We provide a retrospective on our article “QoS-Aware Middleware for Web Service Composition” published in the May 2004 issue of IEEE Transactions on Software Engineering. We start by defining the concept of Web service composition, and reviewing key paradigms emerging from early research on this topic. We then explain how the 2004 QoS article laid down a conceptual foundation that spawned a prolific line of research into the estimation and optimization of Quality of Service (QoS) properties of composite Web services. We close with a reflection on how the challenges tackled by the 2004 QoS article and other research on service composition are re-emerging in the field of Large Language Model (LLM) agents. We argue that the design of LLM agents raises challenges similar to those that the 2004 QoS article tackled, while bringing in new challenges due to the non-deterministic nature of LLMs and the fact that they consume and produce unstructured data.

MapReduce job parameter tuning is a daunting and time consuming task. The parameter configuration space is huge; there are more than 70 parameters that impact job performance. It is also difficult for users to determine suitable values for the parameters without first having a good understanding of the MapReduce application characteristics. Thus, it is a challenge to systematically explore the parameter space and select a near-optimal configuration. Extant offline tuning approaches are slow and inefficient as they entail multiple test runs and significant human effort.

Provisioning legacy simulation applications for Product Lifecycle Management (PLM) is very challenging. There are two key issues: (i) Hosting applications for simulation job executions. These legacy applications are developed in various platforms, usually monolithic, and may heavily consume system resources in the runtime. (ii) Input/output data storage. During the lifecycle of a project, huge amount of design data is generated by various design tools. When a design is completed, it needs to be validated by numerous simulation applications, which consequently create substantial amount of result data. Therefore, among others, the scalability in both system runtime resources and data storage is one of the biggest challenges. A generic cloud solution, which promises elastic scalability in both computation resources and data storage but lack of coordination between runtime resource allocation and data placement, is insufficient for supporting these legacy applications efficiently. In this paper, we share our experience on implementing a cloud solution that incorporates both scalable application hosting and usage-aware data placement into an integrated mechanism. With such a mechanism, our system can not only scale out according to workload, but also improve performance by increasing the possibility of local data access.

Offering personalized services through dynamically formed ecosystems is essential to personal wellness management. In this paper, we present the design of a cloud-enabled platform to facilitate the collection and delivery of evidence for personalization in a multi-provider ecosystem environment. In

Static analysis has gained much attention over the past few years in applications such as bug finding and program verification. As software becomes more complex and componentized, it is common for software systems and applications to be implemented in multiple languages. There is thus a strong need for developing analysis tools for multi-language software. We introduce a technique called Analysis Preserving Language Transformation (aplt) that enables the analysis of multi-language software, and also allows analysis tools for one language to be applied to programs written in another. aplt preserves data and control flow information needed to perform static analyses, but allows the translation to deviate from the original program's semantics in ways that are not pertinent to the particular analysis. We discuss major technical difficulties in building such a translator, using a C-to-Java translator as an example. We demonstrate the feasibility and effectiveness of aplt using two usage cases: analysis of the Java runtime native methods and reuse of Java analysis tools for C. Our preliminary results show that a control- and data-flow equivalent model for native methods can eliminate unsoundness and produce reliable results, and that aplt enables seamless reuse of analysis tools for checking high-level program properties.

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.

The process-driven composition of Web services is emerging as a promising approach to integrate business applications 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. The tasks of this process model are essentially invocations to functionalities offered by the underlying component services. Usually, several component services are able to execute a given task, although with different levels of pricing and quality. In this paper, we advocate that the selection of component services should be carried out during the execution of a composite service, rather than at design-time. In addition, this selection should consider multiple criteria (e.g., price, duration, reliability), and it should take into account global constraints and preferences set by the user (e.g., budget constraints). Accordingly, the paper proposes a global planning approach to optimally select component services during the execution of a composite service. Service selection is formulated as an optimization problem which can be solved using efficient linear programming methods. Experimental results show that this global planning approach outperforms approaches in which the component services are selected individually for each task in a composite service.