Scalable Membership Management and Failure Detection (Dependability) INF5360 Student Presentation by Morten Lindeberg

Size: px
Start display at page:

Download "Scalable Membership Management and Failure Detection (Dependability) INF5360 Student Presentation by Morten Lindeberg mglindeb@ifi.uio."

Transcription

1 Scalable Membership Management and Failure Detection (Dependability) INF5360 Student Presentation by Morten Lindeberg

2 Outline! Membership Management! Gossip Based Membership Protocol [1]! CYCLON [2]! SCAMP [3]! Failure Detection! Newcast [4]

3 Group Membership 1! How to make sure you can reach every group member with multicast.! Need at least some sort of global knowledge of who is in the group! Problem of dynamicity: What to do when a node joins or leaves the group

4 Group Membership 2! Problem of scalability: Updating n nodes with delivery guarantee typically takes a linear amount of messages (m p = 2(n - 1)) from node P. And all group members have to be stored in a list on all nodes! Problem of fault tolerance: What happens if the initiator node P crashes during its reliable multicast?! Answer: Gossiping: m p and number of nodes in routing tables on a sub-linear / logarithmic scale! General scheme:! Node P chooses a random set of nodes R s as gossiping targets, with size k! log (n).! Upon a retrieval of an event that needs to be propagated: send the event to (only) all nodes in the set R s

5 Gossip Based Membership 3! Achieved properties:! Delivery guarantee is probabilistic! Fault tolerance is high because of the randomness property of choosing gossiping targets! Reliability achieved because of the possibility of achieving redundancy by setting the number of nodes in the gossip group large! From [3]:! If n nodes gossips to k = log(n) + m other nodes, then the probability of all other nodes getting a message converges to e -e^(-k)! With n = 10,000,000 and m = 5! Gossip group size k = log (10,000,000) + 5 = 12! Then the probability p = e -e^(-12) =

6 Gossip Based Membership Protocol 1! Each node maintains a small local view of the membership with size k! These local views is chosen in order to obtain properties of a k-connected graph. (Basically avoid partitioning)! Each node periodically updates its local views in rounds! Join: Copy the view of some random node! Leave: Nodes is not expected to leave gracefully, and there are no heartbeat or keepalive messages A connected graph: where there is a path from any point to any point in the graph, whereas a k-connected graph is a graph in which the graph is still connected although k nodes are removed [Weisstein, Eric W. "Connected Graph." From MathWorld--A Wolfram Web Resource.]

7 Gossip Based Membership Protocol 2! Protocol modes (decides when nodes are to perform their updates on the local views): 1. Synchronous: All nodes are updated simultaneously 2. Loosely synchronous: nodes are updated sequentially in random order 3. Asynchronous: n nodes are chosen uniformly at random! Dangling edges created in the graph when a node leaves is fixed during the already mentioned rounds

8 Gossip Based Membership Protocol 3! The new local view is created in two parts of the protocol: 1. Mixing: Node n pulls the views of several nodes in order to keep the graph from partitioning. f (fanout) is the number of nodes to pull view from. The views are kept in the list L 1 2. Reinforcement: When other nodes pulls the view of n, n learns that they still exist and puts them in the list L 2. Edges pointing to dead nodes are removed from the graph 3. Local view creation: Choose k distinct elements from L 1 and L 2. The parameter weight of reinforcement w decides witch of the lists L 1 and L 2 to favor

9 Gossip Based Membership Protocol 4! Results:! How may iterations does it take before single nodes is disconnected because only having dangling edges?! Number of nodes: n! View size: k = log 2 n! Churn: n/32 Because curve is linearly increasing, the authors claims the system scales well. [Figure from Allavena et al[1]]

10 Gossip Based Membership Protocol 5! Conclusion:! Algorithm for local view maintenance without requiring uniformly random views! Authors claim their results show strong guarantees offered by their proofs! Future work:! Maintain timestamps in local views, and base random choices (communication partner and replacement) on the timestamp

11 CYCLON 1! Motivation:! Content based searching in P2P overlays (essentially a membership management issue)! Characteristics and requirements:! High churn rate! Resilience to massive node failures! Membership management should be inexpensive! Disruptions caused by joining and leaving nodes should be efficiently corrected! Essentially: Exploit randomness to disseminate information across a large et of nodes [2]

