文摘
Service-Oriented Architecture (SOA) is a software paradigm for providing a uniform means to discover, interact with and use capabilities to produce desired effects consistent with measurable preconditions and expectation. SOA also provides a framework for service composition by putting together of a number of services to make a more complex one to fulfill a business process and service reconfiguration by replacing faulty services with better performing services to have seamless business process execution. In general, service integration tries to maximize the performance of a business process such as to have a minimum latency, the shortest response time, the optimal reliability, an acceptable level of security and so on. Although technology exists to optimize the performance in terms of the above QoS attributes, it still may not meet the need of all users, since user may have unique service requirement at some specific context.
In order to address this issue, in this dissertation, we present an approach to improve overall performance for the execution of business process by considering context-based service performance profile management through context-adaptive middleware component called Context Manager. Context Manager is designed to collect service performance feedback from users along with their context data, maintain service performance profile data, distinguish influential context for service performance, predict how the service would perform under user context and provide most-adaptive service for user context. We design context tree to represent context data in an efficient way to manage, propose methodology to distinguish which context attributes are affecting service performance, called critical context attribute, and use the K-mean, clustering algorithm to classify and record context dependency on the observed service performance. We also study algorithms to decide the clustering unit of context attribute values and to distinguish those context attributes that may or may not significantly affect service performance. In addition, we propose an approach to integrate context awareness into web service model with various SOAP-based APIs.
Our experiment results indicate that context manager system effectively maintains service performance profile data, clearly understands the influence of some specific context attributes on service performance and provides context-adaptive services as users expect. Moreover, integrating context-awareness into SOA brings significant performance improvement in terms of lower rate of service failure and reconfiguration, and more optimal response time between service requester and service provider. We also present performance studies from actual system implementation and deployment.
