Donut: a Robust Distributed Hash Table Based on Chord
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Discretized Streams: Fault-Tolerant Streaming Computation at Scale
Discretized Streams: Fault-Tolerant Streaming Computation at Scale" Matei Zaharia, Tathagata Das, Haoyuan Li, Timothy Hunter, Scott Shenker, Ion Stoica University of California, Berkeley Published in SOSP ‘13 Slide 1/30 Motivation" • Faults and stragglers inevitable in large clusters running “big data” applications. • Streaming applications must recover from these quickly. • Current distributed streaming systems, including Storm, TimeStream, MapReduce Online provide fault recovery in an expensive manner. – Involves hot replication which requires 2x hardware or upstream backup which has long recovery time. Slide 2/30 Previous Methods" • Hot replication – two copies of each node, 2x hardware. – straggler will slow down both replicas. • Upstream backup – nodes buffer sent messages and replay them to new node. – stragglers are treated as failures resulting in long recovery step. • Conclusion : need for a system which overcomes these challenges Slide 3/30 • Voila ! D-Streams Slide 4/30 Computation Model" • Streaming computations treated as a series of deterministic batch computations on small time intervals. • Data received in each interval is stored reliably across the cluster to form input datatsets • At the end of each interval dataset is subjected to deterministic parallel operations and two things can happen – new dataset representing program output which is pushed out to stable storage – intermediate state stored as resilient distributed datasets (RDDs) Slide 5/30 D-Stream processing model" Slide 6/30 What are D-Streams ?" • sequence of immutable, partitioned datasets (RDDs) that can be acted on by deterministic transformations • transformations yield new D-Streams, and may create intermediate state in the form of RDDs • Example :- – pageViews = readStream("http://...", "1s") – ones = pageViews.map(event => (event.url, 1)) – counts = ones.runningReduce((a, b) => a + b) Slide 7/30 High-level overview of Spark Streaming system" Slide 8/30 Recovery" • D-Streams & RDDs track their lineage, that is, the graph of deterministic operations used to build them. -
The Design and Implementation of Declarative Networks
The Design and Implementation of Declarative Networks by Boon Thau Loo B.S. (University of California, Berkeley) 1999 M.S. (Stanford University) 2000 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor Joseph M. Hellerstein, Chair Professor Ion Stoica Professor John Chuang Fall 2006 The dissertation of Boon Thau Loo is approved: Professor Joseph M. Hellerstein, Chair Date Professor Ion Stoica Date Professor John Chuang Date University of California, Berkeley Fall 2006 The Design and Implementation of Declarative Networks Copyright c 2006 by Boon Thau Loo Abstract The Design and Implementation of Declarative Networks by Boon Thau Loo Doctor of Philosophy in Computer Science University of California, Berkeley Professor Joseph M. Hellerstein, Chair In this dissertation, we present the design and implementation of declarative networks. Declarative networking proposes the use of a declarative query language for specifying and implementing network protocols, and employs a dataflow framework at runtime for com- munication and maintenance of network state. The primary goal of declarative networking is to greatly simplify the process of specifying, implementing, deploying and evolving a network design. In addition, declarative networking serves as an important step towards an extensible, evolvable network architecture that can support flexible, secure and efficient deployment of new network protocols. Our main contributions are as follows. First, we formally define the Network Data- log (NDlog) language based on extensions to the Datalog recursive query language, and propose NDlog as a Domain Specific Language for programming network protocols. -
Apache Spark: a Unified Engine for Big Data Processing
