${\ssr BPELcube}$, a framework comprising a scalable architecture and a set of distributed algorithms, which support the decentralized enactment of BPEL processes. In many application domains, BPEL processes are long-running, involve the exchange of voluminous data with external Web services, and are concurrently accessed by large numbers of users. In such context, centralized BPEL process execution engines pose considerable limitations in terms of scalability and performance. To overcome such problems, a scalable hypercube peer-to-peer topology is employed by ${\ssr BPELcube}$ in order to organize an arbitrary number of nodes, which can then collaborate in the decentralized execution and monitoring of BPEL processes. Contrary to traditional clustering approaches, each node does not fully take charge of executing the whole process; rather, it contributes to the overall process execution by running a subset of the process activities and maintaining a subset of the process variables. Hence, the hypercube-based infrastructure acts as a single execution engine, where workload is evenly distributed among the participating nodes in a fine-grained manner. An experimental evaluation of ${ \ssr BPELcube}$ and a comparison with centralized and clustered BPEL engine architectures demonstrate that the decentralized approach yields improved process execution times and throughput." /> ${\ssr BPELcube}$, a framework comprising a scalable architecture and a set of distributed algorithms, which support the decentralized enactment of BPEL processes. In many application domains, BPEL processes are long-running, involve the exchange of voluminous data with external Web services, and are concurrently accessed by large numbers of users. In such context, centralized BPEL process execution engines pose considerable limitations in terms of scalability and performance. To overcome such problems, a scalable hypercube peer-to-peer topology is employed by ${\ssr BPELcube}$ in order to organize an arbitrary number of nodes, which can then collaborate in the decentralized execution and monitoring of BPEL processes. Contrary to traditional clustering approaches, each node does not fully take charge of executing the whole process; rather, it contributes to the overall process execution by running a subset of the process activities and maintaining a subset of the process variables. Hence, the hypercube-based infrastructure acts as a single execution engine, where workload is evenly distributed among the participating nodes in a fine-grained manner. An experimental evaluation of ${ \ssr BPELcube}$ and a comparison with centralized and clustered BPEL engine architectures demonstrate that the decentralized approach yields improved process execution times and throughput." /> ${\ssr BPELcube}$, a framework comprising a scalable architecture and a set of distributed algorithms, which support the decentralized enactment of BPEL processes. In many application domains, BPEL processes are long-running, involve the exchange of voluminous data with external Web services, and are concurrently accessed by large numbers of users. In such context, centralized BPEL process execution engines pose considerable limitations in terms of scalability and performance. To overcome such problems, a scalable hypercube peer-to-peer topology is employed by ${\ssr BPELcube}$ in order to organize an arbitrary number of nodes, which can then collaborate in the decentralized execution and monitoring of BPEL processes. Contrary to traditional clustering approaches, each node does not fully take charge of executing the whole process; rather, it contributes to the overall process execution by running a subset of the process activities and maintaining a subset of the process variables. Hence, the hypercube-based infrastructure acts as a single execution engine, where workload is evenly distributed among the participating nodes in a fine-grained manner. An experimental evaluation of ${ \ssr BPELcube}$ and a comparison with centralized and clustered BPEL engine architectures demonstrate that the decentralized approach yields improved process execution times and throughput." /> Decentralized Enactment of BPEL Processes
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Issue No.02 - April-June (2014 vol.7)
pp: 184-197
Aphrodite Tsalgatidou , Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
ABSTRACT
This article presents ${\ssr BPELcube}$, a framework comprising a scalable architecture and a set of distributed algorithms, which support the decentralized enactment of BPEL processes. In many application domains, BPEL processes are long-running, involve the exchange of voluminous data with external Web services, and are concurrently accessed by large numbers of users. In such context, centralized BPEL process execution engines pose considerable limitations in terms of scalability and performance. To overcome such problems, a scalable hypercube peer-to-peer topology is employed by ${\ssr BPELcube}$ in order to organize an arbitrary number of nodes, which can then collaborate in the decentralized execution and monitoring of BPEL processes. Contrary to traditional clustering approaches, each node does not fully take charge of executing the whole process; rather, it contributes to the overall process execution by running a subset of the process activities and maintaining a subset of the process variables. Hence, the hypercube-based infrastructure acts as a single execution engine, where workload is evenly distributed among the participating nodes in a fine-grained manner. An experimental evaluation of ${ \ssr BPELcube}$ and a comparison with centralized and clustered BPEL engine architectures demonstrate that the decentralized approach yields improved process execution times and throughput.
INDEX TERMS
Engines, Peer to peer computing, Hypercubes, Web services, Topology, Scalability, Terrain factors,simulation of business processes, Composite web services, processes, business process management
CITATION
Aphrodite Tsalgatidou, "Decentralized Enactment of BPEL Processes", IEEE Transactions on Services Computing, vol.7, no. 2, pp. 184-197, April-June 2014, doi:10.1109/TSC.2013.6
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