Maintaining Replica Consistency Over Large-Scale Data Grid Using Update Propagation Technique

A Data Grid is an organized collection of nodes in a wide area network which contributes to various computation, storage data, and application. In Data Grid high numbers of users are distributed in a wide area environment which is dynamic and heterogeneous. Data management is one of the current issues where data transparency, consistency, fault-tolerance, automatic management and the performance are the user parameters in grid environment. Data management techniques must scale up while addressing autonomy, dynamicity and heterogeneity of the data resource. Data replication is a well known technique used to reduce accesses latency, improve availability and performance in a distributed computing environment. Replication introduces the problem of maintaining consistency among the replicas when files are allowed to be updated. The update information should be propagated to all replicas to guarantee correct read of the remote replicas. An asynchronous replication is a commonly agreed solution for the problem in consistency of replicas. A few studies have been done to maintain replica consistency in Data Grid. However, the introduced techniques are neither efficient nor scalable. They cannot be used in real Data Grid since the issues of large number of replica sites, large scale distribution, load balancing and site autonomy where the capability of grid site to join and leave the grid community at any time have not been addressed. This thesis proposes a new asynchronous replication protocol called Update Propagation Grid (UPG) to maintain replica consistency over a large scale data grid. In UPG the updates reach all on-line secondary replicas using a propagation technique based on nodes organized into a logical structure network in the form of two-dimensional grid structure. The proposed update propagation technique is a hybrid push-pull and dynamic technique that addresses the issues of site autonomy, efficiency, scalability, load balancing and fairness. A two performance analysis studies have been conducted to study the performance of the proposed technique in comparison with other techniques. First study involves mathematical and simulation analysis. Second study is based on Queuing Network Model. The result of the performance analysis shows that the proposed technique scales well with high number of replica sites and with high request loads. The result also shows the reduction on the average update reach time by 5% to 97%. Moreover the result shows that the proposed technique is capable of reaching load balancing while providing update propagation fairnes

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

Data Replication with 2D Mesh Protocol for Data Grid

Data replication is one of the widely approach to achieve high data availability and fault tolerant of a system. Data replication in a large scale distributed and dynamic network such as grid has effects the efficiency of data accessing and data consistency. Therefore a mechanism that can maintain the consistency of the data and provide high data availability is needed. This thesis discusses protocols and strategies of replicating data in distributed database and grid environment where network and users are dynamic. There are few protocols that have been implemented in distributed database and grid computing which is discussed such as Read One-Write All (ROWA), Voting (VT), Tree Quorum (TQ), Grid Configuration (GC), Three Dimensional Grid Structure (TDGS), Diagonal Replication in Grid (DRG) and Neighbor Replication in Grid (NRG). In this thesis, we introduce an enhanced replica control protocol, named Enhance Diagonal Replication 2D Mesh (EDR2M) protocol for grid environment and compares its result of availability, and communication cost with the latest protocol TDGS (2001) and NRG (2007). EDR2M proves data consistency by fulfilling the Quorum Intersection Properties. Evaluations that is suitable and applicability for EDR2M protocol solutions via analytical models and simulations. A simulation of EDR2M protocol is developed and the performance metrics evaluated are data availability, and communication cost. By getting the sufficient number of quorum, number of nodes in each quorum, and selecting the middle node of the diagonal sites to have the copy of the data file have improved the availability and communication cost for read and write operation compared to the latest protocol, TDGS (2001) and NRG (2007). Thus, the experiment has showed scientifically that EDR2M is the adequate protocol to achieve high data availability in a low communication cost by providing replica control protocol for a dynamic network such as grid environmen

Cost and Performance-Based Resource Selection Scheme for Asynchronous Replicated System in Utility-Based Computing Environment

A resource selection problem for asynchronous replicated systems in utility-based computing environment is addressed in this paper. The needs for a special attention on this problem lies on the fact that most of the existing replication scheme in this computing system whether implicitly support synchronous replication and/or only consider read-only job. The problem is undoubtedly complex to be solved as two main issues need to be concerned simultaneously, i.e. 1) the difficulty on predicting the performance of the resources in terms of job response time, and 2) an efficient mechanism must be employed in order to measure the trade-off between the performance and the monetary cost incurred on resources so that minimum cost is preserved while providing low job response time. Therefore, a simple yet efficient algorithm that deals with the complexity of resource selection problem in utility-based computing systems is proposed in this paper. The problem is formulated as a Multi Criteria Decision Making (MCDM) problem. The advantages of the algorithm are two-folds. On one fold, it hides the complexity of resource selection process without neglecting important components that affect job response time. The difficulty on estimating job response time is captured by representing them in terms of different QoS criteria levels at each resource. On the other fold, this representation further relaxed the complexity in measuring the trade-offs between the performance and the monetary cost incurred on resources. The experiments proved that our proposed resource selection scheme achieves an appealing result with good system performance and low monetary cost as compared to existing algorithms

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