14,037 research outputs found

    Communication Facilities for Distributed TransactionProcessing Systems

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    istributed transaction-processing systems must manage such functions as concurrency, recovery, and replication. One way to improve their efficiency and reliability is to increase software modularity, which means the separate components should execute in separate address spaces to permit hardware-enforced separation. This structure offers advantages but demands efficient interprocess communication (IPC) services. In our research at Purdue University, we are investigating mechanisms and paradigms for efficient communication support in conventional architectures, such as virtual-memory, single-processor machines with no special IPC hardware support. (Some mainframes have hardware assistance where more than one address space can be accessed at the same time.) We are studying communication designs in the context of the Raid system, a robust and adaptable distributed database system for transaction processing.' Raid has been developed at Purdue on Sun workstations under the Unix operating svstem in a local area network. Communication software is critical in distributed computing systems. This research identifies efficient mechanisms and paradigms for distributed transaction processing in a replicated database environment. In Raid, each major logical component is implemented as a server, which is a process in a separate address space. Servers interact with other processes through a high-level communication subsystem. Currently, Raid has six servers for transaction management: the user interface (UI). the action driver (AD), the access manager (AM), the atomicity controller (AC), the concurrency controller (CC), and the replication controller (RC). High-level name service is provided by a separate server, the oracle. Raid's communication software, called Raidcomm, has evolved as a result of the knowledge we gained from other systems and from our own experiments, which are summarized in the following sections. The first version, Raidcomm V.l, was developed in 1986. Implemented on top of the SunOS socket-based IPC mechanism using UDP/IP (User Datagram Protocol/Internet Protocol), it provides a clean, location-independent interface between the servers.' To permit defining server interfaces in terms of arbitrary data structures, we used Sun's external data representation standard, XDR. We developed Raidcomm V.2 in 1990 to provide multicasting support for the AC and RC servers. We designed Raidcomm V.3 t

    Multi-Layer Distributed Storage of LHD Plasma Diagnostic Database

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    At the end of LHD experimental campaign in 2003, the amount of whole plasma diagnostics raw data had reached 3.16 GB in a long-pulse experiment. This is a new world record in fusion plasma experiments, far beyond the previous value of 1.5 GB/shot. The total size of the LHD diagnostic data is about 21.6 TB for the whole six years of experiments, and it continues to grow at an increasing rate. The LHD diagnostic database and storage system, i.e. the LABCOM system, has a completely distributed architecture to be sufficiently flexible and easily expandable to maintain integrity of the total amount of data. It has three categories of the storage layer: OODBMS volumes in data acquisition servers, RAID servers, and mass storage systems, such as MO jukeboxes and DVD-R changers. These are equally accessible through the network. By data migration between them, they can be considered a virtual OODB extension area. Their data contents have been listed in a “facilitator” PostgreSQL RDBMS, which now contains about 6.2 million entries, and informs the optimized priority to clients requesting data. Using the “glib” compression for all of the binary data and applying the three-tier application model for the OODB data transfer/retrieval, an optimized OODB read-out rate of 1.7 MB/s and effective client access speed of 3?25 MB/s have been achieved. As a result, the LABCOM data system has succeeded in combination of the use of RDBMS, OODBMS, RAID, and MSS to enable a virtual and always expandable storage volume, simultaneously with rapid data access

    The CDF Data Handling System

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    The Collider Detector at Fermilab (CDF) records proton-antiproton collisions at center of mass energy of 2.0 TeV at the Tevatron collider. A new collider run, Run II, of the Tevatron started in April 2001. Increased luminosity will result in about 1~PB of data recorded on tapes in the next two years. Currently the CDF experiment has about 260 TB of data stored on tapes. This amount includes raw and reconstructed data and their derivatives. The data storage and retrieval are managed by the CDF Data Handling (DH) system. This system has been designed to accommodate the increased demands of the Run II environment and has proven robust and reliable in providing reliable flow of data from the detector to the end user. This paper gives an overview of the CDF Run II Data Handling system which has evolved significantly over the course of this year. An outline of the future direction of the system is given.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 7 pages, LaTeX, 4 EPS figures, PSN THKT00

    MongoDB Performance In The Cloud

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    Web applications are growing at a staggering rate every day. As web applications keep getting more complex, their data storage requirements tend to grow exponentially. Databases play an important role in the way web applications store their information. Mongodb is a document store database that does not have strict schemas that RDBMs require and can grow horizontally without performance degradation. MongoDB brings possibilities for different storage scenarios and allow the programmers to use the database as a storage that fits their needs, not the other way around. Scaling MongoDB horizontally requires tens to hundreds of servers, making it very difficult to afford this kind of setup on dedicated hardware. By moving the database into the cloud, this opens up a possibility for low cost virtual machine instances at reasonable prices. There are many cloud services to choose from and without testing performance on each one, there is very little information out there. This paper provides benchmarks on the performance of MongoDB in the cloud
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