Grid Data Management in Action: Experience in Running and Supporting Data Management Services in the EU DataGrid Project

In the first phase of the EU DataGrid (EDG) project, a Data Management System has been implemented and provided for deployment. The components of the current EDG Testbed are: a prototype of a Replica Manager Service built around the basic services provided by Globus, a centralised Replica Catalogue to store information about physical locations of files, and the Grid Data Mirroring Package (GDMP) that is widely used in various HEP collaborations in Europe and the US for data mirroring. During this year these services have been refined and made more robust so that they are fit to be used in a pre-production environment. Application users have been using this first release of the Data Management Services for more than a year. In the paper we present the components and their interaction, our implementation and experience as well as the feedback received from our user communities. We have resolved not only issues regarding integration with other EDG service components but also many of the interoperability issues with components of our partner projects in Europe and the U.S. The paper concludes with the basic lessons learned during this operation. These conclusions provide the motivation for the architecture of the next generation of Data Management Services that will be deployed in EDG during 2003.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 9 pages, LaTeX, PSN: TUAT007 all figures are in the directory "figures

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

Asynchronous Teams and Tasks in a Message Passing Environment

As the discipline of scientific computing grows, so too does the "skills gap" between the increasingly complex scientific applications and the efficient algorithms required. Increasing demand for computational power on the march towards exascale requires innovative approaches. Closing the skills gap avoids the many pitfalls that lead to poor utilisation of resources and wasted investment. This thesis tackles two challenges: asynchronous algorithms for parallel computing and fault tolerance. First I present a novel asynchronous task invocation methodology for Discontinuous Galerkin codes called enclave tasking. The approach modifies the parallel ordering of tasks that allows for efficient scaling on dynamic meshes up to 756 cores. It ensures high levels of concurrency and intermixes tasks of different computational properties. Critical tasks along domain boundaries are prioritised for an overlap of computation and communication. The second contribution is the teaMPI library, forming teams of MPI processes exchanging consistency data through an asynchronous "heartbeat". In contrast to previous approaches, teaMPI operates fully asynchronously with reduced overhead. It is also capable of detecting individually slow or failing ranks and inconsistent data among replicas. Finally I provide an outlook into how asynchronous teams using enclave tasking can be combined into an advanced team-based diffusive load balancing scheme. Both concepts are integrated into and contribute towards the ExaHyPE project, a next generation code that solves hyperbolic equation systems on dynamically adaptive cartesian grids

Two Major Issues in Data Grid Replication Process

This paper discusses Data Grid as one of popular application of grid and cloud computing. In Data Grid, data are replicated among different nodes of the grid to increase the availability and efficiency. There are two mainissues regarding this replication process; how to do the replication (i.e. where and when to do replication) and how to synchronise all the replicas under heterogeneous database systems in grids to be always consistent. Thispaper explains those two major issues as well as some proposed methods and solutions in order to explore the next problems and challenges on this area. By identifying them, it can be useful to make some improvements inorder to give the best performance on the replication process, and hence can offer a better experience of Data Grid implementation to the users

The Replica Consistency Problem in Data Grids

Fast and reliable data access is a crucial aspect in distributed computing and is often achieved using data replication techniques. In Grid architectures, data are replicated in many nodes of the Grid, and users usually access the "best" replica in terms of availability and network latency. When replicas are modifiable, a change made to one replica will break the consistency with the other replicas that, at that point, become stale. Replica synchronisation protocols exist and are applied in several distributed architectures, for example in distributed databases. Grid middleware solutions provide well established support for replicating data. Nevertheless, replicas are still considered read-only, and no support is provided to the user for updating a replica while maintaining the consistency with the other replicas. In this thesis, done in collaboration with the Italian National Institute of Nuclear Physics (INFN) and the European Organisation for Nuclear Research (CERN), we study the replica consistency problem in Grid computing and propose a service, called CONStanza, that is able to synchronise both files and heterogeneous (different vendors) databases in a Grid environment. We analyse and implement a specific use case that arises in high energy Physics, where conditions databases are replicated using databases of different makes. We provide detailed performance results, and show how CONStanza can be used together with Oracle Streams to provide multitier replication of conditions databases using Oracle and MySQL databases

