/S >> Log in or Register to post comments; Gold Sponsors . Improving MapReduce performance in heterogeneous environments. Dryad: Distributed data-parallel programs from sequential building blocks. This project is an extended and more modular implementation of timely dataflow in Rust. endobj 2011. >> The model involves a directed (possibly cyclic) graph along whose edges messages (data) flow, and a partially ordered set of timestamps that adorn each message. Our choice of C# and .NET was not especially popular, but at the same time the use of Java is largely antithetical to performant systems building. Naiad is a high-performance distributed implementation of the timely dataflow model, a group of processes host workers that each manage a partition of the timely dataflow vertices. Data Center TCP (DCTCP). /Resources /JavaScript /Transparency Proceedings of the Very Large Database Endowment (PVLDB), 2(1):241–252, Aug. 2009. M. Zaharia, A. Konwinski, A. D.Joseph, R. Katz, and I. Stoica. 2 In Proceedings of the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Oct. 2010. ] 全文. Such an implementation can improve performance inside a loop by allowing fast uncoordinated iteration, at the possible expense of sending multiple messages before the output reaches its final value. http://www.microsoft.com/en-us/sqlserver/solutions-technologies/data-warehousing/pdw.aspx. /St While the absence of a total order means we cannot name an “earliest” pointstamp in our set, we can nonetheless establish a set of pointstamps that will never be seen again once we have delivered their corresponding messages. The Naiad project is an investigation of data-parallel dataflow computation, like Dryad and DryadLINQ, but with a focus on low-latency streaming and cyclic computations. In Proceedings of the 14th Workshop on Hot Topics in Operating Systems (HotOS), May 2013. * Your assessment is very important for improving the work P. A. Tucker, D. Maier, T. Sheard, and L. Fegaras. A new computational model, timely dataflow, underlies Naiad and captures opportunities for parallelism across a wide class of algorithms. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), June 2010. M. Welsh, D. Culler, and E. Brewer. M. Abadi, F. McSherry, D. G. Murray, and T. L. Rodeheffer. of artificial intelligence, which forms the content of this project. Communications of the ACM, 56(2):74–80, Feb. 2013. Y. Zhang, Q. Gao, L. Gao, and C. Wang. Timely dataflow is the name we used to describe Naiad’s dataflow model. Naiad introduces a new computational model, timely dataflow, which combines low-latency asynchronous message flow with lightweight coordination when required. This also allows a much simpler implementation of “impersonation”, an oft commented-out Naiad feature which accelerates coordination when it is known that some edges will not exchange data. % ���� This allows tighter coordination when appropriate, for example when workers on a machine want to aggregate values before transmitting them. The Naiad paper has two contributions to the distributed systems design community: a generalized model for high throughput iterative and low latency streaming computation called timely dataflow and an efficient practical implementation of this model called Naiad. obj /Type << /Nums 2012. It also results in not requiring dynamically allocated memory for the core timestamp types. 10 Conclusion. Supports common high-level programming interfaces (e.g. Faster than SOTA batch/streaming frameworks. stream Declarative networking: language, execution and optimization. ($Q�܃CsQ���;u+���3�K+7�X���~�^뎋������� It offers the high throughput of batch processors, the low latency of stream processo. 科研动态. Large-scale incremental processing using distributed transactions and notifications. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. /Creator 0 In Proceedings of the ACM/IEEE Supercomputing Conference (SC), Nov. 2004. RFC 896, Jan. 1984. << This project is something akin to a distributed data-parallel compute engine, which scales the same program up from a single thread on your laptop to distributed execution across a cluster of computers. /S << D. D. Clark. Progress tracking in Naiad is essentially distributed reference counting. It offers the high throughput of batch processors, the low latency of stream processors, and the ability to perform iterative and incremental computations. Solving the straggler problem with bounded staleness. obj 立即登录. While integers are still likely to be popular, this allows types like DateTime at the root scope, (uint, uint) priorities (faked out in Naiad), and Vec
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