Service Quality Assessment for Cloud-based Distributed Data Services

The issue of less-than-100% reliability and trust-worthiness of third-party controlled cloud components (e.g., IaaS and SaaS components from different vendors) may lead to laxity in the QoS guarantees offered by a service-support system S to various applications. An example of S is a replicated data service to handle customer queries with fault-tolerance and performance goals. QoS laxity (i.e., SLA violations) may be inadvertent: say, due to the inability of system designers to model the impact of sub-system behaviors onto a deliverable QoS. Sometimes, QoS laxity may even be intentional: say, to reap revenue-oriented benefits by cheating on resource allocations and/or excessive statistical-sharing of system resources (e.g., VM cycles, number of servers). Our goal is to assess how well the internal mechanisms of S are geared to offer a required level of service to the applications. We use computational models of S to determine the optimal feasible resource schedules and verify how close is the actual system behavior to a model-computed \u27gold-standard\u27. Our QoS assessment methods allow comparing different service vendors (possibly with different business policies) in terms of canonical properties: such as elasticity, linearity, isolation, and fairness (analogical to a comparative rating of restaurants). Case studies of cloud-based distributed applications are described to illustrate our QoS assessment methods. Specific systems studied in the thesis are: i) replicated data services where the servers may be hosted on multiple data-centers for fault-tolerance and performance reasons; and ii) content delivery networks to geographically distributed clients where the content data caches may reside on different data-centers. The methods studied in the thesis are useful in various contexts of QoS management and self-configurations in large-scale cloud-based distributed systems that are inherently complex due to size, diversity, and environment dynamicity

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

Model Based Development of Quality-Aware Software Services

Modelling languages and development frameworks give support for functional and structural description of software architectures. But quality-aware applications require languages which allow expressing QoS as a first-class concept during architecture design and service composition, and to extend existing tools and infrastructures adding support for modelling, evaluating, managing and monitoring QoS aspects. In addition to its functional behaviour and internal structure, the developer of each service must consider the fulfilment of its quality requirements. If the service is flexible, the output quality depends both on input quality and available resources (e.g., amounts of CPU execution time and memory). From the software engineering point of view, modelling of quality-aware requirements and architectures require modelling support for the description of quality concepts, support for the analysis of quality properties (e.g. model checking and consistencies of quality constraints, assembly of quality), tool support for the transition from quality requirements to quality-aware architectures, and from quality-aware architecture to service run-time infrastructures. Quality management in run-time service infrastructures must give support for handling quality concepts dynamically. QoS-aware modeling frameworks and QoS-aware runtime management infrastructures require a common evolution to get their integration

SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE International Conference on Cloud and Service Computing (CSC 2011, IEEE Press, USA), Hong Kong, China, December 12-14, 201

Multimedia Teleservices Modelled with the OSI Application Layer Structure

This paper looks into the communications capabilities that are required by distributed multimedia applications to achieve relation preserving information exchange. These capabilities are derived by analyzing the notion of information exchange and are embodied in communications functionalities. To emphasize the importance of the users' view, a top-down approach is applied. The (revised) OSI Application Layer Structure (OSI-ALS) is used to model the communications functionalities and to develop an architecture for composition of multimedia services with these functionalities. This work may therefore be considered an exercise to evaluate the suitability of OSI-ALS for composition of multimedia teleservices

A survey on quality of service support on middelware-based distributed messaging systems used in multi agent systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-19934-9_10Messaging systems are widely used in distributed systems to hide the details of the communications mechanism to the multi agents systems. However, the Quality of Service is treated in different way depending on the messaging system used. This article presents a review and further analysis of the quality of service treatment in the mainly messaging systems used in distributed multi agent systems. The review covers the issues related to the purpose of the functions provided and the scope of the quality of service offered by every messaging system. We propose ontology for classifying and decide which parameters are relevant to the user. The results of the analysis and the ontology can be used to select the most suitable messaging system to distributed multi agent architecture and to establish the quality of service requirements in a distributed system.The study 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 European FEDER found. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2011). A survey on quality of service support on middelware-based distributed messaging systems used in multi agent systems. En International Symposium on Distributed Computing and Artificial Intelligence. Springer. 77-84. https://doi.org/10.1007/978-3-642-19934-9_10S7784Gaddah, A., Kunz, T.: A survey of middleware paradigms for mobile computing. Technical Report SCE-03-16. Carleton University Systems and Computing Engineering (2003)Foundation for Intelligent Physical Agents, http://www.fipa.org/Java Message Service Specification, http://java.sun.com/products/jms/docs.htmlCommon Object Request Broker Architecture, http://www.corba.org/Data Distribution Service, http://portals.omg.org/dds/Java Agent DEvelopment Framework, http://jade.tilab.com/Agent Oriented Software Pty Ltd., JACK Intelligent Agents: User Guide (1999)Nwana, H., Ndumu, D., Lee, L., Collis, J.: ZEUS: A tool-kit for building distributed multi-agent systems. Applied Artifical Intelligence Journal 13(1), 129–186 (1999)Perdikeas, M.K., Chatzipapadopoulos, F.G., Venieris, I.S., Marino, G.: Mobile Agent Standards and Available Platforms. Computer Networks Journal, Special Issue on ’Mobile Agents in Intelligent Networks and Mobile Communication Systems’ 31(10) (1999)Perrone, P.J., Chaganti, K.: J2EE Developer’s Handbook. Sam’s Publishing, Indianapolis (2003)Apache ActiveMQ, http://activemq.apache.org/IBM WebSphere MQSeries, http://mqseries.net/Object Management Group, http://www.omg.org/RTI Data Distribution Service. RTI corp., http://www.rti.com/OpenSplice DDS. PrismTech Ltd., http://www.prismtech.comVogel, 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)Foundation for Intelligent Physical Agents. FIPA Quality of Service Ontology Specification. Doc: SC00094A (2002)Sun Microsystems, Inc. Java(TM) Message Service Specification Final Release 1.1 (2002)Object Management Group (OMG). The Common Object Request Broker Architecture and Specification. CORBA 2.4.2 (2001