Handling Confidential Data on the Untrusted Cloud: An Agent-based Approach

Cloud computing allows shared computer and storage facilities to be used by a multitude of clients. While cloud management is centralized, the information resides in the cloud and information sharing can be implemented via off-the-shelf techniques for multiuser databases. Users, however, are very diffident for not having full control over their sensitive data. Untrusted database-as-a-server techniques are neither readily extendable to the cloud environment nor easily understandable by non-technical users. To solve this problem, we present an approach where agents share reserved data in a secure manner by the use of simple grant-and-revoke permissions on shared data.Comment: 7 pages, 9 figures, Cloud Computing 201

Replication and fault-tolerance in real-time systems

PhD ThesisThe increased availability of sophisticated computer hardware and the corresponding decrease in its cost has led to a widespread growth in the use of computer systems for realtime plant and process control applications. Such applications typically place very high demands upon computer control systems and the development of appropriate control software for these application areas can present a number of problems not normally encountered in other applications. First of all, real-time applications must be correct in the time domain as well as the value domain: returning results which are not only correct but also delivered on time. Further, since the potential for catastrophic failures can be high in a process or plant control environment, many real-time applications also have to meet high reliability requirements. These requirements will typically be met by means of a combination of fault avoidance and fault tolerance techniques. This thesis is intended to address some of the problems encountered in the provision of fault tolerance in real-time applications programs. Specifically,it considers the use of replication to ensure the availability of services in real-time systems. In a real-time environment, providing support for replicated services can introduce a number of problems. In particular, the scope for non-deterministic behaviour in real-time applications can be quite large and this can lead to difficultiesin maintainingconsistent internal states across the members of a replica group. To tackle this problem, a model is proposed for fault tolerant real-time objects which not only allows such objects to perform application specific recovery operations and real-time processing activities such as event handling, but which also allows objects to be replicated. The architectural support required for such replicated objects is also discussed and, to conclude, the run-time overheads associated with the use of such replicated services are considered.The Science and Engineering Research Council

Smart PIN: performance and cost-oriented context-aware personal information network

The next generation of networks will involve interconnection of heterogeneous individual networks such as WPAN, WLAN, WMAN and Cellular network, adopting the IP as common infrastructural protocol and providing virtually always-connected network. Furthermore, there are many devices which enable easy acquisition and storage of information as pictures, movies, emails, etc. Therefore, the information overload and divergent content’s characteristics make it difficult for users to handle their data in manual way. Consequently, there is a need for personalised automatic services which would enable data exchange across heterogeneous network and devices. To support these personalised services, user centric approaches for data delivery across the heterogeneous network are also required. In this context, this thesis proposes Smart PIN - a novel performance and cost-oriented context-aware Personal Information Network. Smart PIN's architecture is detailed including its network, service and management components. Within the service component, two novel schemes for efficient delivery of context and content data are proposed: Multimedia Data Replication Scheme (MDRS) and Quality-oriented Algorithm for Multiple-source Multimedia Delivery (QAMMD). MDRS supports efficient data accessibility among distributed devices using data replication which is based on a utility function and a minimum data set. QAMMD employs a buffer underflow avoidance scheme for streaming, which achieves high multimedia quality without content adaptation to network conditions. Simulation models for MDRS and QAMMD were built which are based on various heterogeneous network scenarios. Additionally a multiple-source streaming based on QAMMS was implemented as a prototype and tested in an emulated network environment. Comparative tests show that MDRS and QAMMD perform significantly better than other approaches

teaMPI---replication-based resiliency without the (performance) pain.

In an era where we can not afford to checkpoint frequently, replication is a generic way forward to construct numerical simulations that can continue to run even if hardware parts fail. Yet, replication often is not employed on larger scales, as naïvely mirroring a computation once effectively halves the machine size, and as keeping replicated simulations consistent with each other is not trivial. We demonstrate for the ExaHyPE engine—a task-based solver for hyperbolic equation systems—that it is possible to realise resiliency without major code changes on the user side, while we introduce a novel algorithmic idea where replication reduces the time-to-solution. The redundant CPU cycles are not burned “for nothing”. Our work employs a weakly consistent data model where replicas run independently yet inform each other through heartbeat messages whether they are still up and running. Our key performance idea is to let the tasks of the replicated simulations share some of their outcomes, while we shuffle the actual task execution order per replica. This way, replicated ranks can skip some local computations and automatically start to synchronise with each other. Our experiments with a production-level seismic wave-equation solver provide evidence that this novel concept has the potential to make replication affordable for large-scale simulations in high-performance computing