QoS-Based Middleware Architecture for Distributed Control Systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-85863-8_70This paper presents an implementation of a middleware architecture to control distributed systems. The main objective is providing a QoS level between the communications layer and the control layer. This architecture is based on the use of a hierarchical communications structure called logical namespace tree and a structured set of control processes interconnected, called logical sensors graph . This architecture is named Frame Sensor Adapter Control (FSA-Ctrl). In this architecture communication layer and control layer can manage the QoS policies. The communication layer is based on the Data Distribution Service (DDS), a standard proposed by Object Management Group (OMG). Control layer is derived from the Sensor Web Enablement (SWE) model proposed by Open Geospatial Consortium (OGC). Middleware components use messages queues to manage components QoS requirements. By means of QoS policies, control components can take important decisions about distributed questions, like components mobility or information redundancy detection.The architecture described in this article is a part of the coordinated project KERTROL: Kernel control on embedded system strongly connected. Education and Science Department, Spanish Government. CICYT: DPI2005-09327-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2009). QoS-Based Middleware Architecture for Distributed Control Systems. En International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Springer. 587-595. https://doi.org/10.1007/978-3-540-85863-8_70S587595Matteucci, M.: Publish/Subscribe Middleware for Robotics: Requirements and State of the Art. Technical Report N 2003.3, Politecnico di Milano, Milano, Italy (2003)OMG. Data Distribution Service for Real-Time Systems, v1.1. Document formal/2005-12-04 (2005)Botts, M., Percivall, G., Reed, C., Davidson, J. (eds.): OGC. Sensor Web Enablement: Overview and High Level Architecture. OGC White Paper. OGC 06-050r2 (2006)Coulouris, G., Dollimore, J., Kindberg, T.: Distributed systems, concepts and design, 3rd edn. Addison-Wesley, Reading (2001)OMG. Real-Time Corba Specification version 1.1. Document formal /02-08-02 (2002)FIPA. Specfication. Part 2, Agent Communication Language. Foundation for Intelligent Physical Agents (1997)Hapner, M., Sharma, R., Fialli, J., Stout, K.: JMS specification, vol. 1.1. Sun Microsystems Inc., Santa Clara (2002)Pardo-Castellote, G.: OMG Data-Distribution Service: architectural overview. In: Proceedings of 23rd International Conference on Distributed Computing Systems Workshops, Providence, USA, vol. 19-22, pp. 200–206 (2003)Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multimedia and QoS: A Survey. IEEE Multimedia 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet. IETF Internet Draft, pp. 1–37 (1998)Botts, M., Percivall, G., Reed, C., Davidson, J.: OGC. Sensor Web Enablement: Overview and High Level Architecture. OpenGIS Consortium Inc. (2006)Posadas, J.L., Perez, P., Simo, J.E., Benet, G., Blanes, F.: Communication structure for sensory data in mobile robots. Engineering Applications of Artificial Intelligence 15(3-4), 341–350 (2002)Poza, J.L., Posadas, J.L., Simó, J.E., Benet, G.: Hierarchical communication system to manage maps in mobile robot navigation. In: Proceedings of International Conference on Automation, Control and Instrumentation, Valencia, Spain (2006)Poza, J.L., Posadas, J.L., Simó, J.E.: Distributed agent specification to an Intelligent Control Architecture. In: 6th International Workshop on Practical Applications of Agents and Multiagent Systems, Salamanca, Spain (in press, 2007

