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Dynamic Policy-Driven Quality of Service in Service-Oriented Information Management Systems

Service-oriented architecture(SOA) middleware has emerged as a powerful and popular distributed computing paradigm due to its high-level abstractions for composing systems and encapsulating plat-form-level details and complexities. Control of some details encapsulated by SOA middleware is necessary,...

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Main Authors: Loyall, Joseph P, Gillen, Matthew, Paulos, Aaron, Bunch, Larry, Carvalho, Marco, Edmondson, James, Schmidt, Douglas C, Martignoni, III, Andrew, Sinclair, Asher
Format: Report
Language:English
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Summary:Service-oriented architecture(SOA) middleware has emerged as a powerful and popular distributed computing paradigm due to its high-level abstractions for composing systems and encapsulating plat-form-level details and complexities. Control of some details encapsulated by SOA middleware is necessary, however, to provide managed quality-of-service (QoS) for SOA systems that require predictable performance and behavior. This paper presents a policy-driven approach for managing QoS in SOA systems called QoS Enabled Dissemination (QED). QED includes services for (1) specifying and enforcing the QoS preferences of individual clients, (2) mediating and aggregating QoS management on behalf of competing users, and (3) shaping information exchange to improve real-time performance. We describe QED's QoS services and mechanisms in the context of managing QoS for a set of Publish-Subscribe-Query information management services. These services provide a representative case study in which CPU and network bottlenecks can occur, client QoS preferences can conflict, and system-level QoS requirements are based on higher level, aggregate end-to-end goals. We also discuss the design of several key QoS services and describe how QED's policy-driven approach bridges users to the underlying middleware and enables QoS control based on rich and meaningful context descriptions, including users, data types, client preferences, and information characteristics. In addition, we present experimental results that quantify the improved control, differentiation, and client-level QoS enabled by QED. Published in Software: Practice and Experience, v41 n12, pp. 1459-1489. Prepared in collaboration with the Air Force Research Laboratory, Rome, NY; Institute for Human Machine Cognition, Pensacola, FL; Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN; and The Boeing Company, St. Louis, MO.