12 CYCLON 2! Protocol Basics:! Basic shuffling: 1. Peer P choose a random subset of l (l = shuffle length) neighbors, and a random peer Q, from each of its neighbors peer sets 2. Replace Q s address with P s address 3. Send updated subset to Q 4. Receive Q s subset 5. P discard entries pointing to itself, and duplicates 6. P updates its set to include all remaining entries, and replacing those originally sent to Q. [Figure from Voulgaris et al [2]]

13 CYCLON 3! Enhanced Shuffling! Introduces timestamps as mentioned as future work in Allavena et al. [1]! In the cache holding the peer set, an age attribute is also kept.! The node Q in P s neighbor's subset is not chosen randomly; rather the one that was earliest injected into the network is chosen Note that when P has initiated a shuffling operations with Q, P is Q s neighbor, while P is not a neighbor of Q anymore

14 CYCLON 4! Properties of shuffling (based upon experiments):! Connectivity! In a fail-free environment connectivity is guaranteed! Convergence:! Average path length between two nodes => small value! Clustering coefficient over all nodes => small value " Clustering coefficient = existing neighbor links / total possible links. " Should be small: 1. High value increases chance of partitioning 2. Not optimal for information dissemination because of message redundancy

15 CYCLON 5! Degree Distribution! Robustness, efficient information dissemination and load balancing implies that we want a balanced, uniform distribution of links across all nodes in the overlay! Degree is the number of links a node has to other nodes! Results from CYCLON: experience shows that Enhanced shuffling outperforms both Basic shuffling and Random graph, in case of degree distribution! Shuffle length! Only affected the number of cycles before a converged state

16 CYCLON 6! Adding a node:! Presume that a introducer node Q is known! Node P joins with the help of Q, that adds the address of P in some other nodes neighbor set, chosen through random walk.! Removing a node:! No distinction between nodes disconnecting gracefully or not. There are no heartbeat messages either! Dead-link detection is used to remove cache pollution (dead nodes in a node sets).! There exist a timeout when initiating the shuffle method. Unresponding nodes are assumed disconnected! Additionally, the age field is used indirectly, since nodes injected earlier will be chosen over new ones. Thus old dead ones will eventually be discovered to be in a disconnected state

17 CYCLON 7! Robustness:! Experiment suggest CYCLON is capable of repairing an overlay after a serious disaster.

18 CYCLON 8! Bandwidth! Per cycle, a node experience total traffic of 40 * l bytes! Claimed to be inexpensive (e.g. l = 8 and cycle rate!t = 10 s results in 32 bytes/s of bandwidth consumption)! Example Application Usage:! Semantic-based content search! Handle flash crowds (document requests)! Managing P2P routing tables (Newcast)

19 CYCLON 9! Conclusion:! Claimed to be:! Highly scalable! Very robust! Completely decentralized! Improvement of basic shuffling technique! But what about security?! Authors suspect that CYCLON is attack resilient because of its randomness properties

20 SCAMP 1! SCAMP: Scalable probabilistic membership protocol! Aim:! Avoid to need to know the system size n (decentralized operation)! View size k to adapts to desired value as n increases (scalability)! Mechanism to recover isolated nodes

21 SCAMP 2! Basic Protocol! Subscription (a node is joining) 1. New node P sends a request to arbitrary member Q 2. Upon retrieval, Q forwards to P s id to all its view members, e.g including R 3. Upon retrieval, R inserts P s id in its view with a probability of p r, if not, P s id is forwarded to a random node in R s view. (If it is inserted, PartialView (k) increases.) 4. Nodes keep to views: PartialView (nodes to send gossip to) and InView (nodes to retrieve gossip from). The size of the partial view depend on the design parameter c, that can be used to ensure stronger reliability by increasing the view size k > log (n)! Un-subscription (a node is leaving) 1. Tell every node in it its InView that it is leaving 2. Message includes nodes from PartialView 3. The InView nodes basically replace un-subscribing nodes with nodes from the un-subscribing nodes PartialView What about faulty nodes or non-gracefully exists? See next slide!