From the Queue to the Quality of Service Policy: A Middleware Implementation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02481-8_61Quality of service policies in communications is one of the current trends in distributed systems based on middleware technology. To implement the QoS policies it is necessary to define some common parameters. The aim of the QoS policies is to optimize the user defined QoS parameters. This article describes how to obtain the common QoS parameters using message queues for the communications and control components of communication. The paper introduces the Queue-based Quality of Service Cycle concept for each middleware component. The QoS parameters are obtained directly from the queue parameters, and Quality of Service Policies controls directly the message queues to obtain the user-defined parameters values.The middleware architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2009). From the Queue to the Quality of Service Policy: A Middleware Implementation. En Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. Springer Verlag (Germany). 432-437. doi:10.1007/978-3-642-02481-8_61S432437Aurrecoechea, C., Campbell, A.T., Hauw, L.: A Survey of QoS Architectures. Multimedia Systems Journal, Special Issue on QoS Architecture 6(3), 138–151 (1998)OMG. Data Distribution Service for Real-Time Systems, v1.1. Document formal/2005-12-04 (December 2005)Botts, M., Percivall, G., Reed, C., Davidson, J.: OGC®. Sensor Web Enablement: Overview And High Level Architecture, OpenGIS Consortium Inc (2006)Poza, J.L., Posadas, J.I., Simó, J.E.: QoS-based middleware architecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence, Salamanca (2008)Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multi-media and QoS: A Survey 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet, pp. 1–37, XP002219363 (August 1998)ITU-T Recommendation E.800 (0894). Terms and Definitions Related to Quality of Service and Network Performance Including Dependability (1994)Stuck, B.W., Arthurs, E.: A Computer & Communications Network Performance Analysis Primer. Prentice Hall, Englewood Cliffs (1984)Jain, R.: The art of Computer Systems Performance Analysis. John Wiley & Sons Inc., New york (1991)Coulouris, G., Dollimore, J., Kindberg, T.: Distributed Systems. Concepts and Design, 3rd edn. Addison Wesley, Madrid (2001)Jung, J.-l.: Quality of Service in Telecommunications Part II: Translation of QoS Pa-rameters into ATM Performance Parameters in B-ISDN. IEEE Comm. Mag., pp. 112–117 (August 1996)Wohlstadter, E., Tai, S., Mikalsen, T., Rouvellou, I., Devanbu, P.: GlueQoS: Middleware to Sweeten Quality-of-Service Policy Interactions. In: ICSE, 26th International Conference on Software Engineering (ICSE 2004) (2004

Distributed sensor architecture for intelligent control that supports quality of control and quality of service

This paper is part of a study of intelligent architectures for distributed control and communications systems. The study focuses on optimizing control systems by evaluating the performance of middleware through quality of service (QoS) parameters and the optimization of control using Quality of Control (QoC) parameters. The main aim of this work is to study, design, develop, and evaluate a distributed control architecture based on the Data-Distribution Service for Real-Time Systems (DDS) communication standard as proposed by the Object Management Group (OMG). As a result of the study, an architecture called Frame-Sensor-Adapter to Control (FSACtrl) has been developed. FSACtrl provides a model to implement an intelligent distributed Event-Based Control (EBC) system with support to measure QoS and QoC parameters. The novelty consists of using, simultaneously, the measured QoS and QoC parameters to make decisions about the control action with a new method called Event Based Quality Integral Cycle. To validate the architecture, the first five Braitenberg vehicles have been implemented using the FSACtrl architecture. The experimental outcomes, demonstrate the convenience of using jointly QoS and QoC parameters in distributed control systems.The study described in this paper is a part of the coordinated project COBAMI: Mission-based Hierarchical Control. Education and Science Department Spanish Government. CICYT: MICINN: DPI2011-28507-C02-01/02 and project "Real time distributed control systems" of the Support Program for Research and Development 2012 UPV (PAID-06-12).Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Simarro Fernández, R.; Benet Gilabert, G. (2015). Distributed sensor architecture for intelligent control that supports quality of control and quality of service. Sensors. 15(3):4700-4733. https://doi.org/10.3390/s150304700S4700473315

Relationship between quality of control and quality of service in mobile robot navigation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-28765-7_67This article presents the experimental work developed to test the viability and to measure the efficiency of an intelligent control distributed architecture. To do this, a simulated navigation scenario of Braitenberg vehicles has been developed. To test the efficiency, the architecture uses the performance as QoS parameter. The measuring of the quality of the navigation is done through the ITAE QoC parameter. Tested scenarios are: an environment without QoS and QoC man-aging, an environment with a relevant message filtering and an environment with a predictive filtering by the type of control. The results obtained show that some of the processing performed in the control nodes can be moved to the middleware to optimize the robot navigation.The work described in this article is a part of the coordinated project SIDIRELI: (Distributed Systems with Limited Resources) and COBAMI (Mission-Based Control) Education and Science Department, Spanish Government and European FEDER found. MICINN CICYT: SIDIRELI: DPI2008-06737-C02-01/02, COBAMI: DPI2011-28507-C02-02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2012). Relationship between quality of control and quality of service in mobile robot navigation. En Distributed Computing and Artificial Intelligence: 9th International Conference. Springer. 557-564. https://doi.org/10.1007/978-3-642-28765-7_67S557564Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multimedia and QoS: A Survey. IEEE Multimedia 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet. IETF Internet Draft, 1–37 (1998)Bradner, S.: RFC 2026: The Internet Standards Process. IETF Internet Draft, sec.10 (1996)Object Management Group (OMG): Data Distribution Service for Real-Time Systems, v1.1. Document formal (April 12, 2005)Poza, J.L., Posadas, J.L., Simó, J.E.: QoS-based middleware architecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence. DCAI, Salamanca, Spain (2008)Poza, J.L., Posadas, J.L., Simó, J.E.: A Survey on Quality of Service Support on Middleware-Based Distributed Messaging Systems Used in Multi Agent Systems. In: 9th International Conference on Practical Applications of Agents and Multi-Agent Systems. DCAI, Salamanca, Spain (2011)Dorf, R.C., Bishop, R.H.: Modern Control Systems, 11th edn. Prentice Hall (2008)Soucek, S., Sauter, T.: Quality of Service Concerns in IPBased Control Systems. IEEE Transactions on Industrial Electronics 51(6) (December 2004)Poza, J.L., Posadas, J.L., Simó, J.E.: Multi-Agent Architecture with Support to Quality of Service and Quality of Control. In: 11th International Conference on Intelligent Data Engineering and Automated Learning, Paisley, UK (2010)Braitenberg, V.: Vehicles: Experiments on Synthetic Psychology. MIT Press, Cambridge (1984)Gabel, O., Litz, L.: QoS-adaptive Control in NCS with Variable Delays and Packet Losses – A Heuristic Approach. In: 43rd IEEE Conference on Decision and Control (2004)Poza, J.L., Posadas, J.L., Simó, J.E.: From the Queue to the Quality of Service Policy: A Middleware Implementation. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009, Part II. LNCS, vol. 5518, pp. 432–437. Springer, Heidelberg (2009

