An adaptive service oriented architecture: Automatically solving interoperability problems.

Organizations desire to be able to easily cooperate with other companies and still be flexible. The IT infrastructure used by these companies should facilitate these wishes. Service-Oriented Architecture (SOA) and Autonomic Computing (AC) were introduced in order to realize such an infrastructure, however both have their shortcomings and do not fulfil these wishes. This dissertation addresses these shortcomings and presents an approach for incorporating (self-) adaptive behavior in (Web) services. A conceptual foundation of adaptation is provided and SOA is extended to incorporate adaptive behavior, called Adaptive Service Oriented Architecture (ASOA). To demonstrate our conceptual framework, we implement it to address a crucial aspect of distributed systems, namely interoperability. In particular, we study the situation of a service orchestrator adapting itself to evolving service providers.

An adaptive service oriented architecture:Automatically solving interoperability problems

Organizations desire to be able to easily cooperate with other companies and still be flexible. The IT infrastructure used by these companies should facilitate these wishes. Service-Oriented Architecture (SOA) and Autonomic Computing (AC) were introduced in order to realize such an infrastructure, however both have their shortcomings and do not fulfil these wishes. This dissertation addresses these shortcomings and presents an approach for incorporating (self-) adaptive behavior in (Web) services. A conceptual foundation of adaptation is provided and SOA is extended to incorporate adaptive behavior, called Adaptive Service Oriented Architecture (ASOA). To demonstrate our conceptual framework, we implement it to address a crucial aspect of distributed systems, namely interoperability. In particular, we study the situation of a service orchestrator adapting itself to evolving service providers.

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Achieving Autonomic Web Service Compositions with Models at Runtime

Over the last years, Web services have become increasingly popular. It is because they allow businesses to share data and business process (BP) logic through a programmatic interface across networks. In order to reach the full potential of Web services, they can be combined to achieve specifi c functionalities. Web services run in complex contexts where arising events may compromise the quality of the system (e.g. a sudden security attack). As a result, it is desirable to count on mechanisms to adapt Web service compositions (or simply called service compositions) according to problematic events in the context. Since critical systems may require prompt responses, manual adaptations are unfeasible in large and intricate service compositions. Thus, it is suitable to have autonomic mechanisms to guide their self-adaptation. One way to achieve this is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone as the number of con figurations for the service composition grows. The goal of this thesis is to provide a model-driven framework to guide autonomic adjustments of context-aware service compositions. This framework spans over design time and runtime to face arising known and unknown context events (i.e., foreseen and unforeseen at design time) in the close and open worlds respectively. At design time, we propose a methodology for creating the models that guide autonomic changes. Since Service-Oriented Architecture (SOA) lacks support for systematic reuse of service operations, we represent service operations as Software Product Line (SPL) features in a variability model. As a result, our approach can support the construction of service composition families in mass production-environments. In order to reach optimum adaptations, the variability model and its possible con figurations are verifi ed at design time using Constraint Programming (CP). At runtime, when problematic events arise in the context, the variability model is leveraged for guiding autonomic changes of the service composition. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. Changes in the variability model are refl ected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which are deployed at runtime. Model-driven strategies guide the safe migration of running service composition instances. Under the closed-world assumption, the possible context events are fully known at design time. These events will eventually trigger the dynamic adaptation of the service composition. Nevertheless, it is diffi cult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, we extend our framework to cover the dynamic evolution of service compositions to deal with unexpected events in the open world. If model adaptations cannot solve uncertainty, the supporting models self-evolve according to abstract tactics that preserve expected requirements.Alférez Salinas, GH. (2013). Achieving Autonomic Web Service Compositions with Models at Runtime [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34672TESI

09201 Abstracts Collection -- Self-Healing and Self-Adaptive Systems

From May 10th 2009 to May 15th 2009 the Dagstuhl Seminar 09201 ``Self-Healing and Self-Adaptive Systems\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar are put together in this paper. Links to extended abstracts or full papers are provided, if available. A description of the seminar topics, goals and results in general can be found in a separate document ``Executive Summary\u27\u27

DYNAMICO: A Reference Model for Governing Control Objectives and Context Relevance in Self-Adaptive Software Systems

International audienceDespite the valuable contributions on self-adaptation, most implemented approaches assume adaptation goals and monitoring infrastructures as non-mutable, thus constraining their applicability to systems whose context awareness is restricted to static monitors. Therefore, separation of concerns, dynamic monitoring, and runtime requirements variability are critical for satisfying system goals under highly changing environments. In this chapter we present DYNAMICO, a reference model for engineering adaptive software that helps guaranteeing the coherence of (i) adaptation mechanisms with respect to changes in adaptation goals; and (ii) monitoring mechanisms with respect to changes in both adaptation goals and adaptation mechanisms. DYNAMICO improves the engineering of self-adaptive systems by addressing (i) the management of adaptation properties and goals as control objectives; (ii) the separation of concerns among feedback loops required to address control objectives over time; and (iii) the management of dynamic context as an independent control function to preserve context-awareness in the adaptation mechanism

Organic Service-Level Management in Service-Oriented Environments

Dynamic service-oriented environments (SOEs) are characterised by a large number of heterogeneous service components that are expected to support the business as a whole. The present work provides a negotiation-based approach to facilitate automated and multi-level service-level management in an SOE, where each component autonomously arranges its contribution to the whole operational goals. Evaluation experiments have shown an increased responsiveness and stability of an SOE in case of changes

Fault Management For Service-Oriented Systems

Service Oriented Architectures (SOAs) enable the automatic creation of business applications from independently developed and deployed Web services. As Web services are inherently unreliable, how to deliver reliable Web services composition over unreliable Web services is a significant and challenging problem. The process requires monitoring the system\u27s behavior, determining when and why faults occur, and then applying fault prevention/recovery mechanisms to minimize the impact and/or recover from these faults. However, it is hard to apply a non-distributed management approach to SOA, since a manager needs to communicate with the different components through authentications. In SOA, a business process can terminate successfully if all services finish their work correctly through providing alternative plans in case of fault. However, the business process itself may encounter different faults because the fault may occur anywhere at any time due to SOA specifications. In this work, we propose new fault management technique (FLEX) and we identify several improvements over existing techniques. First, existing techniques rely mainly on static information while FLEX is based on dynamic information. Second, existing frameworks use a limited number of attributes; while we use all possible attributes by identify them as either required or optional. Third, FLEX reduces the comparison cost (time and space) by filtering out services at each level needed for evaluation. In general, FLEX is divided into two phases: Phase I, computes service reliability and utility, while in Phase II, runtime planning and evaluation. In Phase I, we assess the fault likelihood of the service using a combination of techniques (e.g., Hidden Marcov Model, Reputation, and Clustering). In Phase II, we build a recovery plan to execute in case of fault(s) and we calculate the overall system reliability based on the fault occurrence likelihoods assessed for all the services that are part of the current composition. FLEX is novel because it relies on key activities of the autonomic control loop (i.e., collect, analyze, decide, plan, and execute) to support dynamic management based on the changes of user requirements and QoS level. Our technique dynamically evaluates the performance of Web services based on their previous history and user requirements, assess the likelihood of fault occurrence, and uses the result to create (multiple) recovery plans. Moreover, we define a method to assess the overall system reliability by evaluating the performance of individual recovery plans, when invoked together. The Experiment results show that our technique improves the service selection quality by selecting the services with the highest score and improves the overall system performance in comparison with existing works. In the future, we plan to investigate techniques for monitoring service oriented systems and assess the online negotiation possibilities for combining different services to create high performance systems