DOCSLIB.ORG

Compsci 514: Computer Networks Lecture 13: Distributed Hash Table

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Compsci 514: Computer Networks Lecture 13: Distributed Hash Table CompSci 514: Computer Networks Lecture 13: Distributed Hash Table Xiaowei Yang Overview • What problems do DHTs solve? • How are DHTs implemented? Background • A hash table is a data structure that stores (key, object) pairs. • Key is mapped to a table index via a hash function for fast lookup. • Content distribution networks – Given an URL, returns the object Example of a Hash table: a web cache http://www.cnn.com0 Page content http://www.nytimes.com ……. 1 http://www.slashdot.org ….. … 2 … … … • Client requests http://www.cnn.com • Web cache returns the page content located at the 1st entry of the table. DHT: why? • If the number of objects is large, it is impossible for any single node to store it. • Solution: distributed hash tables. – Split one large hash table into smaller tables and distribute them to multiple nodes DHT K V K V K V K V A content distribution network • A single provider that manages multiple replicas. • A client obtains content from a close replica. Basic function of DHT • DHT is a virtual hash table – Input: a key – Output: a data item • Data Items are stored by a network of nodes. • DHT abstraction – Input: a key – Output: the node that stores the key • Applications handle key and data item association. DHT: a visual example K V K V (K1, V1) K V K V K V Insert (K1, V1) DHT: a visual example K V K V (K1, V1) K V K V K V Retrieve K1 Desired properties of DHT • Scalability: each node does not keep much state • Performance: look up latency is small • Load balancing: no node is overloaded with a large amount of state • Dynamic reconfiguration: when nodes join and leave, the amount of state moved from nodes to nodes is small. • Distributed: no node is more important than others. A straw man design (0, V1) (3, V2) 0 (1, V3) (2, V5) (4, V4) 1 2 (5, V6) • Suppose all keys are intergers • The number of nodes in the network is n. • id = key % n When node 2 dies (0, V1) 0 (2, V5) (4, V4) (1, V3) (3, V2) (5, V6) 1 • A large number of data items need to be rehashed. Fix: consistent hashing • When a node joins or leaves, the expected fraction of objects that must be moved is the minimum needed to maintain a balanced load. • A node is responsible for a range of keys • All DHTs implement consistent hashing Chord: basic idea • Hash both node id and key into a m-bit one-dimension circular identifier space • Consistent hashing: a key is stored at a node whose identifier is closest to the key in the identifier space – Key refers to both the key and its hash value. Basic components of DHTs • Overlapping key and node identifier space – Hash(www.cnn.com/image.jpg) à a n-bit binary string – Nodes that store the objects also have n-bit string as their identifiers • Building routing tables – Next hops – Distance functions – These two determine the geometry of DHTs • Ring, Tree, Hybercubes, hybrid (tree + ring) etc. – Handle node join and leave • Lookup and store interface Chord: ring topology Key 5 Node 105 K5 N105 K20 Circular 7-bit N32 ID space N90 K80 A key is stored at its successor: node with next higher ID Chord: how to find a node that stores a key? • Solution 1: every node keeps a routing table to all other nodes – Given a key, a node knows which node id is successor of the key – The node sends the query to the successor – What are the advantages and disadvantages of this solution? Solution 2: every node keeps a routing entry to the nodes successor (a linked list) N120 N10 Where is key 80? N105 N90 has K80 N32 K80 N90 N60 Simple lookup algorithm Lookup(my-id, key-id) n = my successor if my-id < n < key-id call Lookup(key-id) on node n // next hop else return my successor // done • Correctness depends only on successors • Q1: will this algorithm miss the real successor? • Q2: whats the average # of lookup hops? Solution 3: Finger table allows log(N)-time lookups ¼ ½ 1/8 1/16 1/32 1/64 1/128 N80 • Analogy: binary search Finger i points to successor of n+2i-1 N120 112 ¼ ½ 1/8 1/16 1/32 1/64 1/128 N80 • A finger table entry includes Chord Id and IP address • Each node stores a small table