The Impact of Data Replicatino on Job Scheduling Performance in Hierarchical data Grid

In data-intensive applications data transfer is a primary cause of job execution delay. Data access time depends on bandwidth. The major bottleneck to supporting fast data access in Grids is the high latencies of Wide Area Networks and Internet. Effective scheduling can reduce the amount of data transferred across the internet by dispatching a job to where the needed data are present. Another solution is to use a data replication mechanism. Objective of dynamic replica strategies is reducing file access time which leads to reducing job runtime. In this paper we develop a job scheduling policy and a dynamic data replication strategy, called HRS (Hierarchical Replication Strategy), to improve the data access efficiencies. We study our approach and evaluate it through simulation. The results show that our algorithm has improved 12% over the current strategies.Comment: 11 pages, 7 figure

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

Next-Generation EU DataGrid Data Management Services

We describe the architecture and initial implementation of the next-generation of Grid Data Management Middleware in the EU DataGrid (EDG) project. The new architecture stems out of our experience and the users requirements gathered during the two years of running our initial set of Grid Data Management Services. All of our new services are based on the Web Service technology paradigm, very much in line with the emerging Open Grid Services Architecture (OGSA). We have modularized our components and invested a great amount of effort towards a secure, extensible and robust service, starting from the design but also using a streamlined build and testing framework. Our service components are: Replica Location Service, Replica Metadata Service, Replica Optimization Service, Replica Subscription and high-level replica management. The service security infrastructure is fully GSI-enabled, hence compatible with the existing Globus Toolkit 2-based services; moreover, it allows for fine-grained authorization mechanisms that can be adjusted depending on the service semantics.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla,Ca, USA, March 2003 8 pages, LaTeX, the file contains all LaTeX sources - figures are in the directory "figures

Efficient HTTP based I/O on very large datasets for high performance computing with the libdavix library

Remote data access for data analysis in high performance computing is commonly done with specialized data access protocols and storage systems. These protocols are highly optimized for high throughput on very large datasets, multi-streams, high availability, low latency and efficient parallel I/O. The purpose of this paper is to describe how we have adapted a generic protocol, the Hyper Text Transport Protocol (HTTP) to make it a competitive alternative for high performance I/O and data analysis applications in a global computing grid: the Worldwide LHC Computing Grid. In this work, we first analyze the design differences between the HTTP protocol and the most common high performance I/O protocols, pointing out the main performance weaknesses of HTTP. Then, we describe in detail how we solved these issues. Our solutions have been implemented in a toolkit called davix, available through several recent Linux distributions. Finally, we describe the results of our benchmarks where we compare the performance of davix against a HPC specific protocol for a data analysis use case.Comment: Presented at: Very large Data Bases (VLDB) 2014, Hangzho

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

Data as a Service (DaaS) for sharing and processing of large data collections in the cloud

Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains.Peer ReviewedPostprint (author's final draft

HEP Applications Evaluation of the EDG Testbed and Middleware

Workpackage 8 of the European Datagrid project was formed in January 2001 with representatives from the four LHC experiments, and with experiment independent people from five of the six main EDG partners. In September 2002 WP8 was strengthened by the addition of effort from BaBar and D0. The original mandate of WP8 was, following the definition of short- and long-term requirements, to port experiment software to the EDG middleware and testbed environment. A major additional activity has been testing the basic functionality and performance of this environment. This paper reviews experiences and evaluations in the areas of job submission, data management, mass storage handling, information systems and monitoring. It also comments on the problems of remote debugging, the portability of code, and scaling problems with increasing numbers of jobs, sites and nodes. Reference is made to the pioneeering work of Atlas and CMS in integrating the use of the EDG Testbed into their data challenges. A forward look is made to essential software developments within EDG and to the necessary cooperation between EDG and LCG for the LCG prototype due in mid 2003.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics Conference (CHEP03), La Jolla, CA, USA, March 2003, 7 pages. PSN THCT00