contributed articles DOI:10.1145/2934664 for interactive SQL queries and Pregel11 This open source computing framework for iterative graph algorithms. In the open source Apache Hadoop stack, unifies streaming, batch, and interactive big systems like Storm1 and Impala9 are data workloads to unlock new applications. also specialized. Even in the relational database world, the trend has been to BY MATEI ZAHARIA, REYNOLD S. XIN, PATRICK WENDELL, move away from “one-size-fits-all” sys- TATHAGATA DAS, MICHAEL ARMBRUST, ANKUR DAVE, tems.18 Unfortunately, most big data XIANGRUI MENG, JOSH ROSEN, SHIVARAM VENKATARAMAN, applications need to combine many MICHAEL J. FRANKLIN, ALI GHODSI, JOSEPH GONZALEZ, different processing types. The very SCOTT SHENKER, AND ION STOICA nature of “big data” is that it is diverse and messy; a typical pipeline will need MapReduce-like code for data load- ing, SQL-like queries, and iterative machine learning. Specialized engines Apache Spark: can thus create both complexity and inefficiency; users must stitch together disparate systems, and some applica- tions simply cannot be expressed effi- ciently in any engine. A Unified In 2009, our group at the Univer- sity of California, Berkeley, started the Apache Spark project to design a unified engine for distributed data Engine for processing. Spark has a programming model similar to MapReduce but ex- tends it with a data-sharing abstrac- tion called “Resilient Distributed Da- Big Data tasets,” or RDDs.25 Using this simple extension, Spark can capture a wide range of processing workloads that previously needed separate engines, Processing including SQL, streaming, machine learning, and graph processing2,26,6 (see Figure 1). -
A Distributed, Cooperative Citeseer
OverCite: A Distributed, Cooperative CiteSeer Jeremy Stribling, Jinyang Li,† Isaac G. Councill,†† M. Frans Kaashoek, Robert Morris MIT Computer Science and Artificial Intelligence Laboratory †New York University and MIT CSAIL, via UC Berkeley ††Pennsylvania State University Abstract common for non-commercial Web sites to be popular yet to lack the resources to support their popularity. Users of CiteSeer is a popular online resource for the computer sci- such sites are often willing to help out, particularly in the ence research community, allowing users to search and form of modest amounts of compute power and network browse a large archive of research papers. CiteSeer is ex- traffic. Examples of applications that thrive on volunteer pensive: it generates 35 GB of network traffic per day, re- resources include SETI@home, BitTorrent, and volunteer quires nearly one terabyte of disk storage, and needs sig- software distribution mirror sites. Another prominent ex- nificant human maintenance. ample is the PlanetLab wide-area testbed [36], which is OverCite is a new digital research library system that made up of hundreds of donated machines over many dif- aggregates donated resources at multiple sites to provide ferent institutions. Since donated resources are distributed CiteSeer-like document search and retrieval. OverCite en- over the wide area and are usually abundant, they also al- ables members of the community to share the costs of run- low the construction of a more fault tolerant system using ning CiteSeer. The challenge facing OverCite is how to a geographically diverse set of replicas. provide scalable and load-balanced storage and query pro- In order to harness volunteer resources at many sites, a cessing with automatic data management. -
A Ditya a Kella
A D I T Y A A K E L L A [email protected] Computer Science Department http://www.cs.cmu.edu/∼aditya Carnegie Mellon University Phone: 412-818-3779 5000 Forbes Avenue Fax: 412-268-5576 (Attn: Aditya Akella) Pittsburgh, PA 15232 Education PhD in Computer Science May 2005 Carnegie Mellon University, Pittsburgh, PA (expected) Dissertation: “An Integrated Approach to Optimizing Internet Performance” Advisor: Prof. Srinivasan Seshan Bachelor of Technology in Computer Science and Engineering May 2000 Indian Institute of Technology (IIT), Madras, India Honors and Awards IBM PhD Fellowship 2003-04 & 2004-05 Graduated third among all IIT Madras undergraduates 2000 Institute Merit Prize, IIT Madras 1996-97 19th rank, All India Joint Entrance Examination for the IITs 1996 Gold medals, Regional Mathematics Olympiad, India 1993-94 & 1994-95 National Talent Search Examination (NTSE) Scholarship, India 1993-94 Research Interests Computer Systems and Internetworking PhD Dissertation An Integrated Approach to Optimizing Internet Performance In my thesis research, I