Distributed Real-time And Embedded Research Articles

DRE system performance optimization with the SMACK cache efficiency metric

System performance improvements are critical for the resource-limited environment of multiple integrated applications executing inside a single distributed real-time and embedded (DRE) system, such as integrated avionics platform or vehtronics systems. While processor caches can effectively reduce execution time there are several factors, such as cache size, system data sharing, and task execution schedule, which make it hard to quantify, predict, and optimize the cache usage of a DRE system. This article presents SMACK, a novel heuristic for estimating the hardware cache usage of a DRE system, and describes a method of varying the runtime behavior of DRE system software without (1) requiring extensive safety recertification or (2) violating the real-time scheduling deadlines. By using SMACK as a maximization target, we were able to reduce integrated DRE system execution time by an average of 2.4% and a maximum of 4.34%.

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.

Performance Evaluation of Transport Protocol in Data Distribution Service Middleware

The DDS middleware provides powerful support for data dissemination in the distributed real-time and embedded (DRE) systems, and supports multiple transport protocol (e.g. TCP, UDP and Multicast) that affect the end-to-end quality of service (QoS) properties (e.g. latency, jitter and reliability).In order to evaluate the performance of the transport protocol and then evaluate the affection on the DDS middleware QoS, this paper first briefly compares the common DDS implementations, and then presents performance evaluation and analysis of the transport protocol in OpenDDS with different environment configurations, at last presents the conclusion.

Supporting end-to-end quality of service properties in OMG data distribution service publish/subscribe middleware over wide area networks

Assuring end-to-end quality-of-service (QoS) in distributed real-time and embedded (DRE) systems is hard due to the heterogeneity and scale of communication networks, transient behavior, and the lack of mechanisms that holistically schedule different resources end-to-end. This paper makes two contributions to research focusing on overcoming these problems in the context of wide area network (WAN)-based DRE applications that use the OMG Data Distribution Service (DDS) QoS-enabled publish/subscribe middleware. First, it provides an analytical approach to bound the delays incurred along the critical path in a typical DDS-based publish/subscribe stream, which helps ensure predictable end-to-end delays. Second, it presents the design and evaluation of a policy-driven framework called Velox. Velox combines multi-layer, standards-based technologies—including the OMG DDS and IP DiffServ—to support end-to-end QoS in heterogeneous networks and shield applications from the details of network QoS mechanisms by specifying per-flow QoS requirements. The results of empirical tests conducted using Velox show how combining DDS with DiffServ enhances the schedulability and predictability of DRE applications, improves data delivery over heterogeneous IP networks, and provides network-level differentiated performance.

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.

Infrastructure for component-based DDS application development

Enterprise distributed real-time and embedded (DRE) systems are increasingly being developed with the use of component-based software techniques. Unfortunately, commonly used component middleware platforms provide limited support for event-based publish/subscribe (pub/sub) mechanisms that meet both quality-of-service (QoS) and configurability requirements of DRE systems. On the other hand, although pub/sub technologies, such as OMG Data Distribution Service (DDS), support a wide range of QoS settings, the level of abstraction they provide make it hard to configure them due to the significant source-level configuration that must be hard-coded at compile time or tailored at run-time using proprietary, ad hoc configuration logic. Moreover, developers of applications using native pub/sub technologies must write large amounts of boilerplate "glue" code to support run-time configuration of QoS properties, which is tedious and error-prone. This paper describes a novel, generative approach that combines the strengths of QoS-enabled pub/sub middleware with component-based middleware technologies. In particular, this paper describes the design and implementation of DDS4CIAO which addresses a number of inherent and accidental complexities in the DDS4CCM standard. DDS4CIAO simplifies the development, deployment, and configuration of component-based DRE systems that leverage DDS's powerful QoS capabilities by provisioning DDS QoS policy settings and simplifying the development of DDS applications.

ScatterD

Distributed real-time and embedded (DRE) systems can be composed of hundreds of software components running across tens or hundreds of networked processors that are physically separated from one another. A key concern in DRE systems is determining the spatial deployment topology, which is how the software components map to the underlying hardware components. Optimizations, such as placing software components with high-frequency communications on processors that are closer together, can yield a number of important benefits, such as reduced power consumption due to decreased wireless transmission power required to communicate between the processing nodes. Determining a spatial deployment plan across a series of processors that will minimize power consumption is hard since the spatial deployment plan must respect a combination of real-time scheduling, fault-tolerance, resource, and other complex constraints. This article presents a hybrid heuristic/evolutionary algorithm, called ScatterD, for automatically generating spatial deployment plans that minimize power consumption. This work provides the following contributions to the study of spatial deployment optimization for power consumption minimization: (1) it combines heuristic bin-packing with an evolutionary algorithm to produce a hybrid algorithm with excellent deployment derivation capabilities and scalability, (2) it shows how a unique representation of the spatial deployment solution space integrates the heuristic and evolutionary algorithms, and (3) it analyzes the results of experiments performed with data derived from a large-scale avionics system that compares ScatterD with other automated deployment techniques. These results show that ScatterD reduces power consumption by between 6% and 240% more than standard bin-packing, genetic, and particle swarm optimization algorithms.

