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Abstract. A problem with many distributed applications is their behavior in lieu of unpredictable variations in user request volumes or in available resources. This paper explores a performance isolation-based approach to creating robust distributed applications. For each application, the approach is to (1) understand the performance dependencies that pervade it and then (2) provide mechanisms for imposing constraints on the possible 'spread' of such dependencies through the application. Concrete results are attained for J2EE middleware, for which we identify sample performance dependencies: in the application layer during request execution and in the middleware layer during request de-fragmentation and during return parameter marshalling. Isolation points are the novel software abstraction used to capture performance dependencies and represent solutions for dealing with them, and they are used to create (2) I(solation)-RMI, which is a version of RMI-IIOP implemented in the WebSphere service infrastructure enhanced with isolation points. Initial results show the approach's ability to detect and filter ill-behaving messages that can cause an up to a 85% drop in performance for the Trade3 benchmark, and to eliminate up to a 56% drop in performance due to misbehaving clients.

In this paper, we present a framework for extracting mutually-salient landmark pairs for registration. Traditional methods detect landmarks one-by-one and separately in two images. Therefore, the detected landmarks might inherit low discriminability and are not necessarily good for matching. In contrast, our method detects landmarks pair-by-pair across images, and those pairs are required to be mutually-salient, i.e., uniquely corresponding to each other. The second merit of our framework is that, instead of finding individually optimal correspondence, which is a local approach and could cause self-intersection of the resultant deformation, our framework adopts a Markov-random-field (MRF)-based spatial arrangement to select the globally optimal landmark pairs. In this way, the geometric consistency of the correspondences is maintained and the resultant deformations are relatively smooth and topology-preserving. Promising experimental validation through a radiologist's evaluation of the established correspondences is presented. © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Author(s)Yangming ABSTRACTIn this paper, we present a framework for extracting mutuallysalient landmark pairs for registration. Traditional methods detect landmarks one-by-one and separately in two images. Therefore, the detected landmarks might inherit low discriminability and are not necessarily good for matching. In contrast, our method detects landmarks pair-by-pair across images, and those pairs are required to be mutually-salient, i.e., uniquely corresponding to each other. The second merit of our framework is that, instead of finding individually optimal correspondence, which is a local approach and could cause self-intersection of the resultant deformation, our framework adopts a Markov-random-field (MRF)-based spatial arrangement to select the globally optimal landmark pairs. In this way, the geometric consistency of the correspondences is maintained and the resultant deformations are relatively smooth and topology-preserving. Promising experimental validation through a radiologist's evaluation of the established correspondences is presented.

Abstract. Modern enterprise applications and systems are characterized by complex underlying software structures, constantly evolving feature sets, and frequent changes in the data on which they operate. The dynamic nature of these applications and systems poses substantial challenges to their use and management, suggesting the need for automated solutions. This paper considers a specific set of dynamic changes, large data updates that reflect changes in the current state of the business, where the frequency of such updates can be multiple times per day. The paper then presents techniques and their middleware implementation for automatically managing requests streams directed at server applications subjected to dynamic data updates, the goal being to improve application reliability in face of evolving feature sets and business data. These techniques (1) automatically detect input patterns that lead to performance degradation or failures and then (2) use these detections to trigger application-specific methods that control input patterns to avoid or at least, defer such undesirable phenomena. Lab experiments using actual traces from Worldspan show a 16% decrease in frequency of server restarts when using these techniques, at negligible costs in additional overheads and within delays suitable for the rates of changes experienced by this application.