log(N) Chord finger table example 1 [1,2) 1 Keys: 2 [2,4) 3 5,6 4 [4,0) 0 0 2 [2,3) 3 Keys: 7 1 3 [3,5) 3 1 5 [5,1) 0 6 2 4 [4,5) 0 5 3 Keys: 4 5 [5,7) 0 2 7 [7,3) 0 Lookup with fingers Lookup(my-id, key-id) look in local finger table for highest node n s.t. my-id < n < key-id if n exists call Lookup(key-id) on node n // next hop else return my successor // done 5 N1 lookup(K54) // ask node n to findthe successor of id N8 n.find successor(id) K54 N56 if (id ∈ (n, successor]) N51 return successor; N14 else N48 // forward the query around the circle return successor.findsuccessor(id); N21 N42 (a) N38 N32 (b) Fig. 3. (a) Simple (but slow) pseudocode to findthe successor node of an identifi er id. Remote procedure calls and variable lookups are preceded by the remote node. (b) The path taken by a query from node 8 for key 54, using the pseudocode in Figure 3(a). N1 N1 Finger table lookup(54) N8 +1 N8 + 1 N14 N8 N8 + 2 N14 K54 N56 +2 N8 + 4 N14 N8 + 8 N21 N51 +4 N51 +32 +8 N14 N8 +16 N32 N14 N8 +32 N42 N48 +16 N48 N21 N42 N21 N42 N38 N38 N32 N32 (a) (b) Fig. 4. (a) The fingertable entries for node 8. (b) The path a query for key 54 starting at node 8, using the algorithm in Figure 5. // ask node n to findthe successor of id Notation Defi nition n.find successor(id) finger[k] first node on circle that succeeds (n + if (id ∈ (n, successor]) 2k−1) mod 2m, 1 ≤ k ≤ m return successor; else successor the next node on the identifier circle; n′ = closest preceding node(id); finger[1].node return n′.findsuccessor(id); predecessor the previous node on the identifier circle // search the local table for the highest predecessor of id n.closest preceding node(id) TABLE I for i = m downto 1 Defi nition of variables for node n, using m-bit identifi ers. if (finger[i] ∈ (n, id)) return finger[i]; return n; Fig. 5. Scalable key lookup using the fingertable. The example in Figure 4(a) shows the finger table of node 8. The first finger of node 8 points to node 14, as node 14 is the 0 6 first node that succeeds (8 + 2 ) mod 2 = 9. Similarly, the last tion, extended to use finger tables. If id falls between n and finger of node 8 points to node 42, as node 42 is the first node its successor, find successor is finished and node n returns its 5 6 that succeeds (8 + 2 ) mod 2 = 40. successor. Otherwise, n searches its finger table for the node This scheme has two important characteristics. First, each n′ whose ID most immediately precedes id, and then invokes node stores information about only a small number of other find successor at n′. The reason behind this choice of n′ is that nodes, and knows more about nodes closely following it on the the closer n′ is to id, the more it will know about the identifier identifier circle than about nodes farther away. Second, a node’s circle in the region of id. finger table generally does not contain enough information to As an example, consider the Chord circle in Figure 4(b), and directly determine the successor of an arbitrary key k. For ex- suppose node 8 wants to find the successor of key 54. Since the ample, node 8 in Figure 4(a) cannot determine the successor of largest finger of node 8 that precedes 54 is node 42, node 8 will key 34 by itself, as this successor (node 38) does not appear in ask node 42 to resolve the query. In turn, node 42 will determine node 8’s finger table. the largest finger in its finger table that precedes 54, i.e., node Figure 5 shows the pseudocode of the find successor opera- 51. Finally, node 51 will discover that its own successor, node Chord lookup example • Lookup(1,6) 1 [1,2) 1 Keys: 5,6 • Lookup(1,2) 2 [2,4) 3 4 [4,0) 0 0 2 [2,3) 3 Keys: 7 1 3 [3,5) 3 1 5 [5,1) 0 6 2 4 [4,5) 0 5 3 Keys: 4 5 [5,7) 0 2 7 [7,3) 0 Node join • Maintain the invariant 1.Each nodes successor is correctly maintained 2.For every node k, node successor(k) answers for k. Its desiraBle that finger taBle entries are correct • Each nodes maintains a predecessor pointer • Tasks: – Initialize predecessor and fingers of new node – Update existing nodes state – Notify apps to transfer state to new node Chord Joining: linked list insert N25 N36 1. Lookup(36) N40 K30 K38 • Node n queries a known node n to initialize its state • for its successor: lookup (n) Join (2) N25 2.