Evaluating distributed real-time and embedded system test correctness using system execution traces

AbstractEffective validation of distributed real-time and embedded (DRE) system quality-of-service (QoS) properties (e.g., event prioritization, latency, and throughput) requires testing system capabilities in representative execution environments. Unfortunately, evaluating the correctness of such tests is hard since it requires validating many states dispersed across many hardware and software components. To address this problem, this article presents a method called Test Execution (TE) Score for validating execution correctness of DRE system tests and empirically evaluates TE Score in the context of a representative DRE system. Results from this evaluation show that TE Score can determine the percentage correctness in test execution, and facilitate trade-off analysis of execution states, thereby increasing confidence in QoS assurance and improving test quality.

Automated software and hardware evolution analysis for distributed real-time and embedded systems

AbstractSoftware evolution is critical to extending the utility and life of distributed real-time and embedded (DRE) systems. Determining the optimal set of software and hardware components to evolve that (1) incorporate cutting-edge technology and (2) satisfy DRE system resource constraints, such as memory, power, and CPU usage is an NP-Hard problem. This article provides four contributions to evolving legacy DRE system configurations. First, we present the Software Evolution Analysis with Resources (SEAR) technique for converting legacy DRE system configurations, external resource availabilities, and candidate replacement components into multiple-choice multidimension knapsack problems (MMKP). Second, we present a formal methodology for assessing the validity of evolved system configurations. Third, we apply heuristic approximation algorithms to determine low-cost, high value evolution paths in polynomial time. Finally, we analyze results of experiments that apply these techniques to determine which technique is most effective for given system parameters. Our results show that constraint solvers can only evolve small system configurations, whereas approximation techniques are needed to evolve larger system configurations.

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 context-aware reflective middleware framework for distributed real-time and embedded systems

Context-aware reflective middleware (CARM), which supports application reconfiguration, has been an appealing technique for building distributed real-time and embedded (DRE) systems as it can adapt their behaviors to changing environments at run time. However, existing CARM frameworks impose dependence restrictions and reconfiguration overhead, which makes the reconfiguration time of these frameworks too long (normally in the range of seconds or more) to satisfy the stringent real-time requirements of DRE systems. To improve the reconfiguration efficiency for supporting DRE systems, we have designed a new CARM framework – MARCHES (Middleware for Adaptive Robust Collaborations across Heterogeneous Environments and Systems), which offers an original structure of multiple component chains to reduce local behavior change time and a novel synchronization protocol using active messages to reduce distributed behavior synchronization time. MARCHES uses a layered architecture and provides both component-level and system-level reflection to incorporate standard components, a hierarchical event notification model to evaluate contexts, and a lightweight XML-based script language to describe and manage adaptation policies. The MARCHES framework and supported applications have been implemented on PC and PDA platforms. Based on a novel theoretical model, we have analyzed the reconfiguration efficiency of MARCHES and compared it with those of peer CARM frameworks: MobiPADS and CARISMA. Quantitative empirical results show that the reconfiguration time of MARCHES is reduced from seconds to hundreds of microseconds. Evaluations demonstrate that MARCHES is robust, scalable and generates a small memory footprint, which makes it suitable for supporting DRE systems.

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.

Automating deployment planning with an aspect weaver

Deployment has emerged as a major challenge in distributed real-time and embedded (DRE) systems. Application deployment planners must integrate numerous functional and non-functional constraints, such as security and performance, to produce correct deployment plans. The numerous deployment constraints and their complex interactions make manually deducing correct/efficient deployments hard. Four contributions to the study of automated deployment processes are presented. First, it shows that a deployment planner and an aspect weaver accomplish the same abstract problem - that is, mapping items from a source set (advice or components) to items in a target set (joinpoints or nodes) according to a set of rules - and uses this abstract definition of deployment planning to automate it with an aspect weaver. Second, this paper describes how the ScatterML domain-specific aspect language incorporates complex global constraints for specifying deployment pointcuts. Third, we show how static aspect weaving problems can be reduced to a constraint satisfaction problem and a constraint solver used to deduce a correct weaving. Fourth, we show that phrasing weaving as a constraint satisfaction problem and automating deployment through a constraint solver-based weaver yields several key benefits, ranging from guaranteed deployment plan correctness to bounds on worst-case solution quality.

