$\mathcal{P}ro\mathcal{M}o$ – A Scalable and Efficient Framework for Online Data Delivery

Abstract

Web enabled application servers have had to increase the sophistication of their server capabilities in order to keep up with the increasing demand for client customization. Typical applications include RSS feeds, stock prices and auctions on the commercial Internet, and increasingly, the availability of Grid computational resources. Web data delivery technology has not kept up with these demands. There still remains a fundamental trade-off between the scalability of both performance and ease of implementation on the server side, with respect to the multitude and diversity of clients, and the required customization to deliver the right service/data to the client at the desired time. Current data delivery solutions can be classified as either push or pull solutions, each suffering from different drawbacks. Push is not scalable, and reaching a large numbers of potentially transient clients is typically expensive in terms of resource consumption and implementation by a server. In some cases, where there is a mismatch with client needs, pushing information may overwhelm the client with unsolicited information. Pull, on the other hand, can increase network and server workload and often cannot meet client needs. Several hybrid push-pull solutions have also been presented in the past. In this demonstration we present $\mathcal{P}ro\mathcal{M}o$, a scalable and efficient hybrid data delivery solution.