Nishanth Shankaran

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

Distributed real-time embedded (DRE) systems perform sequences of coordination and heterogeneous data manipulation tasks in dynamic environments to meet specified goals. Autonomous operation of DRE systems can benefit from the integrated operation of (1) a decision-theoretic spreading activation partial order planner (SA-POP) that combines task planning and scheduling in uncertain environments with (2) a resource allocation and control engine (RACE) middleware framework that integrates multiple resource management algorithms for (re)deploying and (re)configuring task sequence components in these systems. This paper demonstrates the effectiveness of SA-POP and RACE in managing and executing mission goals for a multisatellite application. Our results show that combining planning, scheduling and resource constraints dynamically is the key to implementing autonomy in DRE systems

Design and performance evaluation of an adaptive resource management framework for distributed real-time and embedded systems

Achieving end-to-end quality of service (QoS) in distributed real-time embedded (DRE) systems require QoS support and enforcement from their underlying operating platforms that integrates many real-time capabilities, such as QoS-enabled network protocols, real-time operating system scheduling mechanisms and policies, and real-time middleware services. As standards-based quality of service (QoS) enabled component middleware automates integration and configuration activities, it is increasingly being used as a platform for developing open DRE systems that execute in environments where operational conditions, input workload, and resource availability cannot be characterized accurately a priori. Although QoS-enabled component middleware offers many desirable features, however, it historically lacked the ability to allocate resources efficiently and enable the system to adapt to fluctuations in input workload, resource availability, and operating conditions. This paper presents three contributions to research on adaptive resource management for component-based open DRE systems. First, we describe the structure and functionality of the resource allocation and control engine (RACE), which is an open-source adaptive resource management framework built atop standards-based QoS-enabled component middleware. Second, we demonstrate and evaluate the effectiveness of RACE in the context of a representative open DRE system: NASAs magnetospheric multiscale mission system. Third, we present an empirical evaluation of RACEs scalability as the number of nodes and applications in a DRE system grows. Our results show that RACE is a scalable adaptive resource management framework and yields a predictable and high-performance system, even in the face of changing operational conditions and input workload.

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

Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori and are subject to little or no change at runtime. There is an increasing demand, however, for autonomous capabilities in open distributed real-time and embedded (DRE) systems that execute in environments where input workload and resource availability cannot be accurately characterized a priori. These systems can benefit from autonomic computing capabilities, such as self-(re)configuration and self-optimization, that enable autonomous adaptation under varying-even unpredictable-operational conditions. A challenging problem faced by researchers and developers in enabling autonomic computing capabilities to open DRE systems involves devising adaptive planning and resource management strategies that can meet mission objectives and end-to-end quality of service (QoS) requirements of applications. To address this challenge, this paper presents the integrated planning, allocation, and control (IPAC) framework, which provides decision-theoretic planning, dynamic resource allocation, and runtime system control to provide coordinated system adaptation and enable the autonomous operation of open DRE systems. This paper presents two contributions to research on autonomic computing for open DRE systems. First, we describe the design of IPAC and show how IPAC resolves the challenges associated with the autonomous operation of a representative open DRE system case study. Second, we empirically evaluate the planning and adaptive resource management capabilities of IPAC in the context of our case study. Our experimental results demonstrate that IPAC enables the autonomous operation of open DRE systems by performing adaptive planning and management of system resources.

Toward Effective Multi-Capacity Resource Allocation in Distributed Real-Time and Embedded Systems

Effective resource management for distributed real-time embedded (DRE) systems is hard due to their unique characteristics, including (1) constraints in multiple resources and (2) highly fluctuating resource availability and input workload. DRE systems can benefit from a middleware framework that enables adaptive resource management algorithms to ensure application QoS requirements are met. This paper identifies key challenges in designing and extending resource allocation algorithms for DRE systems. We present an empirical study of bin-packing algorithms enhanced to meet these challenges. Our analysis identifies input application patterns that help generate appropriate heuristics for using these algorithms effectively in DRE systems.

A framework for (re)deploying components in distributed real-time and embedded systems

This paper describes the Resource Allocation and Control Engine (RACE) that integrates multiple resource management algorithms for (re) deploying and managing performance of application components in distributed real-time and embedded (DRE) systems. RACE enables DRE systems to (re) configure allocation and control algorithms depending on application characteristics and environmental conditions. It also enables developers to focus on algorithm logic, while reusing many mechanisms used to (re)configure and (re)deploy the algorithms on distributed computing nodes.