adopted a systematic approach to understand how to optimize the perfor- mance of well-connected Internet end-points, such as universities, large enterprises and data centers. I showed that constrained bottlenecks inside and between carrier networks in the Internet could limit the performance of such end-points. I observed that a clever end point-based strategy, called Mul- tihoming Route Control, can help end-points route around these bottlenecks, and thereby improve performance. Furthermore, I showed that the Internet's topology, routing and trends in its growth may essentially worsen the wide-area congestion in the future. To this end, I proposed changes to the Internet's topology in order to guarantee good future performance. -
Distributed Hash Tables CS6450: Distributed Systems Lecture 12
Distributed Hash Tables CS6450: Distributed Systems Lecture 12 Ryan Stutsman Material taken/derived from Princeton COS-418 materials created by Michael Freedman and Kyle Jamieson at Princeton University. Licensed for use under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. 1 Some material taken/derived from MIT 6.824 by Robert Morris, Franz Kaashoek, and Nickolai Zeldovich. Consistency models Linearizability Causal Eventual Sequential 2 Recall use of logical clocks • Lamport clocks: C(a) < C(z) Conclusion: None • Vector clocks: V(a) < V(z) Conclusion: a → … → z • Distributed bulletin board application • Each post gets sent to all other users • Consistency goal: No user to see reply before the corresponding original message post • Conclusion: Deliver message only after all messages that causally precede it have been delivered 3 Causal Consistency 1. Writes that are potentially P1 P2 P3 causally related must be seen by a all machines in same order. f b c 2. Concurrent writes may be seen d in a different order on different e machines. g • Concurrent: Ops not causally related Physical time ↓ Causal Consistency Operations Concurrent? P1 P2 P3 a, b N a f b, f Y b c c, f Y d e, f Y e e, g N g a, c Y a, e N Physical time ↓ Causal Consistency: Quiz Causal Consistency: Quiz • Valid under causal consistency • Why? W(x)b and W(x)c are concurrent • So all processes don’t (need to) see them in same order • P3 and P4 read the values ‘a’ and ‘b’ in order as potentially causally related. -
Scooped, Again
Scooped, Again The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Ledlie, Jonathan, Jeff Shneidman, Margo Seltzer, and John Huth. 2003. Scooped, again. Peer-to-peer systems II: Second international workshop, IPTPS 2003, Berkeley, California, February 21-22, 2003, ed. IPTPS 2003, Frans Kaashoek, and Ion Stoica. Berlin: Springer Verlang. Previously published in Lecture Notes in Computer Science 2735: 129-138. Published Version http://dx.doi.org/10.1007/b11823 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:2799042 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Scooped, Again Jonathan Ledlie, Jeff Shneidman, Margo Seltzer, John Huth Harvard University jonathan,jeffsh,margo ¡ @eecs.harvard.edu [email protected] Abstract p2p Grid Users (scientists) users Users (disparate The Peer-to-Peer (p2p) and Grid infrastructure commu- groups ) ©§ nities are tackling an overlapping set of problems. In ad- App. ¢¤£¦¥§¥§¨writers place demand demand dressing these problems, p2p solutions are usually moti- demand (e.g., on vated by elegance or research interest. Grid researchers, App. writers OGSA) writers} under pressure from thousands of scientists with real file research and Infrastructure writers Infrastructure writers sharing and computational needs, are pooling their solu- development tions from a wide range of sources in an attempt to meet user demand. Driven by this need to solve large scientific Figure 1: In serving their well-defined user base, the problems quickly, the Grid is being constructed with the Grid community has needed to draw from both its ances- tools at hand: FTP or RPC for data transfer, centraliza- try of supercomputing and from Systems research (in- tion for scheduling and authentication, and an assump- cluding p2p and distributed computing). -
Alluxio: a Virtual Distributed File System by Haoyuan Li A
