Recent technological advances have enabled the deployment of wide area applications against Internet accessible sources.  A performance challenge to such applications is unpredictable behavior, e.g., the end-to-end latency of accessing sources in the dynamic WAN.  Our objective is to exploit open protocols and to use passive and non-intrusive information gathering mechanisms to learn end-to-end latency distributions and construct Latency Profiles (LPs) corresponding to these distributions. We hypothesize that groups of clients (client-cluster), within an autonomous system (AS), that are accessing a content server, in another AS, may be represented by a single LP.  Related networking research on IDMaps, points of congestion, and BGP routes support such a hypothesis.

For more information: http://www.umiacs.umd.edu/research/CLIP/Handle/

Profile-Based Data Delivery in Wide Area and Mobile Environments

An important characteristic of data access in both wide area and mobile environments is that clients often have different preferences for the latency and recency of their data.  Traditional caching and scheduling schemes do not provide interfaces for clients to express these preferences, and treat all client and applications alike.  In this research we develop a data delivery scheme using client profiles, a set of parameters that allow clients to express preferences for their different applications.  A cache uses profiles to determine whether to deliver a cached object to the client or to download a fresh object from a remote server.  We develop an architecture for profiles that is both scalable and straightforward to implement at a cache.  We also study how profiles can be used to support diverse mobile applications. We develop a scheme that uses profiles for both caching and scheduling data delivery on the wireless bandwidth.

For more information: http://www.cs.umd.edu/~bright/research.html

Resource Profiles: Monitoring Resources for Improved Performance

Resource profiles consist of actively gathered information regarding temporal patterns of availability and updates of resources. With such a profile at hand, mediators can optimize query performance using estimated resource availability. The main challenge we face is the immense effort that is needed to maintain resource profiles of some tens of millions of resources. Therefore, brute force solution is replaced with an intelligent algorithm that bases future resource probing on past performance. This work is complementary to the work on profile-based data delivery (http://www.cs.umd.edu/~bright/research.html). Resource profiles serve as the basis for choosing among various policies for pulling servers.

Efficient Algorithms for Computing Resource Availability and Update Patterns

With the immense volume of resources available on the Internet, frequently updated, changed, and eventually expired, efficient and effective information delivery becomes a true challenge. This task is shared by many prevalent applications such as Web crawlers and Web caches. Mediators were proposed in supporting access to objects and provide integration among data sources. We propose to extend mediator's responsibility beyond its typical services, to improve information delivery by estimating resource availability and update patterns. Typically, such services require background information on the server's performance (to measure latency) and availability. The aim of this project is to propose efficient and configurable algorithms for background collection of such knowledge on servers' patterns of behavior (such as regular periodic updates, or abrupt changes), to improve the accuracy and efficiency of information delivery. The verification of some tens of millions of resources and their current values cannot be handled by brute force and requires the use of resource prioritization. For more information contact Avigdor Gal.