating environments. This paper describes our research on integrating CORBA scheduling and resource management services, and presents experiments we conducted to validate and quantify the benefits of this integration. Our experimental results show that integrating distributed scheduling and resource management in middleware for open DRE systems can offer significant improvements in predictability. Specifically, integrating our stand-alone resource management service with a previously unmanaged experimental baseline application reduced the ratio of missed deadlines from 26% to 10%, and the same application performed even better under the control of integrated scheduling and resource management services, with a missed deadline ratio of only 1%.

Heuristic resource allocation algorithms for maximizing allowable workload in dynamic, distributed real-time systems

Summary form only given. We examine several heuristic algorithms for the maximum allowable workload (MAW) problem for real-time systems with tasks having variable workloads. Briefly, the problem concerns the allocation of tasks to m processors, where each task t is characterized by a function t.r(w) that gives the running time of the task in terms of its workload (or input size) w. The objective of the maximum allowable workload problem is to find an allocation of tasks to processors so that the allocation is feasible (no task misses its deadline) when each task is given a workload of w or smaller and w is maximized. This optimization problem uses a robustness measure that is closely related to the MAIL (maximum allowable increase in load) metric recently proposed by Gertphol et al. The main contribution of this paper is the comparison of several heuristic algorithms for the MAW-RMS problem. Hillclimbing, random search, simulated annealing, and first-fit heuristics are presented and evaluated via simulation. As we show here, the first-fit greedy heuristic produces solutions of a reasonable quality compared to the other algorithms. In addition, we demonstrate the applicability of our model in air defense systems.

Utility-function based resource allocation for adaptable applications in dynamic, distributed real-time systems

Summary form only given. We propose architecture and a general optimization framework for dynamic, distributed real-time systems. Interesting features of this model include the consideration of adaptive applications and utility functions. We extend by formalizing the corresponding multicriterial optimization problem. As the most difficult part of this problem, we identified the evaluation and comparison of the quality of single allocations and sets of allocations, respectively. To this end, we propose and examine metrics for measuring the goodness of solutions within our general resource management framework. These metrics lay the basis for further work on developing both online and offline algorithms to tackle the general optimization problem and provide an efficient adaptive resource manager for dynamic, distributed real-time systems.

Collaborative problem solving agent for on-board real-time systems

Breakthrough in Earth Science Observing will occur when constellations of Earth observing satellites are able to fully collaborate together and collectively monitor the conditions of our planet through a vast array of instruments. These satellites form a network that consists of distributed processes that need to respond to perceived scientific events, the spacecraft environment, spacecraft anomalies and user commands. The requests and responses exhibit dynamic behavior. In order to handle such dynamic environments, a method is needed to guarantee the real-time quality of service constraints. The DeSiDeRaTa resource management approach is being enhanced to characterize the dynamic aspects of intraconstellation topologies and to accommodate the concept of service levels and utility. This paper presents a design model of cooperative problem solving to show how the solution approach addresses the key challenges presented in the problem and specifies how the agent, resource manager and satellite constellations would operate correctly and interact in complex, dynamic and unpredictable environments. It extends the system model of DeSiDeRaTa to accommodate the concepts of utility, service levels and planning. The system model for the IPA is presented to show the proof of concept.