Adding an ontology to a standardized QoS-based MAS middleware

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02481-8_12In a Multi-Agent system, middleware is one of the components used to isolate control and communications. The use of standards in the implementation of an intelligent distributed system is always advantageous. This paper presents a middleware that provides support to a multi-agent system. Middleware is based on the standard Data Distribution Services (DDS), proposed by Object Management Group (OGM). Middleware organizes information by tree based ontology and provides a set of quality of service policies that agents can use to increase efficiency. DDS provides a set of quality of service policy. Joining quality of service policy and the ontology allows getting many advantages, among others the possibility of to conceal some details of the communications system to agents, the correct location of the agents in the distributed system, or the monitoring agents in terms of quality of service. For modeling the middleware architecture it has used UML class diagrams. As an example it has presented the implementation of a mobile robot navigation system through agents that model behaviors.The MAS architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2009). Adding an ontology to a standardized QoS-based MAS middleware. En Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. Springer. 83-90. doi:10.1007/978-3-642-02481-8_12S8390Coulouris, G., Dollimore, J., Kindberg, T.: Distributed systems, concepts and design, 3rd edn. Addison Wesley, Reading (2001)Hapner, M., Sharma, R., Fialli, J., Stout, K.: JMS specification, vol. 1.1. Sun Microsystems Inc., Santa Clara (2002)Lewis, R.: Advanced Messaging Applications with MSMQ and MQ Series. Que Publishing (1999)OMG. Real-Time Corba Specification version 1.1. Document formal /02-08-02 (2002)FIPA. Specfication. Part 2, Agent Communication Language. Foundation for Intelligent Physical Agents (1997)Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multimedia and QoS: A Survey. IEEE Multimedia 2(2), 10–19 (1995)Smith, B.: Beyond concepts, or: Ontology as reality representation. In: Formal Ontology in Information Systems (FOIS 2004), pp. 73–84 (2004)Gruber, T.R.: Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal Human-Computer Studies 43(5-6), 907–928 (1995)Pardo-Castellote, G.: OMG Data-Distribution Service: architectural overview. In: Proceedings of 23rd International Conference on Distributed Computing Systems Workshops, Providence, USA, vols. 19-22, pp. 200–206 (2003)Object Management Group (OMG). Unified Modeling Language Specification, v1.4.2, ISO/IEC 19501 (2001)Poza, J.L., Posadas, J.I., Simó, J.E.: Distributed agent specification to an Intelligent Control Architecture. In: 6th International Workshop on Practical Applications of Agents and Multiagent Systems, Salamanca (2007)Poza, J.L., Posadas, J.l., Simó, J.E.: QoS-based middleware archi-tecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence, Salamanca (2008

Multi-Agent Architecture with Support to Quality of Service and Quality of Control

