Results 1 - 10 of 1,168

CoolStreaming/DONet: A Data-driven Overlay Network for Peer-to-Peer Live Media Streaming

by Xinyan Zhang, Jiangchuan Liu, Bo Li, Tak-shing Peter Yum - in IEEE Infocom , 2005
"... This paper presents DONet, a Data-driven Overlay Network for live media streaming. The core operations in DONet are very simple: every node periodically exchanges data availability information with a set of partners, and retrieves unavailable data from one or more partners, or supplies available dat ..."
Abstract - Cited by 475 (42 self) - Add to MetaCart
directions; and 3) robust and resilient, as the partnerships enable adaptive and quick switching among multi-suppliers. We show through analysis that DONet is scalable with bounded delay. We also address a set of practical challenges for realizing DONet, and propose an efficient member- and partnership

Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing

by Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy Mccauley, Michael J. Franklin, Scott Shenker, Ion Stoica , 2011
"... We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative algo ..."
Abstract - Cited by 239 (27 self) - Add to MetaCart
We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative

Scalable In-Memory Aggregation

by Robert Jan Kopaczyk, Dr. Peter Pietzuch, Prof Alexander Wolf , 2011
"... OLAP (Online Analytical Processing) systems play an important role in many industries today. By aggregating the individual records of a data set, they provide an intuitive multi-dimensional view on the large volumes of data commonly stored by many organisations and are used for the purposes of analy ..."
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larger amounts of data. The goal of this project was to build a prototype of a scalable in-memory aggregator. The result, a system called Simian, is able to distribute data and computation across a cluster of machines,

Scalable In-Memory Computing

by Ru Uta, Andreea S, Stefania Costache, Thilo Kielmann
"... Abstract—Data-intensive scientific workflows are composed of many tasks that exhibit data precedence constraints leading to communication schemes expressed by means of intermediate files. In such scenarios, the storage layer is often a bottleneck, limiting overall application scalability, due to lar ..."
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to large volumes of data being generated during runtime at high I/O rates. To alleviate the storage pressure, applications take advantage of in-memory runtime distributed file systems that act as a fast, distributed cache, which greatly enhances I/O performance. In this paper, we present scalability

Using Restricted Transactional Memory to Build a Scalable In-Memory Database

by unknown authors
"... The recent availability of Intel Haswell processors marks the transition of hardware transactional memory from research toys to mainstream reality. DBX is an in-memory database that uses Intel’s restricted transactional memory (RTM) to achieve high performance and good scalability across multi-core ..."
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The recent availability of Intel Haswell processors marks the transition of hardware transactional memory from research toys to mainstream reality. DBX is an in-memory database that uses Intel’s restricted transactional memory (RTM) to achieve high performance and good scalability across multi

Scalable In-memory RDFS Closure on Billions of Triples

by Eric L. Goodman, David Mizell - In Proc. SSWS, volume 669 of CEUR WS Proceedings
"... Abstract. We present an RDFS closure algorithm, specifically designed and implemented on the Cray XMT supercomputer, that obtains infer-ence rates of 13 million inferences per second on the largest system con-figuration we used. The Cray XMT, with its large global memory (4TB for our experiments), p ..."
Abstract - Cited by 6 (2 self) - Add to MetaCart
inferred triples. The global nature of the hash table allows the algorithm to avoid a common obstacle for dis-tributed memory machines: the creation of duplicate triples. On LUBM data sets ranging between 1.3 billion and 5.3 billion triples, we obtain nearly linear speedup except for two portions: file I

High Performance and Scalability through Application-Tier, In-Memory Data Management

by The Timesten Team, Timesten Performance , 2000
"... TimesTen Performance Software's Front-Tier product is an application-tier data cache that inter -operates with disk-based relational database management systems (RDBMSs) to achieve breakthrough response time and throughput, scalability in transaction load, high availability, and ease of a ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
TimesTen Performance Software's Front-Tier product is an application-tier data cache that inter -operates with disk-based relational database management systems (RDBMSs) to achieve breakthrough response time and throughput, scalability in transaction load, high availability, and ease

Hedera: Dynamic flow scheduling for data center networks

by Mohammad Al-fares, Sivasankar Radhakrishnan, Barath Raghavan, Nelson Huang, Amin Vahdat - In Proc. of Networked Systems Design and Implementation (NSDI) Symposium , 2010
"... Today’s data centers offer tremendous aggregate bandwidth to clusters of tens of thousands of machines. However, because of limited port densities in even the highest-end switches, data center topologies typically consist of multi-rooted trees with many equal-cost paths between any given pair of hos ..."
Abstract - Cited by 223 (7 self) - Add to MetaCart
of hosts. Existing IP multipathing protocols usually rely on per-flow static hashing and can cause substantial bandwidth losses due to longterm collisions. In this paper, we present Hedera, a scalable, dynamic flow scheduling system that adaptively schedules a multi-stage switching fabric to efficiently

Gorilla: A Fast, Scalable, In-Memory Time Series Database

by Tuomas Pelkonen, Scott Franklin, Justin Teller, Paul Cavallaro, Qi Huang, Justin Meza, Kaushik Veeraraghavan
"... Large-scale internet services aim to remain highly available and responsive in the presence of unexpected failures. Pro-viding this service often requires monitoring and analyzing tens of millions of measurements per second across a large number of systems, and one particularly effective solution is ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
is to store and query such measurements in a time series database (TSDB). A key challenge in the design of TSDBs is how to strike the right balance between efficiency, scalability, and relia-bility. In this paper we introduce Gorilla, Facebook’s in-memory TSDB. Our insight is that users of monitoring sys

Transactional Auto Scaler: Elastic Scaling of In-Memory Transactional Data Grids

by Diego Didona, Paolo Romano, Sebastiano Peluso, Francesco Quaglia
"... In this paper we introduce TAS (Transactional Auto Scaler), a system for automating elastic-scaling of in-memory transactional data grids, such as NoSQL data stores or Distributed Transactional Memories. Applications of TAS range from on-line self-optimization of in-production applications to automa ..."
Abstract - Cited by 10 (7 self) - Add to MetaCart
In this paper we introduce TAS (Transactional Auto Scaler), a system for automating elastic-scaling of in-memory transactional data grids, such as NoSQL data stores or Distributed Transactional Memories. Applications of TAS range from on-line self-optimization of in-production applications
Results 1 - 10 of 1,168