The Business Process Execution Language (BPEL) standardizes the development of composite enterprise applications that make use of software components exposed as Web services. BPEL processes are currently executed by a centralized orchestration engine, in which issues such as scalability, platform heterogeneity, and division across administrative domains can be difficult to manage. We propose a distributed agent-based orchestration engine in which several lightweight agents execute a portion of the original business process and collaborate in order to execute the complete process. The complete set of standard BPEL activities are supported, and the transformations of several BPEL activities to the agent-based architecture are described. Evaluations of an implementation of this architecture demonstrate that agent-based execution scales better than a non-distributed approach, with at least 70% and 120% improvements in process execution time, and throughput, respectively, even with a large number of concurrent process instances. In addition, the distributed architecture successfully executes large processes that are shown to be infeasible to execute with a nondistributed engine.

This paper formalizes transactional properties for publish/subscribe client mobility and develops protocols to realize them. Evaluations show that compared to traditional protocols, those developed in this paper, in addition to supporting transactional properties, are more stable with respect to message and processing overheads. Changes in factors such as the number of moving clients have little impact, making the protocols more scalable and simpler to administer due to predictable resource requirements.

XML-based data dissemination networks are rapidly gaining momentum. In these networks XML content is routed from data producers to data consumers throughout an overlay network of content-based routers. Routing decisions are based on XPath expressions (XPEs) stored at each router. To enable efficient routing, while keeping the routing state small, we introduce an advertisement-based routing algorithm for XML content, present a novel data structure for managing XPEs, especially apt for the hierarchical nature of XPEs and XML, and develop several optimizations for reducing the number of XPEs required to manage the routing state. The experimental evaluation shows that our algorithms and optimizations reduce the routing table size by up to 90%, improve the routing time by roughly 85%, and reduce overall network traffic by about 35%. Experiments running on PlanetLab show the scalability of our approach.

This paper addresses the XML/XPath content-based routing problem. More specifically, this paper focuses on the problem of efficiently routing an XML document emitted from a data producers at one point in the network to a set of data consumers located anywhere throughout the network. Prior to receiving XML documents, consumers must have expressed interest in receiving XML documents by registering XPEs with the network. This problem statement is akin to the well-known publish/subscribe matching problem. However, the main difference here is that in the case of data dissemination networks there exists no one single centralized publish/subscribe system, but a network of content-based routers (i.e., a network or federation of publish/subscribe systems).

The challenge faced by content-based publish/subscribe systems is the ability to handle a vast amount of dynamic information with limited system resources. In current p/s systems, each subscription is processed in isolation. Neither relationships among individual subscriptions are exploited, nor historic information about subscriptions and publications is taken into account. We believe that this neglect limits overall system efficiency. In this paper, we represent subscriptions using modified binary decision diagrams (MBDs), and design an index data structure to maintain distinct predicates and manage associated Boolean variables. Our MBD-based approach can address, in a unified way, publication routing and subscription/advertisement covering and merging. We propose a novel covering algorithm based on MBDs. The algorithm can take historic information about subscription and publication populations into account and exploits relations between subscriptions. We explore merging, especially imperfect merging, and discuss an advertisement-based optimization applicable to subscription merging