Enterprise Distributed Real-time And Embedded Research Articles

Supporting SIP-based end-to-end Data Distribution Service QoS in WANs

Assuring end-to-end QoS in enterprise distributed real-time and embedded (DRE) systems is hard due to the heterogeneity and transient behavior of communication networks, the lack of integrated mechanisms that schedule communication and computing resources holistically, and the scalability limits of IP multicast in wide-area networks (WANs). This paper makes three contributions to research on overcoming these problems in the context of enterprise DRE systems that use the OMG Data Distribution Service (DDS) quality-of-service (QoS)-enabled publish/subscribe (pub/sub) middleware over WANs. First, it codifies the limitations of conventional DDS implementations deployed over WANs. Second, it describes a middleware component called Proxy DDS that bridges multiple, isolated DDS domains deployed over WANs. Third, it describes the NetQSIP framework that combines multi-layer, standards-based technologies including the OMG-DDS, Session Initiation Protocol (SIP), and IP DiffServ to support end-to-end QoS in a WAN and shield pub/sub applications from tedious and error-prone details of network QoS mechanisms. The results of experiments using Proxy DDS and NetQSIP show how combining DDS with SIP in DiffServ networks significantly improves dynamic resource reservation in WANs and provides effective end-to-end QoS management.

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

Context: Component-based middleware, such as the Lightweight CORBA 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 Locality-Enhanced 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 1000 components and in a cluster environment with up to 100 nodes. Our results show LE-DAnCE's optimizations provide a bounded deployment latency of less than 2s for the 1000 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.

Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments

Several adaptation approaches have been devised to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable, however, for the stringent accuracy, timeliness, and development complexity requirements of distributed real-time and embedded (DRE) systems. This paper empirically evaluates constant-time supervised machine learning techniques, such as artificial neural networks (ANNs) and support vector machines (SVMs), and presents a composite metric to support quantitative evaluation of accuracy and timeliness for these adaptation approaches.

Integrating Machine Learning Techniques to Adapt Protocols for QoS-enabled Distributed Real-time and Embedded Publish/Subscribe Middleware

Quality-of-service (QoS)-enabled publish/subscribe (pub/sub) middleware provides the infra-structure needed to disseminate data predictably, reliably, and scalably in distributed real-time and embedded (DRE) systems. Maintaining QoS properties as the operating environ¬ment fluctuates is challenging, however, since the chosen mechanism (e.g., transport protocol or caching algorithm for data persistence) may no longer provide the needed QoS. Moreover, some adaptation approaches are tailored for particular types of operating environments, such as environments whose configuration properties (e.g., number of data receivers or data sending rate) are known prior to runtime versus unknown until runtime. For DRE pub/sub systems operating in dynamic environments, adjustments to mechanisms must be timely, accurate for known environments, and resilient to environments unknown until runtime. Several adaptation approaches, such as policy-based [1] and reinforcement learning [2] have been developed to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable for DRE pub/sub systems, however, due to their stringent accuracy, timeliness, and development complexity requirements. Supervised machine learning techniques, such as artificial neural networks (ANNs) [3] and support vector machines (SVMs) [4], are promising approaches to address the accuracy, time complexity, and development complexity concerns of adaptive enterprise DRE systems. This article describes the results of research that (1) empirically evaluates supervised machine learning techniques used to adapt the transport protocols of QoS-enabled pub/sub middleware autonomically in a dynamic environment and (2) integrates multiple techniques to increase accuracy for environments known a priori and not known until runtime. Our results show that both ANNs and SVMs provide constant time complexity, low latency, and reduced de-velopment complexity. ANNs are generally more accurate in providing adaptation guidance for environments whose properties are known prior to runtime and provide sub-sec response times, whereas SVMs provide higher accuracy with sec latencies for environments whose properties are not known until runtime. Both approaches can be leveraged together with QoS-enabled pub/sub middleware to address the timeliness, accuracy, and development com-plexity needs of enterprise DRE systems executing in dynamic environments.

A multi-layered resource management framework for dynamic resource management in enterprise DRE systems

Enterprise distributed real-time and embedded (DRE) systems can benefit from dynamic management of computing and networking resources to optimize and reconfigure system resources at runtime in response to changing mission needs and/or other situations, such as failures or system overload. This paper provides two contributions to the study of dynamic resource management (DRM) for enterprise DRE systems. First, we describe a standards-based multi-layered resource management (ARMS MLRM) architecture that provides DRM capabilities to enterprise DRE systems. Second, we show the results of experiments evaluating our ARMS MLRM architecture in the context of a representative enterprise DRE system for shipboard computing.