Shared resource

About: Shared resource is a research topic. Over the lifetime, 7536 publications have been published within this topic receiving 123491 citations. The topic is also known as: network share.

Implementing Journaling in a Linux Shared Disk File System

Abstract: In computer systems today, speed and responsiveness is often determined by network and storage subsystem performance. Faster, more scalable networking interfaces like Fibre Channel and Gigabit Ethernet provide the scaffolding from which higher performance computer systems implementations may be constructed, but new thinking is required about how machines interact with network-enabled storage devices. In this paper we describe how we implemented journaling in the Global File System (GFS), a shared-disk, cluster file system for Linux. Our previous three papers on GFS at the Mass Storage Symposium discussed our first three GFS implementations, their performance, and the lessons learned. Our fourth paper describes, appropriately enough, the evolution of GFS version 3 to version 4, which supports journaling and recovery from client failures. In addition, GFS scalability tests extending to 8 machines accessing 8 4-disk enclosures were conducted: these tests showed good scaling. We describe the GFS cluster infrastructure, which is necessary for proper recovery from machine and disk failures in a collection of machines sharing disks using GFS. Finally, we discuss the suitability of Linux for handling the big data requirements of supercomputing centers.

Real-time streamed data download system and method

Abstract: Near real-time transfer of a datafile from a first computer to a second computer over a computer network. These computers are operated such that on the first computer a server side script, responsive to a download request from the second computer, operable to launch an httpstreamproducer and to read and write data over the computer network. The httpstreamproducer is operable to read a designated source file and simultaneously write data from the source file into a return-data-buffer connected to the server-side script and a read-while-write mechanism allowing the httpstreamproducer to read data from the designated source file while the designated source file is being written. The second computer has a transaction handler class, each instance of which is operable to read and write data produced by an httpstreamproducer over the computer network and to write blocks of data to a destination simultaneously with receiving data from the computer network.

Virtual Partitioning by Dynamic Priorities: Fair and Efficient Resource-Sharing by Several Services

Abstract: We propose a scheme for sharing an unbuffered resource, such as bandwidth or capacity, by various services The scheme assigns a nominal capacity to each service class and implements a form of virtual partitioning by means of state-dependent priorities That is, instead of each class of traffic having a fixed priority, as in traditional trunk reservation schemes, the priorities depend on the state of the system An approximate method of analysis based on fixed point equations is given Numerical results are obtained from the approximation, exact computations and simulations The results show that the scheme is robust, fair and efficient

Identifying and analyzing researchers on twitter

Abstract: For millions of users Twitter is an important communication platform, a social network, and a system for resource sharing. Likewise, scientists use Twitter to connect with other researchers, announce calls for papers, or share their thoughts. Filtering tweets, discovering other researchers, or finding relevant information on a topic of interest, however, is difficult since no directory of researchers on Twitter exists.In this paper we present an approach to identify Twitter accounts of researchers and demonstrate its utility for the discipline of computer science. Based on a seed set of computer science conferences we collect relevant Twitter users which we can partially map to ground-truth data. The mapping is leveraged to learn a model for classifying the remaining. To gain first insights into how researchers use Twitter, we empirically analyze the identified users and compare their age, popularity, influence, and social network.