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Middleware. However, at the same time not all data grids require the same capabilities and services because of differences in access requirements, security and location of resources in comparison to users. In any case, most data grids will have similar middleware services that provide for a universal name space, data transport service, data access service, data replication and resource management service. When taken together, they are key to the data grids functional capabilities. Universal namespace. A universal or unified name space makes it possible to create logical file names (LFNs) that can be referenced within the data grid that map to PFNs. The end user can then choose from the returned results the most appropriate replica to use. This service is usually provided as part of a management system known as a Storage Resource Broker (SRB). Data transport will encompass multiple functions that are not just limited to the transfer of bits, to include such items as fault tolerance and data access. As an example, Grid. FTP provides for fault tolerance by sending data from the last acknowledged byte without starting the entire transfer from the beginning. The data transport service also provides for the low- level access and connections between hosts for file transfer. The data transport service hides the complexity of access and transfer between the different systems to the user so it appears as one unified data source. Data access service. Common forms of security for authentication can include the use of passwords or Kerberos (protocol). Authorization services are the mechanisms that control what the user is able to access after being identified through authentication. Common forms of authorization mechanisms can be as simple as file permissions. However, need for more stringent controlled access to data is done using Access Control Lists (ACLs), Role- Based Access Control (RBAC) and Tasked- Based Authorization Controls (TBAC). The final data access service that might be present to protect the confidentiality of the data transport is encryption. Format GRIB files are a collection of self-contained records of 2D data, and the individual records stand alone as meaningful data, with no references to other records or to an overall schema. So collections of GRIB records can be appended to each other or the. A data grid is an architecture or set of services that gives individuals or groups of users the ability to access, modify and transfer extremely large amounts of geographically distributed data for research purposes. Data grids make this possible through a host of middleware. Ignite UI plays nice with all of your favorite app frameworks, databinding libraries and IDEs. 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This demo illustrates the hierarchical data grid feature of Kendo UI Grid widget. Try the demo source code to play with the grid in your web app. Description There are projects which require to display table data in hierarchical-like fashion, in order to visualize. Authors: Michael Armbrust UC Berkeley Reliable Adaptive Distributed Systems Laboratory (RAD Lab) Armando Fox UC Berkeley Reliable Adaptive Distributed Systems Laboratory (RAD Lab) Rean Griffith UC Berkeley Reliable Adaptive. While all of these access services operate within the data grid, access services within the various administrative domains that host the datasets will still stay in place to enforce access rules. The data grid access services must be in step with the administrative domains access services for this to work. Data replication service. However, replication of datasets and creation of replicas is bound by the availability of storage within sites and bandwidth between sites. The replication and creation of replica datasets is controlled by a replica management system. The replica management system determines user needs for replicas based on input requests and creates them based on availability of storage and bandwidth. In order to perform the tasks undertaken by the replica management system, it needs to be able to manage the underlying storage infrastructure. The data management system will also ensure the timely updates of changes to replicas are propagated to all nodes. Replication update strategy. The updates may be designed around a centralized model where a single master replica updates all others, or a decentralized model, where all peers update each other. If a hierarchy topology is used then updates would flow in a tree like structure through specific paths. In a flat topology it is entirely a matter of the peer relationships between nodes as to how updates take place. In a hybrid topology consisting of both flat and hierarchy topologies updates may take place through specific paths and between peers. Replication placement strategy. If the storage architecture supports replica placement with sufficient site storage, then it becomes a matter of the needs of the users who access the datasets and a strategy for placement of replicas. There is not one universal strategy that fits every requirement the best. It is a matter of the type of data grid and user community requirements for access that will determine the best strategy to use. Replicas can even be created where the files are encrypted for confidentiality that would be useful in a research project dealing with medical files. The data management system keeps track of available storage on all nodes. It also keeps track of requests (hits) for which data clients (users) in a site are requesting. When the number of hits for a specific dataset exceeds the replication threshold it triggers the creation of a replica on the server that directly services the user. If the direct servicing server known as a father does not have sufficient space, then the father. The data management system algorithm also allows for the dynamic deletion of replicas that have a null access value or a value lower than the frequency of the data to be stored to free up space. This improves system performance in terms of response time, number of replicas and helps load balance across the data grid. This method can also use dynamic algorithms that determine whether the cost of creating the replica is truly worth the expected gains given the location. It works on a similar algorithm to dynamic replication with file access requests being a prime factor in determining which files should be replicated. A key difference, however, is the number and frequency of replica creations is keyed to a dynamic threshold that is computed based on request arrival rates from clients over a period of time. As with dynamic replication, the removal of replicas that have a lower threshold that were not created in the current replication interval can be removed to make space for the new replicas. Fair- share replication. Also, like the two before, the popularity of files play a key role in determining which files will be replicated. The difference with this method is the placement of the replicas is based on access load and storage load of candidate servers. Placing a replicate on this candidate could degrade performance for all clients accessing this candidate server. Therefore, placement of replicas with this method is done by evaluating each candidate node for access load to find a suitable node for the placement of the replica. If all candidate nodes are equivalently rated for access load, none or less accessed than the other, then the candidate node with the lowest storage load will be chosen to host the replicas. Similar methods to the other described replication methods are used to remove unused or lower requested replicates if needed. Replicas that are removed might be moved to a parent node for later reuse should they become popular again. Other replication. Below are some others that have been proposed and tested along with the previously described replication strategies. If the threshold is exceeded a replica is created at the first tier down from the root, if the threshold is exceeded again a replica is added to the next tier down and so on like a waterfall effect until a replica is placed at the client itself. Plain Caching . To resolve the problem, majority of systems use extended classic methods of scheduling. Another specificity of data grids, dynamics, consists in the continuous process of connecting and disconnecting of nodes and local load imbalance during an execution of tasks. That can make obsolete or non- optimal results of initial resource allocation for a task. As a result, much of the data grids utilize execution- time adaptation techniques that permit the systems to reflect to the dynamic changes: balance the load, replace disconnecting nodes, use the profit of newly connected nodes, recover a task execution after faults. Resource management system (RMS). It is the heart of the system that manages all actions related to storage resources. In some data grids it may be necessary to create a federated RMS architecture because of different administrative policies and a diversity of possibilities found within the data grid in place of using a single RMS. In such a case the RMSs in the federation will employ an architecture that allows for interoperability based on an agreed upon set of protocols for actions related to storage resources. On the right are four diagrams of various topologies that have been used in data grids. Each of these topologies is further explained below. Federation topology is the choice for institutions that wish to share data from already existing systems. It allows each institution control over their data. When an institution with proper authorization requests data from another institution it is up to the institution receiving the request to determine if the data will go to the requesting institution. The federation can be loosely integrated between institutions, tightly integrated or a combination of both. Monadic topology has a central repository that all collected data is fed into. The central repository then responds to all queries for data. There are no replicas in this topology as compared to others. Data is only accessed from the central repository which could be by way of a web portal. One project that uses this data grid topology is the Network for Earthquake Engineering Simulation (NEES) in the United States. One such project that will benefit from this topology would be CERN that runs the Large Hadron Collider that generates enormous amounts of data. This data is located at one source and needs to be distributed around the world to organizations that are collaborating in the project. |
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