No abstract

Abstract

We present the Whirlpool Routing Protocol (WARP), which efficiently routes data to a node moving within a static mesh. The key insight in WARP's design is that data traffic can use an existing routing gradient to efficiently probe the topology, repair the routing gradient, and communicate these repairs to nearby nodes.Using simulation, controlled testbeds, and real mobility experiments, we find that using the data plane for topology maintenance is highly effective due to the incremental nature of mobility updates. WARP leverages the fact that converging flows at a destination make the destination have the region of highest traffic. We provide a theoretical basis for WARP's behavior, defining an "update area" in which the topology must adjust when a destination moves. As long as packets arrive at a destination before it moves outside of the update area, WARP can repair the topology using the data plane. Compared to existing protocols, such as DYMO and HYPER, WARP's packet drop rate is up to 90% lower while sending up to 90% fewer packets.

In this paper we describe JClarens; a Java based implementation of the Clarens remote data server. JClarens provides web services for an interactive analysis environment to dynamically access and analyze the tremendous amount of data scattered across various locations. Additionally this research is aimed to develop a service oriented Grid Enabled Portal (GEP) that provides interface and access to several Grid services to give a homogeneous and optimized view of the distributed and heterogeneous environment. Other than showing platform independent behavior provided by Java, the use of XML-RPC based Web Services enabled JClarens to be a language neutral server and demonstrated interoperability with its Python variant. Extreme care has been taken in the usage and manipulation of various Java libraries to cater the needs of high performance computing. The overall exercise has yielded in a prototype with strong emphasis on security and virtual organization management (VOM). This shall provide a common platform to support development of larger, more flexible framework with future aims to integrate it with a loosely coupled, decentralized, and autonomous framework for Grid enabled Analysis Environment (GAE).

No abstract

No abstract

Selecting optimal resources for submitting jobs on a computational Grid or accessing data from a data grid is one of the most important tasks of any

High Energy Physics