Metadata Ranking and Pruning for Failure Detection in Grids

Abstract

The objective of Grid computing is to make processing power as accessible and easy to use as electricity and water. The last decade has seen an unprecedented growth in Grid infrastructures which nowadays enables large-scale deployment of applications in the scientific computation domain. One of the main challenges in realizing the full potential of Grids is making these systems dependable.In this paper we present FailRank, a novel framework for integrating and ranking information sources that characterize failures in a grid system. After the failing sites have been ranked, these can be eliminated from the job scheduling resource pool yielding in that way a more predictable, dependable and adaptive infrastructure. We also present the tools we developed towards evaluating the FailRank framework. In particular, we present the FailBase Repository which is a 38GB corpus of state information that characterizes the EGEE Grid for one month in 2007. Such a corpus paves the way for the community to systematically uncover new, previously unknown patterns and rules between the multitudes of parameters that can contribute to failures in a Grid environment. Additionally, we present an experimental evaluation study of the FailRank system over 30 days which shows that our framework identifies failures in 93% of the cases and can achieve this by only fetching 65% of the available information sources. We believe that our work constitutes another important step towards realizing adaptive Grid computing systems.

Mobility Aware Energy Efficient Congestion Control in Mobile Wireless Sensor Network

“…The advancement in static WSN along with distributed robotics [1] has led to a new set of Mobile WSN (MWSN). MWSNs have same network architecture as WSNs have; however they are provided with explicit or implicit mechanisms that provides mobility to these sensor nodes to move in space (e.g., terrestrial robotic car, underwater, or air current) over time [2]. In addition, MWSNs are able to derive their coordinates through relative means (localization techniques [3]) or absolute means (e.g., geographic positioning system (GPS)).…”

“…In addition, MWSNs are able to derive their coordinates through relative means (localization techniques [3]) or absolute means (e.g., geographic positioning system (GPS)). There are quite a few classes of MWSN that can be plainly categorized into the following classes: (i) highly mobile; in this scenario, devices can move at a velocity such as human, cars, and airplanes, (ii) mostly static; in this scenario, the devices can move at a very low velocity such as moving robots, and (iii) hybrid, in this scenario, we have both classes, that is, highly mobile and mostly static, such as moving cars having sensors installed in it [2].…”

“…TSEEC uses mobility pattern that enables protocol to robustly adjust the mobility pattern, making it suitable for sensor environments suitable for both high and low mobilities. TSEEC assumes that MSNs know their location information by using any localization technique [2]. This location information is used to predict the mobility pattern by using [4].…”

An adaptive mobility aware and energy efficient MAC protocol for wireless sensor networks

“…MSNs have a same architecture to their stationary counterparts, thus MSNs are constrained by the same energy and processing limitations, but they are supplemented with implicit or explicit mechanisms that enable these devices to move in space (e.g. motor or sea/air current) over time [7]. Additionally, MSN devices might derive their coordinates through absolute (e.g.…”

“…localization techniques [8], which enable sensing devices to 978-1-4244-4671-1/09/$25.00 ©2009 IEEE derive their coordinates using signal strength, time difference of arrival or angle of arrival). There are several classes of MSNs which can coarsely be structured into the following classes: i) highly mobile, which contains scenarios in which devices move at high velocities such as cars, human with cell phones, airplanes, and others; ii) mostly static which contains scenarios in which devices move at low velocities such as monitoring sensors in a shop floor with moving robots; and iii) hybrid, which contains both classes such as an airplane that has sensors installed on inside and outside [7].…”