Presence, broadly defined as an event publish-notification infrastructure for converged applications, has emerged as a key mechanism for collecting and disseminating context attributes for next-generation services in both enterprise and provider domains. Current presence-based solutions and products lack in the ability to a) support flexible user-defined queries over dynamic presence data and b) derive composite presence from multiple provider domains. Accordingly, current uses of context are limited to individual domains/organizations and do not provide a programmable mechanism for rapid creation of context-aware services. This paper describes a presence virtualization architecture, where a Virtualized Presence Server receives customizable queries from multiple presence clients, retrieves the necessary data from the base presence servers, applies the required virtualization logic and notifies the presence clients. To support both query expressiveness and computational efficiency, virtualization queries are structured to separately identify both the XSLT-based transformation primitives and the presence sources over which the transformation occurs. For improved scalability, the proposed architecture offloads the XSLT-related processing to a high-performance XML processing engine. We describe our current implementation and present performance results that attest to the promise of this virtualization approach.

Session Initiation Protocol (SIP) has begun to be widely deployed for multiple services such as VoIP, Instant Messaging and Presence. Each of these services uses different SIP messages, and depending on the value of a service, e.g. revenue, the associated messages may need to be prioritized accordingly. Even within the same service, different messages may be assigned different priorities. In this paper, we present the design and implementation of a programmable classification engine for SIP messages in the Linux kernel. This design uses a novel algorithm that in addition to classifying messages can extract and maintain state information across multiple messages. We apply the classifier for overload control using operator-specified rules for categorizing messages and associated actions, augmented with a protocol-level understanding of SIP message structure. When faced with loads beyond their capacity (e.g., during catastrophic situations and major network outages), SIP servers must drop messages. It is therefore desirable that the server process high-value messages in preference to lower-value messages. We evaluated our in-kernel classifier implementation with an open source SIP server (SER) for such an overload scenario. The workload consists of a mix of call setup and call handoff messages, and the classifier is programmed with rules that prioritize handoffs over call setups. We show that, while SER can process about 40K messages/sec (in a FIFO manner), our classifier can examine and prioritize 105K messages/sec during overload. With the classifier operating at peak throughput, SER's processing rate drops to 31.6K messages/sec, but all of the available high-value messages are processed.

Measurement of software development productivity is needed in order to control software costs, but it is discouragingly labor-intensive and expensive. Computer-aided software engineering (CASE) technologies/spl minus/especially repository-based, integrated CASE/spl minus/have the potential to support the automation of this measurement. We discuss the conceptual basis for the development of automated analyzers for function point and software reuse measurement for object-based CASE. Both analyzers take advantage of the existence of a representation of the application system that is stored within an object repository, and that contains the necessary information about the application system. We also discuss metrics for software reuse measurement, including reuse leverage, reuse value, and reuse classification that are motivated by managerial requirements and the efforts, within industry and the IEEE, to standardize measurement. The functionality and the analytical capabilities of state-of-the-art automated software metrics analyzers are illustrated in the context of an investment banking industry application that is similar to systems deployed at the New York City-based investment bank where these tools were developed and tested.>