Load more

Recommended publications
  • Cisco SCA BB Protocol Reference Guide
    Cisco Service Control Application for Broadband Protocol Reference Guide Protocol Pack #60 August 02, 2018 Cisco Systems, Inc. www.cisco.com Cisco has more than 200 offices worldwide. Addresses, phone numbers, and fax numbers are listed on the Cisco website at www.cisco.com/go/offices. THE SPECIFICATIONS AND INFORMATION REGARDING THE PRODUCTS IN THIS MANUAL ARE SUBJECT TO CHANGE WITHOUT NOTICE. ALL STATEMENTS, INFORMATION, AND RECOMMENDATIONS IN THIS MANUAL ARE BELIEVED TO BE ACCURATE BUT ARE PRESENTED WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. USERS MUST TAKE FULL RESPONSIBILITY FOR THEIR APPLICATION OF ANY PRODUCTS. THE SOFTWARE LICENSE AND LIMITED WARRANTY FOR THE ACCOMPANYING PRODUCT ARE SET FORTH IN THE INFORMATION PACKET THAT SHIPPED WITH THE PRODUCT AND ARE INCORPORATED HEREIN BY THIS REFERENCE. IF YOU ARE UNABLE TO LOCATE THE SOFTWARE LICENSE OR LIMITED WARRANTY, CONTACT YOUR CISCO REPRESENTATIVE FOR A COPY. The Cisco implementation of TCP header compression is an adaptation of a program developed by the University of California, Berkeley (UCB) as part of UCB’s public domain version of the UNIX operating system. All rights reserved. Copyright © 1981, Regents of the University of California. NOTWITHSTANDING ANY OTHER WARRANTY HEREIN, ALL DOCUMENT FILES AND SOFTWARE OF THESE SUPPLIERS ARE PROVIDED “AS IS” WITH ALL FAULTS. CISCO AND THE ABOVE-NAMED SUPPLIERS DISCLAIM ALL WARRANTIES, EXPRESSED OR IMPLIED, INCLUDING, WITHOUT LIMITATION, THOSE OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OR ARISING FROM A COURSE OF DEALING, USAGE, OR TRADE PRACTICE. IN NO EVENT SHALL CISCO OR ITS SUPPLIERS BE LIABLE FOR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, OR INCIDENTAL DAMAGES, INCLUDING, WITHOUT LIMITATION, LOST PROFITS OR LOSS OR DAMAGE TO DATA ARISING OUT OF THE USE OR INABILITY TO USE THIS MANUAL, EVEN IF CISCO OR ITS SUPPLIERS HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
    [Show full text]
  • Peer-To-Peer Systems
    Peer-to-Peer Systems Winter semester 2014 Jun.-Prof. Dr.-Ing. Kalman Graffi Heinrich Heine University Düsseldorf Peer-to-Peer Systems Unstructured P2P Overlay Networks – Unstructured Heterogeneous Overlays This slide set is based on the lecture "Communication Networks 2" of Prof. Dr.-Ing. Ralf Steinmetz at TU Darmstadt Unstructured Heterogeneous P2P Overlays Unstructured P2P Structured P2P Centralized P2P Homogeneous P2P Heterogeneous P2P DHT-Based Heterogeneous P2P 1. All features of 1. All features of 1. All features of 1. All features of 1. All features of Peer-to-Peer Peer-to-Peer Peer-to-Peer Peer-to-Peer Peer-to-Peer included included included included included 2. Central entity is 2. Any terminal 2. Any terminal 2. Any terminal 2. Peers are necessary to entity can be entity can be entity can be organized in a provide the removed without removed without removed hierarchical service loss of loss of without loss of manner 3. Central entity is functionality functionality functionality 3. Any terminal some kind of 3. ! no central 3. ! dynamic central 3. ! No central entity can be index/group entities entities entities removed without database 4. Connections in loss of functionality the overlay are Examples: “fixed” Examples: Examples: § Gnutella 0.6 Examples: Examples: § Napster § Gnutella 0.4 § Fasttrack § Chord • AH-Chord § Freenet § eDonkey § CAN • Globase.KOM § Kademlia from R.Schollmeier and J.Eberspächer, TU München HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
    [Show full text]
  • A Fog Storage Software Architecture for the Internet of Things Bastien Confais, Adrien Lebre, Benoît Parrein
    A Fog storage software architecture for the Internet of Things Bastien Confais, Adrien Lebre, Benoît Parrein To cite this version: Bastien Confais, Adrien Lebre, Benoît Parrein. A Fog storage software architecture for the Internet of Things. Advances in Edge Computing: Massive Parallel Processing and Applications, IOS Press, pp.61-105, 2020, Advances in Parallel Computing, 978-1-64368-062-0. 10.3233/APC200004. hal- 02496105 HAL Id: hal-02496105 https://hal.archives-ouvertes.fr/hal-02496105 Submitted on 2 Mar 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. November 2019 A Fog storage software architecture for the Internet of Things Bastien CONFAIS a Adrien LEBRE b and Benoˆıt PARREIN c;1 a CNRS, LS2N, Polytech Nantes, rue Christian Pauc, Nantes, France b Institut Mines Telecom Atlantique, LS2N/Inria, 4 Rue Alfred Kastler, Nantes, France c Universite´ de Nantes, LS2N, Polytech Nantes, Nantes, France Abstract. The last prevision of the european Think Tank IDATE Digiworld esti- mates to 35 billion of connected devices in 2030 over the world just for the con- sumer market. This deep wave will be accompanied by a deluge of data, applica- tions and services.