Alluxio: A Virtual Distributed File System by Haoyuan Li A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Ion Stoica, Co-chair Professor Scott Shenker, Co-chair Professor John Chuang Spring 2018 Alluxio: A Virtual Distributed File System Copyright 2018 by Haoyuan Li 1 Abstract Alluxio: A Virtual Distributed File System by Haoyuan Li Doctor of Philosophy in Computer Science University of California, Berkeley Professor Ion Stoica, Co-chair Professor Scott Shenker, Co-chair The world is entering the data revolution era. Along with the latest advancements of the Inter- net, Artificial Intelligence (AI), mobile devices, autonomous driving, and Internet of Things (IoT), the amount of data we are generating, collecting, storing, managing, and analyzing is growing ex- ponentially. To store and process these data has exposed tremendous challenges and opportunities. Over the past two decades, we have seen significant innovation in the data stack. For exam- ple, in the computation layer, the ecosystem started from the MapReduce framework, and grew to many different general and specialized systems such as Apache Spark for general data processing, Apache Storm, Apache Samza for stream processing, Apache Mahout for machine learning, Ten- sorflow, Caffe for deep learning, Presto, Apache Drill for SQL workloads. There are more than a hundred popular frameworks for various workloads and the number is growing. Similarly, the storage layer of the ecosystem grew from the Apache Hadoop Distributed File System (HDFS) to a variety of choices as well, such as file systems, object stores, blob stores, key-value systems, and NoSQL databases to realize different tradeoffs in cost, speed and semantics. -
D3.5 Design and Implementation of P2P Infrastructure
INNOVATION ACTION H2020 GRANT AGREEMENT NUMBER: 825171 WP3 – Vision Materialisation and Technical Infrastructure D3.5 – Design and implementation of P2P infrastructure Document Info Contractual Delivery Date: 31/12/2019 Actual Delivery Date: 31/12/2019 Responsible Beneficiary: INOV Contributing Beneficiaries: UoG Dissemination Level: Public Version: 1.0 Type: Final This project has received funding from the European Union’s H2020 research and innovation programme under the grant agreement No 825171 ` DOCUMENT INFORMATION Document ID: D3.5: Design and implementation of P2P infrastructure Version Date: 31/12/2019 Total Number of Pages: 36 Abstract: This deliverable describes the EUNOMIA P2P infrastructure, its design, the EUNOMIA P2P APIs and a first implementation of the P2P infrastructure and the corresponding APIs made available for the remaining EUNOMIA modules to start integration (for the 1st phase). Keywords: P2P design, P2P infrastructure, IPFS AUTHORS Full Name Beneficiary / Organisation Role INOV INESC INOVAÇÃO INOV Overall Editor University of Greenwich UoG Contributor REVIEWERS Full Name Beneficiary / Organisation Date University of Nicosia UNIC 23/12/2019 VERSION HISTORY Version Date Comments 0.1 13/12/2019 First internal draft 0.6 22/12/2019 Complete draft for review 0.8 29/12/2019 Final draft following review 1.0 31/12/2019 Final version to be released to the EC Type of deliverable PUBLIC Page | ii H2020 Grant Agreement Number: 825171 Document ID: WP3 / D3.5 EXECUTIVE SUMMARY This deliverable describes the P2P infrastructure that has been implemented during the first phase of the EUNOMIA to provide decentralized support for storage, communication and security functions. It starts with a review of the main existing P2P technologies, where each one is analysed and a selection of candidates are selected to be used in the project. -
UC Berkeley UC Berkeley Electronic Theses and Dissertations
UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Scalable Systems for Large Scale Dynamic Connected Data Processing Permalink https://escholarship.org/uc/item/9029r41v Author Padmanabha Iyer, Anand Publication Date 2019 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Scalable Systems for Large Scale Dynamic Connected Data Processing by Anand Padmanabha Iyer A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Ion Stoica, Chair Professor Scott Shenker Professor Michael J. Franklin Professor Joshua Bloom Fall 2019 Scalable Systems for Large Scale Dynamic Connected Data Processing Copyright 2019 by Anand Padmanabha Iyer 1 Abstract Scalable Systems for Large Scale Dynamic Connected Data Processing by Anand Padmanabha Iyer Doctor of Philosophy in Computer