New SpiroPlanck Heuristics for High Energy Physics Networking and Future Internet Testbeds

TL;DR

This paper introduces SpiroPlanck heuristics inspired by wireless sensor networks to model and optimize high energy physics research networks and Future Internet testbeds, focusing on scalability and coverage.

Contribution

It proposes a novel framework using SpiroPlanck heuristics for modeling, visualizing, and optimizing dense high-performance research networks and Future Internet testbeds.

Findings

SpiroPlanck heuristics effectively model network coverage.

The framework improves visualization and performance measurement.

Results indicate promising optimization potential for large-scale networks.

Abstract

The need for data intensive Grids, and advanced networks with high performance that support our science has made the High Energy Physics community a leading and a key co-developer of leading edge wide area networks. This paper gives an overview of the status for the world's research networks and major international links used by the high energy physics and other scientific communities, showing some Future Internet testbed architectures, scalability, geographic scope, and extension between networks. The resemblance between wireless sensor network and future internet network, especially in scale consideration as density and network coverage, inspires us to adopt the models of the former to the later. Then we test this assumption to see that this provides a concise working model. This paper collects some heuristics that we call them SpiroPlanck and employs them to model the coverage of dense networks. In this paper, we propose a framework for the operation of FI testbeds containing a test scenario, new representation and visualization techniques, and possible performance measures. Investigations show that it is very promising and could be seen as a good optimization