Performance Analysis of Available Bandwidth Estimation Tools for Grid Networks

Abstract Modern large-scale grid computing systems for processing advanced science and engineering applications rely on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for efficient task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Our experiments consider the accuracy of the estimation, the convergence time, their level of intrusion in the grid links, and the ability to handle multiple simultaneous estimations. Overall, pathload represents a good solution to estimate available bandwidth in grid environments

The Design and Demonstration of the Ultralight Testbed

In this paper we present the motivation, the design, and a recent demonstration of the UltraLight testbed at SC|05. The goal of the Ultralight testbed is to help meet the data-intensive computing challenges of the next generation of particle physics experiments with a comprehensive, network- focused approach. UltraLight adopts a new approach to networking: instead of treating it traditionally, as a static, unchanging and unmanaged set of inter-computer links, we are developing and using it as a dynamic, configurable, and closely monitored resource that is managed from end-to-end. To achieve its goal we are constructing a next-generation global system that is able to meet the data processing, distribution, access and analysis needs of the particle physics community. In this paper we will first present early results in the various working areas of the project. We then describe our experiences of the network architecture, kernel setup, application tuning and configuration used during the bandwidth challenge event at SC|05. During this Challenge, we achieved a record-breaking aggregate data rate in excess of 150 Gbps while moving physics datasets between many Grid computing sites

Network emulation focusing on QoS-Oriented satellite communication

This chapter proposes network emulation basics and a complete case study of QoS-oriented Satellite Communication

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

A First Step Towards Automatically Building Network Representations

To fully harness Grids, users or middlewares must have some knowledge on the topology of the platform interconnection network. As such knowledge is usually not available, one must uses tools which automatically build a topological network model through some measurements. In this article, we define a methodology to assess the quality of these network model building tools, and we apply this methodology to representatives of the main classes of model builders and to two new algorithms. We show that none of the main existing techniques build models that enable to accurately predict the running time of simple application kernels for actual platforms. However some of the new algorithms we propose give excellent results in a wide range of situations