Science University of California, Berkeley Professor Ion Stoica, Chair As the proliferation of sensors rapidly make the Internet-of-Things (IoT) a reality, the devices and sensors in this ecosystem—such as smartphones, video cameras, home automation systems, and autonomous vehicles—constantly map out the real-world producing unprecedented amounts of dynamic, connected data that captures complex and diverse relations. Unfortunately, existing big data processing and machine learning frameworks are ill-suited for analyzing such dynamic connected data and face several challenges when employed for this purpose. This dissertation focuses on the design and implementation of scalable systems for dynamic connected data processing. We discuss simple abstractions that make it easy to operate on such data, efficient data structures for state management, and computation models that reduce redundant work. -
Why Kad Lookup Fails Hun J
Why Kad Lookup Fails Hun J. Kang, Eric Chan-Tin, Nicholas J. Hopper, Yongdae Kim University of Minnesota - Twin Cities {hkang,dchantin,hopper,kyd}@cs.umn.edu Abstract retrieve the information. Even worse, 25% of searching peers fail to locate the information 10 hours after storing A Distributed Hash Table (DHT) is a structured over- the information. This poor performance is due to inconsis- lay network service that provides a decentralized lookup for tency between storing and searching lookups; Kad lookups mapping objects to locations. In this paper, we study the for the same objects map to an inconsistent set of nodes. lookup performance of locating nodes responsible for repli- From our measurement, only 18% of replica roots located cated information in Kad – one of the largest DHT networks by storing and searching lookups are the same on average. existing currently. Throughout the measurement study, we Moreover, this lookup inconsistency causes an inefficient found that Kad lookups locate only 18% of nodes storing use of resources. We also find that 45% of replica roots are replicated data. This failure leads to limited reliability and never located and thus used by any searching peers for rare an inefficient use of resources during lookups. Ironically, objects. Furthermore, when many peers search for popular we found that this poor performance is due to the high level information stored by many peers, 85% of replica roots are of routing table similarity, despite the relatively high churn never used and only a small number of the roots suffer the rate in the network. We propose solutions which either ex- burden of most requests. -
On the Feasibility of Peer-To-Peer Web Indexing and Search
University of Pennsylvania ScholarlyCommons Departmental Papers (CIS) Department of Computer & Information Science October 2003 On the Feasibility of Peer-to-Peer Web Indexing and Search Jiyang Li Massachusetts Institute of Technology Boon Thau Loo University of Pennsylvania, [email protected] Joseph M. Hellerstein University of California M Frans Kaashoek Massachusetts Institute of Technology David Karger Massachusetts Institute of Technology See next page for additional authors Follow this and additional works at: https://repository.upenn.edu/cis_papers Recommended Citation Jiyang Li, Boon Thau Loo, Joseph M. Hellerstein, M Frans Kaashoek, David Karger, and Robert Morris, "On the Feasibility of Peer-to-Peer Web Indexing and Search", . October 2003. Postprint version. Published in Lecture Notes in Computer Science, Volume 2735, Peer-to-Peer Systems II, 2003, pages 207-215. Publisher URL: http://dx.doi.org/10.1007/b11823 NOTE: At the time of publication, author Boon Thau Loo was affiliated with the University of California at Berkeley. Currently (April 2007), he is a faculty member in the Department of Computer and Information Science at the University of Pennsylvania. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_papers/328 For more information, please contact [email protected]. On the Feasibility of Peer-to-Peer Web Indexing and Search Abstract This paper discusses the feasibility of peer-to-peer full-text keyword search of the Web. Two classes of keyword search techniques are in use or have been proposed: flooding of queries vo er an overlay network (as in Gnutella), and intersection of index lists stored in a distributed hash table.