QoS-driven proactive adaptation of service composition

Proactive adaptation of service composition has been recognized as a major research challenge for service-based systems. In this paper we describe an approach for proactive adaptation of service composition due to changes in service operation response time; or unavailability of operations, services, and providers. The approach is based on exponentially weighted moving average (EWMA) for modelling service operation response time. The prediction of problems and the need for adaptation consider a group of services in a composition flow, instead of isolated services. The decision of the service operations to be used to replace existing operations in a composition takes into account response time and cost values. A prototype tool has been implemented to illustrate and evaluate the approach. The paper also describes the results of a set of experiments that we have conducted to evaluate the work

Learning a goal-oriented model for energy efficient adaptive applications in data centers

This work has been motivated by the growing demand of energy coming from the IT sector. We propose a goal-oriented approach where the state of the system is assessed using a set of indicators. These indicators are evaluated against thresholds that are used as goals of our system. We propose a self-adaptive context-aware framework, where we learn both the relations existing between the indicators and the effect of the available actions over the indicators state. The system is also able to respond to changes in the environment, keeping these relations updated to the current situation. Results have shown that the proposed methodology is able to create a network of relations between indicators and to propose an effective set of repair actions to contrast suboptimal states of the data center. The proposed framework is an important tool for assisting the system administrator in the management of a data center oriented towards Energy Efficiency (EE), showing him the connections occurring between the sometimes contrasting goals of the system and suggesting the most likely successful repair action(s) to improve the system state, both in terms of EE and QoS

Proactive and reactive runtime service discovery: a framework and its evaluation

The identification of services during the execution of service-based applications to replace services in them that are no longer available and/or fail to satisfy certain requirements is an important issue. In this paper we present a framework to support runtime service discovery. This framework can execute service discovery queries in pull and push mode. In pull mode, it executes queries when a need for finding a replacement service arises. In push mode, queries are subscribed to the framework to be executed proactively, and in parallel with the operation of the application, in order to identify adequate services that could be used if the need for replacing a service arises. Hence, the proactive (push) mode of query execution makes it more likely to avoid interruptions in the operation of service-based applications when a service in them needs to be replaced at runtime. In both modes of query execution, the identification of services relies on distance-based matching of structural, behavioural, quality, and contextual characteristics of services and applications. A prototype implementation of the framework has been developed and an evaluation was carried out to assess the performance of the framework. This evaluation has shown positive results, which are discussed in the paper

KPI-related monitoring, analysis, and adaptation of business processes

In today's companies, business processes are increasingly supported by IT systems. They can be implemented as service orchestrations, for example in WS-BPEL, running on Business Process Management (BPM) systems. A service orchestration implements a business process by orchestrating a set of services. These services can be arbitrary IT functionality, human tasks, or again service orchestrations. Often, these business processes are implemented as part of business-to-business collaborations spanning several participating organizations. Service choreographies focus on modeling how processes of different participants interact in such collaborations. An important aspect in BPM is performance management. Performance is measured in terms of Key Performance Indicators (KPIs), which reflect the achievement towards business goals. KPIs are based on domain-specific metrics typically reflecting the time, cost, and quality dimensions. Dealing with KPIs involves several phases, namely monitoring, analysis, and adaptation. In a first step, KPIs have to be monitored in order to evaluate the current process performance. In case monitoring shows negative results, there is a need for analyzing and understanding the reasons why KPI targets are not reached. Finally, after identifying the influential factors of KPIs, the processes have to be adapted in order to improve the performance. %The goal thereby is to enable these phases in an automated manner. This thesis presents an approach how KPIs can be monitored, analyzed, and used for adaptation of processes. The concrete contributions of this thesis are: (i) an approach for monitoring of processes and their KPIs in service choreographies; (ii) a KPI dependency analysis approach based on classification learning which enables explaining how KPIs depend on a set of influential factors; (iii) a runtime adaptation approach which combines monitoring and KPI analysis in order to enable proactive adaptation of processes for improving the KPI performance; (iv) a prototypical implementation and experiment-based evaluation.Die Ausführung von Geschäftsprozessen wird heute zunehmend durch IT-Systeme unterstützt und auf Basis einer serviceorientierten Architektur umgesetzt. Die Prozesse werden dabei häufig als Service Orchestrierungen implementiert, z.B. in WS-BPEL. Eine Service Orchestrierung interagiert mit Services, die automatisiert oder durch Menschen ausgeführt werden, und wird durch eine Prozessausführungsumgebung ausgeführt. Darüber hinaus werden Geschäftsprozesse oft nicht in Isolation ausgeführt sondern interagieren mit weiteren Geschäftsprozessen, z.B. als Teil von Business-to-Business Beziehungen. Die Interaktionen der Prozesse werden dabei in Service Choreographien modelliert. Ein wichtiger Aspekt des Geschäftsprozessmanagements ist die Optimierung der Prozesse in Bezug auf ihre Performance, die mit Hilfe von Key Performance Indicators (KPIs) gemessen wird. KPIs basieren auf Prozessmetriken, die typischerweise die Dimensionen Zeit, Kosten und Qualität abbilden, und evaluieren diese in Bezug auf die Erreichung von Unternehmenszielen. Die Optimierung der Prozesse in Bezug auf ihre KPIs umfasst mehrere Phasen. Im ersten Schritt müssen KPIs durch Monitoring der Prozesse zur Laufzeit erhoben werden. Falls die KPI Werte nicht zufriedenstellend sind, werden im nächsten Schritt die Faktoren analysiert, die die KPI Werte beeinflussen. Schließlich werden auf Basis dieser Analyse die Prozesse angepasst um die KPIs zu verbessern. In dieser Arbeit wird ein integrierter Ansatz für das Monitoring, die Analyse und automatisierte Adaption von Prozessen mit dem Ziel der Optimierung hinsichtlich der KPIs vorgestellt. Die Beiträge der Arbeit sind wie folgt: (i) ein Ansatz zum Monitoring von KPIs über einzelne Prozesse hinweg in Service Choreographien, (ii) ein Ansatz zur Analyse von beeinflussenden Faktoren von KPIs auf Basis von Entscheidungsbäumen, (iii) ein Ansatz zur automatisierten, proaktiven Adaption von Prozessen zur Laufzeit auf Basis des Monitorings und der KPI Analyse, (iv) eine prototypische Implementierung und experimentelle Evaluierung