Henry Chang

QoS-aware middleware for Web services composition

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.

Monitoring the QoS for Web Services

Quality of Service (QoS) information for Web services is essential to QoS-aware service management and composition. Currently, most QoS-aware solutions assume that the QoS for component services is readily available, and that the QoS for composite services can be computed from the QoS for component services. The issue of how to obtain the QoS for component services has largely been overlooked. In this paper, we tackle this fundamental issue. We argue that most of QoS metrics can be observed/computed based on service operations. We present the design and implementation of a high-performance QoS monitoring system. The system is driven by a QoS observation model that defines IT- and business-level metrics and associated evaluation formulas. Integrated into the SOA infrastructure at large, the monitoring system can detect and route service operational events systemically. Further, a model-driven, hybrid compilation/interpretation approach is used in metric computation to process service operational events and maintain metrics efficiently. Experiments suggest that our system can support high event processing throughput and scales to the number of CPUs.

Flexible Composition of Enterprise Web Services

The process-based composition of Web services is emerging as a promising approach to automate business process within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. Business process automation technology such as workflow management systems (WFMSs) can be used to choreograph the component services. However, one of the fundamental assumptions of most WFMSs is that workflow schemas are static and predefined. Such an assumption is impractical for business processes that have an explosive number of options, or dynamic business processes that must be generated and altered on the fly to meet rapid changing business conditions. In this paper, we describe a rule inference framework called DY flow , where end users declaratively define their business objectives or goals and the system dynamically composes Web services

Event-Driven Quality of Service Prediction

Quality of Service Management (QoSM) is a new task in IT-enabled enterprises that supports monitoring, collecting and predicting QoS data. QoSM solutions must be able to efficiently process runtime events, compute and pre dict QoS metrics, and provide real-time visibility and prediction of key perform ance indicators (KPI). Currently, most QoSM systems focus on moni tor ing of QoS constraints, i.e., they report what has been happened. In a way, this provides the awareness of past developments and sets the basis for decisions. However, this kind of knowledge is afterwit. For example, it cannot provide early warnings to prevent the QoS degradation or the violation of commitments. In this paper, we move one step forward to provide QoS prediction. We argue that performance metrics and KPIs can be predicted based on historical data. We present the design and implementation of a novel event-driven QoS prediction system. Integrated into the SOA infrastructure at large, the prediction system can process operational service events in a real-time fashion, in order to predict or refine the prediction of metrics and KPIs.

Model driven development for business performance management

Business process integration and monitoring provides an invaluable means for an enterprise to adapt to changing conditions. However, developing such applications using traditional methods is challenging because of the intrinsic complexity of integrating large-scale business processes and existing applications. Model Driven DevelopmentTM (MDDTM) is an approach to developing applications-from domain-specific models to platform-sensitive models-that bridges the gap between business processes and information technology. We describe the MDD framework and methodology used to create the IBM Business Performance Management (BPM) solution. We describe how we apply model-driven techniques to BPM and present a scenario from a pilot project in which these techniques were applied. Technical details on models and transformation are presented. Our framework uses and extends the IBM business observation metamodel and introduces a data warehouse metamodel and other platform-specific and transformational models. We discuss our lessons learned and present the general guidelines for using MDD to develop enterprise-scale applications.

Complex Event Processing using Simple Rule-based Event Correlation Engines for Business Performance Management

Event correlation is an important component in business performance management. Simple rule-based event correlation engines, like Zurich Correlation Engine (ZCE), usually accept input events that include only name-value pairs. However, when input events are in XML format, the ZCE needs to be adapted to accept complex/structural events. In this paper, we present a model-driven approach to derive solutions for adapting the new type of events, so that the light weight correlation engine can still be used for event monitoring and correlation

Model-driven business performance management

In this paper, we present a model-driven approach to business performance management (BPM). BPM is a new frontier in IT-enabled enterprise that supports the monitoring and control of business operations. BPM solutions must be able to efficiently process business events, compute business metrics, detect business situations, and provide the real-time visibility of key performance indicators. In addition, system support is required for the rapid development of BPM solutions and the adaptation of the solutions to the dynamic business environment. We have adopted a meta-model, dubbed the observation meta-model, for capturing the business requirements for BPM, which frees solution developers from low-level programming concerns. We have also used a hybrid compilation-interpretation approach to map an observation model to the runtime executable. First, we extract and refactor the data aspect of the observation model to facilitate runtime access. Second, we compile the operational aspect of the model, such as logic for metric computation and situation detection, into Java code. Third, we develop a runtime engine that interprets the refactored model and dynamically loads the generated code, according to the meta-model. Our framework further enables the evolution and hot deployment of the observation model and provides the platform for several on-going customer engagement efforts

Policy Driven Business Performance Management

Business performance management (BPM) has emerged as a critical discipline to enable enterprise to manage their business solutions in an on demand fashion. BPM applications promote an adaptive means by emphasizing the ability to monitor and control both business processes and IT events. However, most BPM processes and architectures are usually linear and rigid; and once done, will be very hard to change. Hence, it does not help enterprise to create adaptive monitoring and control applications for business solutions. There is an urgent need of adaptive BPM framework to be used as a platform of developing BPM applications. This paper presents a policy based BPM framework to help enterprise to achieve on demand monitoring and control framework for business solutions.

A model driven XML transformation framework for business performance management

As XML formats have been widely adopted for representing business documents both within and across enterprises, XML to XML translation becomes a common and critical component for business process integration. Due to limitations of popular approaches such as XSLT for XML translations, we designed a model driven development framework for XML to XML translation with the additional benefits of code re-use and strong built-in model validation. We further applied this framework to the domain of business performance management, converting documents from human-readable XML format to machine-readable XMI format

A model driven XML transformation framework for Business Performance Management model creation

As XML formats have been widely adopted for representing business documents both within and across enterprises, XML to XML translation becomes a common and critical component for business process integration. Due to limitations of popular approaches such as XSLT for XML translations, we designed a model driven development framework for XML to XML translation with the additional benefits of code re-use and strong built-in model validation. We further applied this framework to the domain of Business Performance Management (BPM), converting documents from human-readable XML format to machine-readable XMI format. Experimental evaluation indicated that XML models can have more than 75% size reduction.