    [Show full text]
  • Peer-To-Peer Systems: Taxonomy and Characteristics 1B
    IJCST VOL . 3, Iss UE 2, APR I L - JUNE 2012 ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print) Peer-to-Peer Systems: Taxonomy and Characteristics 1B. Lalitha, 2Dr. Ch. D. V. Subbarao 1Dept. of CSE, JNTUCE, Anantapur, AP, India 2Dept. of CSE, S.V University, Tirupathi, AP, India Abstract Various types of networks include: The limitations of client/server systems became a proof in large scale distributed systems for emerging of peer to peer systems, A. Centralized Networks which is the basis for decentralized distributed computing. In peer Centralized P2P protocols consist of a centralized file list. In this to peer model each node takes both the roles of client and server. model a user can send a query for a file to the centralized server. As a client, it can query and download its wanted data files from The server would then send back a list of peers that have the other nodes (peers) and as a server, it can provide data files to requested file. Once the user chooses which peer to download the other nodes. This paper provides the taxonomy of P2P systems file from the centralized would then facilitate the connection of gives an overview of structured and unstructured P2P systems, the peers then remove itself from the process as illustrated in Fig also discusses the characteristics and applications of peer to peer 1. Examples of centralized networks are Napster and eDonkey systems". in its early stages. Keywords Peer-To-Peer, Distributed Systems, Structured P2P, Unstructured P2P Systems. I. Introduction A Peer-to-Peer (P2P) computing or networking is a distributed application architecture that partitions tasks or workloads between peers.
    [Show full text]
  • 2.3 Blockchain
    POLITECNICO DI TORINO Corso di Laurea Magistrale in Ingegneria Informatica - Data Science Tesi di Laurea Magistrale Supporting the portability of profiles using the blockchain in the Mastodon social network Relatore Candidato prof. Giovanni Squillero Alessandra Rossaro Anno Accademico 2018-2019 École polytechnique de Louvain Supporting the portability of profiles using the blockchain in the Mastodon social network Authors: Alessandra ROSSARO, Corentin SURQUIN Supervisors: Etienne RIVIERE, Ramin SADRE Readers: Lionel DRICOT, Axel LEGAY, Giovanni SQUILLERO Academic year 2018–2019 Master [120] in Computer Science Acknowledgements We would like to thank anyone who made the writing of this thesis possible, directly or indirectly. First of all, we would like to thank our supervisors, Prof. Etienne Riviere and Prof. Ramin Sadre for their continous support and advice during the year. We would never have gone this far without them. Secondly, we thank Lionel Dricot, Prof. Axel Legay and Prof. Giovanni Squillero for accepting to be the readers of this thesis. Alessandra First of all, I would like to thank my family, my parents Claudia and Alberto, my brother Stefano and my sister Eleonora, that from the beginning of my studies believed in me, every time urging me to give more and sustaining me each time that I had difficulties. They are my strength and I feel really lucky to have them in my life. Another thanks is to my friends, to Soraya, Beatrice, Sinto and Stefano and especially to Matteo and Edoardo that each time that I needed, remember me to believe in myself and don’t give up. Thank you, sincerely! I would like to thank also my partner, Corentin, because we were a great team, sometimes with some misunderstandings, but I appreciated to work at this project with him! Corentin I must express my deep gratitude to my family and friends for their moral support.
