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

Suri, Dipa; Howell, Adam; Schmidt, Douglas C.; Biswas, Gautam; Kinnebrew, John S.; Otte, William R.; Shankaran, N.

doi:10.1109/aero.2007.352770

Cited by 13 publications

(10 citation statements)

References 3 publications

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“…We then describe how we have addressed the SEAMON-STER challenges as part of the development of the Multiagent Architecture for Coordinated Responsive Observations (MACRO) [6] platform. MACRO uses QoS-enabled component middleware to help automate many system configuration and management tasks for sensor web applications.…”

Section: Introductionmentioning

confidence: 99%

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

Kinnebrew

Otte

Shankaran

et al. 2009

2009 IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing

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Abstract-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 (e.g., power management through system sleep/wake cycles and reaction to local environmental changes) and server operation (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.

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Section: Introductionmentioning

confidence: 99%

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

Kinnebrew

Otte

Shankaran

et al. 2009

2009 IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing

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“…To evaluate the benefits of the CaDAnCE D&C framework, we applied it to a representative open DRE system prototype of the NASA MMS mission system [9]. This section summarizes our hardware/software testbed and then presents the results of using it to evaluate the effectiveness of CaDAnCE empirically.…”

Section: Resultsmentioning

confidence: 99%

CaDAnCE: A Criticality-Aware Deployment and Configuration Engine

Deng

Schmidt

Gokhale

2008

2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC)

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Predictable deployment and configuration (D&C) of components in response to dynamic environmental changes or system mode changes is essential for ensuring open distributed real-time and embedded (DRE) system real-time QoS. This paper provides three contributions to research on the predictability of D&C for component-based open DRE systems. First, we describe how the dependency relationships among different components and their criticality levels can cause deployment order inversion of tasks, which impedes deployment predictability. Second, we describe how to minimize D&C latency of mission-critical tasks with a multi-graph dependency tracing and graph recomposition algorithm called CaDAnCE. Third, we empirically evaluate the effectiveness of CaDAnCE on a representative open DRE system case study based on NASA Earth Science Enterprise's Magnetospheric Multi-Scale (MMS) mission system. Our results show that CaDAnCE avoids deployment order inversion while incurring negligible (<1%) performance overhead, thereby significantly improving D&C predictability. Keywords: Component middleware, Open DistributedReal-time and Embedded systems, Deployment and Configuration. . IntroductionEmerging Trends and Challenges. Open DRE system are often large and complex, e.g., a shipboard computing system may consist of thousands of software components that run a wide range of missions, such as ship navigation, ship structural health monitoring, vision-based object tracking and object characterization. To manage the overall complexity of such systems, the missions are often decomposed into many domain-related tasks that can be modeled as operational strings [1]. An operational string is an assembly of software components that capture the partial order and workflow of a set of executing software capabilities for particular domain tasks.In complex DRE systems, many operational strings must often be deployed and configured dynamically and simultaneously in response to system operational mode changes or environmental changes. Different operational strings can have different criticality levels, which are determined by the importance of the operational strings. In the context of D&C, criticality of an operational string means the urgency for its service startup, i.e., the latency of deploying and configuring all components in an operational string is important so the string can serve client requests.Unfortunately, when dependencies exist among operational strings, deployment order inversions may occur during the deployment and configuration (D&C) process. A deployment order inversion occurs when a high-criticality operational string is deployed after a low-criticality operational string due to one or more dependencies from the high-criticality operational string to the low-criticality operational string. Deployment order inversions are problematic for open DRE systems since they impede the predictability of the D&C process. Existing D&C frameworks [2, 3] and standards [4], however, only consider dependency relationships between oper...

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“…Component-based software engineering techniques are increasingly applied to develop enterprise distributed real-time and embedded (DRE) systems, such as air-traffic management [1], shipboard computing environments [2], and distributed sensor webs [3]. These systems are often characterized as "open" since applications running in them must contend not only with changing environmental conditions (such as changing power levels, operational nodes, or network status), but also evolving operational requirements and mission objectives [4].…”

Section: Introductionmentioning

confidence: 99%

Efficient and deterministic application deployment in component-based enterprise distributed real-time and embedded systems

Otte

Gokhale

Schmidt

2013

Information and Software Technology

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Context: Component-based middleware, such as the Lightweight COR-BA Component Model, is increasingly used to implement enterprise distributed real-time and embedded (DRE) systems. In addition to supporting the quality-of-service (QoS) requirements of individual DRE systems, component technologies must also support bounded latencies when effecting deployment changes to DRE systems in response to changing environmental conditions and operational requirements.Objective: The goals of this paper are to (1) study sources of inefficiencies and non-deterministic performance in deployment capabilities for DRE systems and (2) devise solutions to overcome these performance problems.Method: The paper makes two contributions to the study of the deployment and configuration of distributed component based applications. First, we analyze how conventional implementations of the OMG's Deployment and Configuration (D&C) specification for component-based systems can significantly degrade deployment latencies. Second, we describe architectural changes and performance optimizations implemented within the LocalityEnhanced Deployment and Configuration Engine (LE-DAnCE) implementation of the D&C specification to obtain efficient and deterministic deployment latencies.Results: We analyze the performance of LE-DAnCE in the context of component deployments on 10 nodes for a representative DRE system consisting of 1,000 components and in a cluster environment with up to 100 nodes. Our results show LE-DAnCE's optimizations provide a bounded dePreprint submitted to Information and Software Technology July 27, 2012 ployment latency of less than 2 seconds for the 1,000 component scenario with just a 4 percent jitter. Conclusion:The improvements contained in the LE-DAnCE infrastructure provide an efficient and scaleable standards-based deployment system for component-based enterprise DRE systems. In particular, deployment time parallelism can improve deployment latency significantly, both during pre-deployment analysis of the deployment plan and during the process of installing and activating components.

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

Cited by 13 publications

References 3 publications

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

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

CaDAnCE: A Criticality-Aware Deployment and Configuration Engine

Efficient and deterministic application deployment in component-based enterprise distributed real-time and embedded systems

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