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-15381-5_17Multi Agent Systems (MAS) are one of the most suitable frameworks for the implementation of intelligent distributed control system. Agents provide suitable flexibility to give support to implied heterogeneity in cyber-physical systems. Quality of Service (QoS) and Quality of Control (QoC) parameters are commonly utilized to evaluate the efficiency of the communications and the control loop. Agents can use the quality measures to take a wide range of decisions, like suitable placement on the control node or to change the workload to save energy. This article describes the architecture of a multi agent system that provides support to QoS and QoC parameters to optimize de system. The architecture uses a Publish-Subscriber model, based on Data Distribution Service (DDS) to send the control messages. Due to the nature of the Publish-Subscribe model, the architecture is suitable to implement event-based control (EBC) systems. The architecture has been called FSACtrlThe architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government and FEDER funds.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2010). Multi-Agent Architecture with Support to Quality of Service and Quality of Control. En Intelligent Data Engineering and Automated Learning – IDEAL 2010. Springer Verlag (Germany). 137-144. doi:10.1007/978-3-642-15381-5_17S137144Lee, E.A.: Cyber Physical Systems: Design Challenges. In: 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing, pp. 363–369 (2008)Siegel, J.: CORBA 3: Fundamentals and Programming. OMG (2000)FIPA. FIPA-QoS (2002), http://www.fipa.org/specs/fipa00094Object Management Group (OMG): Data Distribution Service for Real-Time Systems, v1.1. Document formal (2005-12-04)Posadas, J.L., Poza, J.L., Simó, J.E., Benet, G., Blanes, F.: Agent Based Distributed Architecture for Mobile Robot Control. Engineering Applications of Artificial Intelligence 21(6), 805–823 (2008)Aurrecoechea, C., Campbell, A.T., Hauw, L.: A Survey of QoS Architectures. Multimedia Systems Journal, Special Issue on QoS Architecture 6(3), 138–151 (1998)Pardo-Castellote, G.: OMG Data-Distribution Service: architectural overview. In: Proceedings of 23rd International Conference on Distributed Computing Systems Workshops, Providence, USA, vol. 19(22), pp. 200–206 (2003)International Telecommunication Union (ITU). Terms and Definitions Related to Quality of Service and Network Performance Including Dependability. ITU-T Recommendation E.800 (0894) (1994)Sánchez, J., Guarnes, M.Á., Dormido, S.: On the Application of Different Event-Based Sampling Strategies to the Control of a Simple Industrial Process. Sensors 9, 6795–6818 (2009)Dorf, R.C., Bishop, R.H.: Modern Control Systems, 11th edn. Prentice Hall, Englewood Cliffs (2008)Poza, J.L., Posadas, J.L., Simó, J.E.: Middleware with QoS Support to Control Intelligent Systems. In: 2th International Conference on Advanced Engineering Computing and Applications in Sciences, ADVCOMP, pp. 211–216 (2008)Poza, J.L., Posadas, J.L., Simó, J.E.: From the Queue to the Quality of Service Policy: A Middleware Implementation. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 432–437. Springer, Heidelberg (2009

A QoS-Control Architecture for Object Middleware

This paper presents an architecture for QoS-aware middleware platforms. We present a general framework for control, and specialise this framework for QoS provisioning in the middleware context. We identify different alternatives for control, and we elaborate the technical issues related to controlling the internal characteristics of object middleware. We illustrate our QoS control approach by means of a scenario based on CORBA

Design of a middleware for QoS-aware distribution transparent content delivery

Developers of distributed multimedia applications face a diversity of multimedia formats, streaming platforms and streaming protocols. Furthermore, support for end-to-end quality-of-service (QoS) is a crucial factor for the development of future distributed multimedia systems. This paper discusses the architecture, design and implementation of a QoS-aware middleware platform for content delivery. The platform supports the development of distributed multimedia applications and can deliver content with QoS guarantees. QoS support is offered by means of an agent infrastructure for QoS negotiation and enforcement. Properties of content are represented using a generic content representation model described using the OMG Meta Object Facility (MOF) model. A content delivery framework manages stream paths for content delivery despite differences in streaming protocols and content encoding. The integration of the QoS support, content representation and content delivery framework results in a QoS-aware middleware that enables representation transparent and location transparent delivery of content

A Survey on Service Composition Middleware in Pervasive Environments

The development of pervasive computing has put the light on a challenging problem: how to dynamically compose services in heterogeneous and highly changing environments? We propose a survey that defines the service composition as a sequence of four steps: the translation, the generation, the evaluation, and finally the execution. With this powerful and simple model we describe the major service composition middleware. Then, a classification of these service composition middleware according to pervasive requirements - interoperability, discoverability, adaptability, context awareness, QoS management, security, spontaneous management, and autonomous management - is given. The classification highlights what has been done and what remains to do to develop the service composition in pervasive environments