A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies

Grid is an infrastructure that involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid applications often involve large amounts of data and/or computing resources that require secure resource sharing across organizational boundaries. This makes Grid application management and deployment a complex undertaking. Grid middlewares provide users with seamless computing ability and uniform access to resources in the heterogeneous Grid environment. Several software toolkits and systems have been developed, most of which are results of academic research projects, all over the world. This chapter will focus on four of these middlewares--UNICORE, Globus, Legion and Gridbus. It also presents our implementation of a resource broker for UNICORE as this functionality was not supported in it. A comparison of these systems on the basis of the architecture, implementation model and several other features is included.Comment: 19 pages, 10 figure

Analysis of simulation environment

In this paper the requirements for an ALN simulation environment are analysed, as needed in the CATNETS Project. A number of grid and general purpose simulators are evaluated regarding the identified requirements for simulating economical resource allocation mechanisms in ALNs. Subsequently a suitable simulator is chosen for usage in the CATNETS project. --CATNETS simulator,requirements analysis,simulator selection

Transferring big data across the globe

Transmitting data via the Internet is a routine and common task for users today. The amount of data being transmitted by the average user has dramatically increased over the past few years. Transferring a gigabyte of data in an entire day was normal, however users are now transmitting multiple gigabytes in a single hour. With the influx of big data and massive scientific data sets that are measured in tens of petabytes, a user has the propensity to transfer even larger amounts of data. When transferring data sets of this magnitude on public or shared networks, the performance of all workloads in the system will be impacted. This dissertation addresses the issues and challenges inherent with transferring big data over shared networks. A survey of current transfer techniques is provided and these techniques are evaluated in simulated, experimental and live environments. The main contribution of this dissertation is the development of a new, nice model for big data transfers, which is based on a store-and-forward methodology instead of an end-to-end approach. This nice model ensures that big data transfers only occur when there is idle bandwidth that can be repurposed for these large transfers. The nice model improves overall performance and significantly reduces the transmission time for big data transfers. The model allows for efficient transfers regardless of time zone differences or variations in bandwidth between sender and receiver. Nice is the first model that addresses the challenges of transferring big data across the globe

Fault Tolerant Resource Allocation for Query Processing in Grid Environments

International audienceIn this paper, we propose a new algorithm for fault-tolerant resource allocation for query processing in grid environments. For this, we propose an initial resource allocation algorithm followed by a fault-tolerance protocol. The proposed fault-tolerance protocol is based on the passive replication of stateful operators in queries. We provide theoretical analyses of the proposed algorithms and consolidate our analyses with the simulations

Scalable dimensioning of resilient Lambda Grids

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Dimensionerings- en werkverdelingsalgoritmen voor lambda grids

Grids bestaan uit een verzameling reken- en opslagelementen die geografisch verspreid kunnen zijn, maar waarvan men de gezamenlijke capaciteit wenst te benutten. Daartoe dienen deze elementen verbonden te worden met een netwerk. Vermits veel wetenschappelijke applicaties gebruik maken van een Grid, en deze applicaties doorgaans grote hoeveelheden data verwerken, is het noodzakelijk om een netwerk te voorzien dat dergelijke grote datastromen op betrouwbare wijze kan transporteren. Optische transportnetwerken lenen zich hier uitstekend toe. Grids die gebruik maken van dergelijk netwerk noemt men lambda Grids. Deze thesis beschrijft een kader waarin het ontwerp en dimensionering van optische netwerken voor lambda Grids kunnen beschreven worden. Ook wordt besproken hoe werklast kan verdeeld worden op een Grid eens die gedimensioneerd is. Een groot deel van de resultaten werd bekomen door simulatie, waarbij gebruik gemaakt wordt van een eigen Grid simulatiepakket dat precies focust op netwerk- en Gridelementen. Het ontwerp van deze simulator, en de daarbijhorende implementatiekeuzes worden dan ook uitvoerig toegelicht in dit werk

Distributed Computing Grid Experiences in CMS

The CMS experiment is currently developing a computing system capable of serving, processing and archiving the large number of events that will be generated when the CMS detector starts taking data. During 2004 CMS undertook a large scale data challenge to demonstrate the ability of the CMS computing system to cope with a sustained data-taking rate equivalent to 25% of startup rate. Its goals were: to run CMS event reconstruction at CERN for a sustained period at 25 Hz input rate; to distribute the data to several regional centers; and enable data access at those centers for analysis. Grid middleware was utilized to help complete all aspects of the challenge. To continue to provide scalable access from anywhere in the world to the data, CMS is developing a layer of software that uses Grid tools to gain access to data and resources, and that aims to provide physicists with a user friendly interface for submitting their analysis jobs. This paper describes the data challenge experience with Grid infrastructure and the current development of the CMS analysis system