    [Show full text]
  • Challenges in the Decentralised Web: the Mastodon Case∗
    Challenges in the Decentralised Web: The Mastodon Case∗ Aravindh Raman1, Sagar Joglekar1, Emiliano De Cristofaro2;3, Nishanth Sastry1, and Gareth Tyson3;4 1King’s College London, 2University College London, 3Alan Turing Institute, 4Queen Mary University of London faravindh.raman,sagar.joglekar,[email protected], [email protected], [email protected] Abstract cols to let instances interact and aggregate their users to offer a globally integrated service. The Decentralised Web (DW) has recently seen a renewed mo- DW platforms intend to offer a number of benefits. For ex- mentum, with a number of DW platforms like Mastodon, Peer- ample, data is spread among many independent instances, thus Tube, and Hubzilla gaining increasing traction. These offer al- possibly making privacy-intrusive data mining more difficult. ternatives to traditional social networks like Twitter, YouTube, Data ownership is more transparent, and the lack of centralisa- and Facebook, by enabling the operation of web infrastruc- tion could make the overall system more robust against techni- ture and services without centralised ownership or control. cal, legal or regulatory attacks. Although their services differ greatly, modern DW platforms mostly rely on two key innovations: first, their open source However, these properties may also bring inherent chal- software allows anybody to setup independent servers (“in- lenges that are difficult to avoid, particularly when consid- stances”) that people can sign-up to and use within a local ering the natural pressures towards centralisation in both so- community; and second, they build on top of federation pro- cial [12, 49] and economic [42] systems.
    [Show full text]
  • 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.
    [Show full text]
  • Distributed Systems Security Knowledge Area Issue 1.0
    Distributed Systems Security Knowledge Area Issue 1.0 Neeraj Suri Lancaster University EDITOR Emil Lupu Imperial College London REVIEWERS Konstantin Beznosov University of British Columbia Marko Vukolic´ IBM Research The Cyber Security Body Of Knowledge www.cybok.org COPYRIGHT © Crown Copyright, The National Cyber Security Centre 2019. This information is licensed under the Open Government Licence v3.0. To view this licence, visit: http://www.nationalarchives.gov.uk/doc/open-government-licence/ When you use this information under the Open Government Licence, you should include the following attribution: CyBOK © Crown Copyright, The National Cyber Security Centre 2018, li- censed under the Open Government Licence: http://www.nationalarchives.gov.uk/doc/open- government-licence/. The CyBOK project would like to understand how the CyBOK is being used and its uptake. The project would like organisations using, or intending to use, CyBOK for the purposes of education, training, course development, professional development etc. to contact it at con- [email protected] to let the project know how they are using CyBOK. Issue 1.0 is a stable public release of the Distributed Systems Security Knowledge Area. How- ever, it should be noted that a fully-collated CyBOK document which includes all of the Knowl- edge Areas is anticipated to be released by the end of July 2019. This will likely include up- dated page layout and formatting of the individual Knowledge Areas KA Distributed Systems Security j October 2019 Page 1 The Cyber Security Body Of Knowledge
    [Show full text]
  • Storing and Managing Data in a Distributed Hash Table
    Storing and Managing Data in a Distributed Hash Table by Emil Sit S.B., Computer Science (1999); S.B., Mathematics (1999); M.Eng., Computer Science (2000) Massachusetts Institute of Technology Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of MASSACHUSETTS iNS Doctor of Philosophy in Computer Science OF TEHNOLO at the JUL 0 1 2008 MASSACHUSETTS INSTITUTE OF TECHNOLOGY LIBRARIES June 2008 AC.NV8 @ Massachusetts Institute of Technology 2008. All rights reserved. Author ................ Department of Electrical Engineering and Computer Science 4,A• • tMay 1,2008 Certified by..... WM. Frans Kaashoek Professor / 1l Thesis Supervisor Certified by .......................................... Robert T. Morris Associate Professor Thesis Supervisor Accepted by... ................... Terry P. Orlando Chair, Department Committee on Graduate Students Storing and Managing Data in a Distributed Hash Table by Emil Sit Submitted to the Department of Electrical Engineering and Computer Science on May 1, 2008, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science Abstract Distributed hash tables (DHTs) have been proposed as a generic, robust storage infrastruc- ture for simplifying the construction of large-scale, wide-area applications. For example, UsenetDHT is a new design for Usenet News developed in this thesis that uses a DHT to cooperatively deliver Usenet articles: the DHT allows a set of N hosts to share storage of Usenet articles, reducing their combined storage requirements by a factor of O(N). Usenet generates a continuous stream of writes that exceeds 1 Tbyte/day in volume, comprising over ten million writes. Supporting this and the associated read workload requires a DHT engineered for durability and efficiency.