A Multi-Agent Architecture Provides Smart Sensing for the NASA Sensor Web

Remote sensing missions for Earth Science contribute greatly to the understanding of the dynamics of our planet. Conventional approaches however, impede the scientific communitys ability to (1) generate and refine models of complex phenomena, such as, extended weather forecasting, (2) detect and rapidly respond to critical transient events (e.g., disasters, such as hurricanes and floods). This paper describes a more effective approach based on intelligent, networked sensor webs that incorporate seamless dynamic connectivity between spacecraft, aircraft, and in situ terrestrial sensors, employs reactive and proactive strategies for improved temporal, spectral, and spatial coverage of the earth and its atmosphere, and uses enhanced dynamic decision-making for rapid responses to changing situations. MACRO, an extension of our earlier work on a multi-agent framework for heterogeneous spacecraft constellations, will provide interoperability and autonomy to achieve the needs for smart sensing in NASAs proposed sensor web. The system capability will be demonstrated via a simulated but salient disaster management scenario on an existing hardware testbed at the Lockheed Martin Advanced Technology Center.

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

Standards-based quality of service (QoS)-enabled component middleware is increasingly being used as a platform for developing distributed real-time embedded (DRE) systems that execute in open environments where operational conditions, input workload, and resource availability cannot be characterized accurately a priori. Although QoS-enabled component middleware offers many desirable features, until recently it lacked the ability to efficiently allocate resources and configure platform-specific QoS settings based on utilization of system resources and application QoS. Moreover, it has also lacked the ability to monitor and enforce application QoS requirements. This paper presents two contributions to research on adaptive resource management for component-based DRE systems. First, we describe the structure and functionality of the resource allocation and control engine (RACE), which is an open-source adaptive resource management framework built atop standards-based QoS-enabled component middleware. Second, we demonstrate the effectiveness of RACE in the context of a representative DRE system: NASAs magneto spheric multi-scale mission system

Bulls-Eye – a resource provisioning service for enterprise distributed real-time and embedded systems

Middleware is increasingly used to develop and deploy compo nents in enterprise distributed real-time and embed ded (DRE) systems A key chal lenge in these systems is de vising resource management algorithms that deploy appli cation components properly onto target nodes To provide an accurate view of system re source utilization, these algorithms need monitor resources at runtime Runtime resource monitoring is also needed to make redeployment or reconfigu ration decisions trig gered by various factors, such as failures, attacks, overloads, or changes in quality of service (QoS) re quirements DRE sys tems with a diverse range of applications can therefore benefit from a common re source provisioning service capable of monitoring re source data and ena bling proper resource allocation in a timely manner. This paper provides two contributions to the study of run time resource provi sioning for enterprise DRE systems First, it describes the challenges in devel oping Bulls-Eye, which is an open implementation of the OMG standard Target Manager specification that provides a reusable service for provisioning distrib uted resources in enter prise DRE systems Second, it presents the results of ex periments that applied Bulls-Eye to the multi-layer resource manage ment sub system of a ship board computing environment Our re sults show that provi sioning re sources at runtime in a DRE system via Bulls-Eye simplifies resource manage ment and helps automate adaptations in the face of dynamic changes in operat ing conditions.

Intelligent Resource Management and Dynamic Adaptation in a Distributed Real-time and Embedded Sensor Web System

Sensor webs are often composed of servers connected to distributed real-time embedded (DRE) systems that operate in open environments where operating conditions, workload, resource availability, and connectivity cannot be accurately characterized a priori. The South East Alaska MOnitoring Network for Science, Telecommunications, Education, and Research (SEAMONSTER) project exhibits many common system management and dynamic operation challenges for effective, autonomous system adaptation in a representative sensor web. These challenges cover both field operation ({\em e.g.}, power management through system sleep/wake cycles and reaction to local environmental changes) and server operation ({\em e.g.}, system adaptation for new/modified goals, resource allocation for a changing set of applications, and configuration changes for fluctuating workload). This paper presents the results of integrating and applying quality-of-service (QoS)-enabled component middleware, dynamic resource management, and autonomous agent technologies to address these challenges in SEAMONSTER.

Hierarchical control of multiple resources in distributed real-time and embedded systems

Abstract Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori, and are subject to little or no change at runtime. There is increasing demand, however, for adaptive capabilities in distributed real-time and embedded (DRE) systems that execute in open environments where system operational conditions, input workload, and resource availability cannot be characterized accurately a priori. A challenging problem faced by researchers and developers of such systems is devising effective adaptive resource management strategies that can meet end-to-end quality of service (QoS) requirements of applications. To address key resource management challenges of open DRE systems, this paper presents the Hierarchical Distributed Resource-management Architecture (HiDRA), which provides adaptive resource management using control techniques that adapt to workload fluctuations and resource availability for both bandwidth and processor utilization simultaneously. This paper presents three contributions to research in adaptive resource management for DRE systems. First, we describe the structure and functionality of HiDRA. Second, we present an analytical model of HiDRA that formalizes its control-theoretic behavior and presents analytical assurance of system performance. Third, we evaluate the performance of HiDRA via experiments on a representative DRE system that performs real-time distributed target tracking. Our analytical and empirical results indicate that HiDRA yields predictable, stable, and efficient system performance, even in the face of changing workload and resource availability.