A Decision-Theoretic Planner with Dynamic Component Reconfiguration for Distributed Real-Time Applications

Abstract

Middleware is increasingly being used to develop and deploy components in large-scale distributed real-time and embedded (DRE) systems, such as the proposed NASA sensor web composed of networked remote sensing satellites, atmospheric, oceanic, and terrestrial sensors. Such a system must perform sequences of autonomous coordination and heterogeneous data manipulation tasks to meet specified goals. For example, accurate weather prediction requires multiple satellites that fly coordinated missions to collect and analyze large quantities of atmospheric and earth surface data. The efficacy and utility of the task sequences are governed by dynamic factors, such as data analysis results, changing goals and priorities, and uncertainties due to changing environmental conditions. One way to implement task sequences in DRE systems is to use component middleware (Heineman & Councill 2001), which automates remoting, lifecycle management, system resource management, and deployment and configuration. In large DRE systems, the sheer number of available components often poses a combinatorial planning problem for identifying component sequences to achieve specified goals. Moreover, the dynamic nature of these systems requires runtime management and modification of deployed components.To support such DRE systems, we have developed a novel computationally efficient algorithm called the Spreading Activation Partial Order Planner (SA-POP) for dynamic (re)planning under uncertainty. Prior research (Srivastava & Kambhampati 1999) identified scaling limitations in earlier AI approaches that combine planning and resource allocation/scheduling in one computational algorithm. To address this problem, we combined SA-POP with a Resource Allocation and Control Engine (RACE), which is a reusable component middleware framework that separates resource allocation and control algorithms from the underlying middleware deployment, configuration, and control mechanisms to enforce quality of service (QoS) requirements (see http://www.dre.vanderbilt.edu/˜schmidt/WCCD.pdf for an overview of RACE). The separation of concerns between

Coordinating Aerial Robots and Unattended Ground Sensors for Intelligent Surveillance Systems

“…Schmidt et al [8] present an approach that uses artificial intelligence techniques to configure an underlying middleware. Their approach uses the concepts of missions and goals to plan the allocation of tasks in a network of homogeneous nodes.…”

An Integrated Planning and Adaptive Resource Management Architecture for Distributed Real-Time Embedded Systems

“…SA-POP provides a planning approach for dynamic generation of component-based applications that operate with limited resources in uncertain environments. The architecture of SA-POP is shown in Given one or more goals specified by a software agent or system user, SA-POP takes into account current conditions to generate partial order plans with high expected utility [3]. Goals are specified as desired conditions with associated utility values.…”

“…ning for online generation of applications, (2) planning with multiple interacting goals, (3) efficiently allocate system resources to application components, (4) avoid overutilization of system resources, thereby ensuring system stability and application QoS requirements are met, even under high load conditions.…”

“…To address the planning and adaptive resource management needs of open DRE systems, we developed the Spreading Activation Partial Order Planner (SA-POP) [3] and the Resource Allocation and Control Engine (RACE) [4], respectively. SA-POP combines decision-theoretic task planning with resource constraints for DRE systems operating in uncertain environments to produce effective executable component sequences that can achieve current mission goals, including desired QoS, with available system resources.…”

Design and Performance Evaluation of Configurable Component Middleware for End-to-End Adaptation of Distributed Real-Time Embedded Systems

“…Moreover, when applications are composed at runtime by intelligent mission planners [9], only end-toend QoS requirements of the applications are specified. What is needed, therefore, are middleware-centric capabilities for allocating resources automatically and monitoring/(re)configuring QoS settings of applications to enforce their end-to-end QoS requirements.…”