Model driven middleware: A new paradigm for developing distributed real-time and embedded systems

Distributed real-time and embedded (DRE) systems have become critical in domains such as avionics ( e.g., flight mission computers), telecommunications ( e.g., wireless phone services), tele-medicine ( e.g., robotic surgery), and defense applications ( e.g., total ship computing environments). These types of system are increasingly interconnected via wireless and wireline networks to form systems of systems. A challenging requirement for these DRE systems involves supporting a diverse set of quality of service (QoS) properties, such as predictable latency/jitter, throughput guarantees, scalability, 24x7 availability, dependability, and security that must be satisfied simultaneously in real-time. Although increasing portions of DRE systems are based on QoS-enabled commercial-off-the-shelf (COTS) hardware and software components, the complexity of managing long lifecycles (often ∼15–30 years) remains a key challenge for DRE developers and system integrators. For example, substantial time and effort is spent retrofitting DRE applications when the underlying COTS technology infrastructure changes. This paper provides two contributions that help improve the development, validation, and integration of DRE systems throughout their lifecycles. First, we illustrate the challenges in creating and deploying QoS-enabled component middleware-based DRE applications and describe our approach to resolving these challenges based on a new software paradigm called Model Driven Middleware (MDM), which combines model-based software development techniques with QoS-enabled component middleware to address key challenges faced by developers of DRE systems — particularly composition, integration, and assured QoS for end-to-end operations. Second, we describe the structure and functionality of CoSMIC (Component Synthesis using Model Integrated Computing), which is an MDM toolsuite that addresses key DRE application and middleware lifecycle challenges, including partitioning the components to use distributed resources effectively, validating software configurations, assuring multiple simultaneous QoS properties in real-time, and safeguarding against rapidly changing technology.

FCS/nORB: A feedback control real-time scheduling service for embedded ORB middleware

Object Request Broker (ORB) middleware has shown promise in meeting the functional and real-time performance requirements of distributed real-time and embedded (DRE) systems. However, existing real-time ORB middleware standards such as RT-CORBA do not adequately address the challenges of (1) managing unpredictable workload, and (2) providing robust performance guarantees portably across different platforms. To overcome this limitation, we have developed software called FCS/nORB that integrates a Feedback Control real-time Scheduling (FCS) service with the nORB small-footprint real-time ORB designed for networked embedded systems. FCS/nORB features feedback control loops that provide real-time performance guarantees by automatically adjusting the rate of remote method invocations transparently to an application. FCS/nORB thus enables real-time applications to be truly portable in terms of real-time performance as well as functionality, without the need for hand tuning. This paper presents the design, implementation, and empirical evaluation of FCS/nORB. Our extensive experiments on a Linux testbed demonstrate that FCS/nORB can provide deadline miss ratio and utilization guarantees in the face of changes in platform and task execution times, while introducing only a small amount of overhead.

A Petri Net based Method for Analyzing Schedulability of Distributed Real-time Embedded Systems

As computer systems become increasingly internetworked, a challenging problem faced by researchers and developers of distributed real-time and embedded (DRE) systems is devising and implementing an effective shedulability strategy that can meet real-time requirements in varying operational conditions. In this paper, an extended Place-timed Petri nets (EPdPN) is proposed for schedulability analysis in DRE systems. First, we can capture important features of DRE systems and describe them by the semantic model. Second, the key component in DRE systems such as task, the relations between task, communication between module and resource et al. can be modeled by using EPdPN. Third, we present the concept of greatest concurrent set and convert schedulability problem into the analysis of state graph by using proposed algorithm, which can work out the feasible solution of scheduling in DRE systems. Finally, a specific example is given to simulate analytical process by using EPdPN, the results show that the method can be a good solution to analyze the schedulability of DRE systems.

Automated Configuration of Component-based Distributed Real-time and Embedded Systems from Feature Models

Component-based distributed real-time and embedded (DRE) systems facilitate the reuse of software artifacts across applications. To achieve a high-level of reuse, component-based DRE systems leverage late binding to allow dynamic system assembly at deployment time (e.g., via configuration scripts) rather than statically at compile-time. The complexity of deriving a correct manual configuration of an arbitrary set of components, however, is a key source of system failures, downtime, and missed deadlines. This paper presents a model-driven engineering tool called Fresh that uses (1) feature models to codify the configuration rules of components, (2) a constraint solver to derive a correct application configuration, and (3) an XML annotation engine to inject configuration decisions from the constraint solver directly into an application configuration. We use an avionics mission computing case study based on the Lightweight CORBA Component Model (CCM) to (1) demonstrate the complexities of configuring components in/out of a component-based application and (2) motivate the reduction in the configuration complexity when Fresh is used. The results show that Fresh achieves ~80-90% reduction in manual configuration effort, while also ensuring that the derived configuration is correct with respect to application configuration constraints and QoS requirements.

Hierarchical control of multiple resources in distributed real-time and 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 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.

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.