    [Show full text]
  • Distributed Hash Tables in P2P Systems - a Literary Survey
    Distributed Hash Tables in P2P Systems - A literary survey Timo Tanner Helsinki University of Technology [email protected] Abstract networking, every node in the network has a Globally unique identifier. (GUID) Each node functions as a container for the Distributed Hash Tables (DHT) are algorithms used in mod- distributed data. ern peer-to-peer applications, which provide a reliable, scal- Nodes in this type of P2P network are arranged to a Net- able, fault tolerant and efficient way to manage P2P networks work overlay, which for example dictates how the nodes are in a true peer to peer manner. A lot of literature has been connected and to how many neighbors each node can con- published on the subject to analyze various different DHT nect to. A network overlay is a virtual network that runs algorithms, their properties and performance. on top of another network and can be managed indepen- The purpose of this paper is to find out about recent re- dently. A network overlay can utilize the underlying pro- search conducted on the subject and wrap up a literary survey tocols as seen fit. Some DHTs prefer using connectionless containing a comparison of four different DHT algorithms. UDP whereas others resort to the connection oriented TCP. The algorithms chosen for the survey are Chord, Tapestry, In P2P world, the network overlay functions on the applica- CAN and Kademlia. The comparison concentrates on the tion level. relative efficiencies of the different DHTs, presents them and Distributed Hash Tables promote several ideas that distin- analyzes the factors that affect the efficiency of a Distributed guish them from traditional Client-Server oriented services: Hash Table.
    [Show full text]
  • Distributed Hash Tables and Chord
    Distributed Hash Tables and Chord Hari Balakrishnan 6.829 Fall 2018 October 30, 2018 What is a P2P system? Node Node Node Internet Node Node • A distributed system architecture in which: • There’s no centralized control • Nodes are symmetric in function • Large number of (unreliable) nodes What can P2P teach us about infrastructure design? • Resistant to DoS and failures • Safety in numbers, no single point of failure • Self-assembling • Nodes insert themselves into structure • No manual configuration or oversight • Flexible: nodes can be • Widely distributed or colocated • Powerful hosts or low-end PCs • Each peer brings a little bit to the dance • Aggregate is equivalent to a big distributed server farm behind a fat network pipe General Abstraction? • Big challenge for P2P: finding content • Many machines, must find one that holds data • Not too hard to find hay, but what about needles? • Essential task: lookup(key) • Given key, find host that has data (value) corresponding to that key • Higher-level interface: put(key,val)/get(key) • Easy to layer on top of lookup() • Allows application to ignore details of storage • Good for some apps, not for others Data-centric network abstraction • TCP provides a conversation abstraction socket = connect (IP address, port); send(data on socket); /* goes to IP addr / TCP port */ • A DHT provides a data-centric abstraction as an overlay over the Internet • A key is a semantic-free identifier for data • E.g., key = hash(filename) put(key, value) Distributed application get (key) value (data) DHT Infrastructure DHT layering Distributed application put(key, data) get (key) data Distributed hash table lookup(key) node IP address Lookup service node node ….
    [Show full text]
  • P2P and Bittorrent
    inf5040 - Presentation by group 1 nghial, baardehe, chricar 30.10.08 Goals of today After this lecture you should have a general understanding of what P2P and bittorrent is be able to recognize the main differences of bitTorrent and other P2P networks A way of organizing resource sharing in computer networks What is P2P? Server/client model Peer-to-peer model Characteristics of P2P networks Peers act as equals Peers function as both client and server No central server managing the network No central router Examples of ”pure” P2P networks Gnutella, Freenet (filesharing) In short Decentralization and multirole BUT! Most networks and applications described as P2P actually contain or rely on some non-peer elements History 1970 – SMTP, NNTP (Usenet) One process both server and client IBM, 1984 ”Advanced Peer to Peer Networking” Software for filesharing in a LAN 1990 – IRC (DCC), MBONE One client can both send and receive 1997 – Napster Created a lot of controversy Convicted because of the centralized file indexing Advantages of P2P networks Better performance and reliability compared to server/client scheme Popular resources will be available at several locations Principle of locality -> less delay and faster transmission Overlay routing Application layer routing (middleware) Two ways of searching for files Flooding DHT (Distributed Hash Table) Area of application Mostly used in ad hoc networks Often categorized by what it’s used for Filesharing Media streaming Telephony (skype) Discussion forums Used to distribute
    [Show full text]