22 SCAMP 3! Detect and recovery from isolation (Faulty nodes, or nodes not gracefully existing can cause isolated nodes) 1. Every node periodically sends heartbeat messages 2. Nodes discover that they are isolated if it does not receive heartbeat messages for a long time 3. Isolated nodes simply re-subscribes

23 SCAMP 4! Rebalancing The Graph! When nodes join e.g, choosing the same arbitrary node to send join requests to, this will eventually cause the graph of connected nodes (through views) to be unbalanced! Mechanism to avoid this:! Indirection " The node Q that receives the init request from P does not necessarily perform the initial task of including it in its view. Instead it passes its request to other random nodes according to the forwarding rule! Lease mechanism " Nodes can only hold a subscription for a finite amount of time (leasing) " When lease is out, it has to re-subscribe to a node chosen randomly in its their partial view

24 SCAMP 5! Results from SCAMP experiments:! View size: Results show that the mean value of k! " log(n) yields message delivery with high probability! Resilience to node failures: Results confirmed (not surprisingly) the remarkable fault-tolerance of gossip-based scheme

25 SCAMP 6! Rebalancing the graph! Lease mechanism increase probability of delivery! Indirection (where a single node was responsible the arbitrary chosen one for all joining nodes) helped maintaining low partial view sizes (k)

26 Outline! Membership Management! Gossip Based Membership Protocol [1]! CYCLON [2]! P2P Membership Management for Gossip-Based Protocols [3]! Failure Detection! Newscast [4]

27 Failure Detection! Provide input to monitoring systems! Fast and reliable information dissemination (e.g., propagation of alarms)! Motivation: The growth of the Internet! The Internet is:! Huge! Heterogeneous! Unreliable! Dynamic

28 Newscast 1! Aim: Aggregate (find statistics e.g., extreme values, averages, sums etc.) information in a large and dynamic distributed environment in a robust and dependable manner! Distributed Aggregation: Compute aggregates of some value in a distributed way! Basic idea (epidemic-based aggregation): 1. Each node n i maintains a single number x i. (x i could for instance be memory consumption of node n i ) 2. Every node n i selects a random node n k, and sends its value x i to n k 3. n k responds with the aggregate (e.g. max(x i,x k ) ) of the incoming and its own value 4. Aggregate values will converge exponentially to the overall value incase the aggregation is a average function, and superexponentially incase of a maximum function

29 Newscast 2! Taxonomy: c is almost the same as k in terms of the other systems! Agent: the component on the network nodes! Correspondent: basic entity that runs the newscast protocol! Protocol: 1. Correspondent maintains a fixes-size cache of c news 2. When agent passes a news item to its correspondent, the latter timestamps the item and adds its own network address and caches it 3. Every!T, the correspondent: 1. Requests fresh news from local agent 2. Randomly select accessible peer and asks for information exchange 3. Send all cache entries, which in turn is returned (at most c). 4. Passes received entries from peer agent to local agent 5. Merge entries into local cache 6. Throws away oldest one to keep c fresh ones 4. Clock synchronization is not need for all peers, but the timestamps in single cache s should be consistent: 1. When A sends its cache to B, it also sends its local time. 2. When B retrieves cache it normalizes the time of each new entry to those already cached. (Communication time assumed to be smaller than!t)

30 Newscast 3! Membership management! Indirectly since correspondent addresses is disseminated with news items submitted by the agents! Subscription:! Correspondent initialize cache with at least one known correspondent, which has to be a member of the group! Un-subscription:! Treated as failures (simply just stop communicating)! Because of the timestamps, outdated information is quickly forgotten

31 Newscast 4 [Jelasity et al. [4]]! Wanted properties: 1. Self-organizing 2. Effective 3. Scalable 4. Robust! Not surprisingly (they claim to present empirical evidence that it is)! The average path length converge to a fixed value when experiments with highly dynamic node behavior were performed. This implies that Newscast can withstand dynamic node behavior with efficient information dissemination! Removing random nodes, does not cause other nodes to be disconnected with n = 50 and cache size c = 80! Communication costs: 2*c every!t

32 Conclusion System Motivation Joining (balancing the graph) Handle missing nodes Leave Group / view size (k) Gossip based membership protocol [1] Membership management Arbitrary known node. Balancing:! Mixing (L 1 )! Re-inforcement (L 2 ) Periodic rounds (no heartbeats):! Synchronous! Loosely synchronous! Asynchronous mode Not expected to leave gracefully Fixed: k = log(n) + m view = p 1 *L 1 + p 2 *L 2 CYCLON [2] Membership management Arbitrary known node. Balancing: Random walk! Basic shuffling! Enhanced shuffling w/timeout removes cache pollution Not expected to leave gracefully Fixed: l SCAMP [3] Membership management! Forwarding rule! PartialView! InView Heartbeat messages and lease mechanism Send message to InView (Opportunistic) Dynamic based upon an algorithm that sets k! log(n). Increase reliability with c factor Newcast [4] Failure Detection (distributed aggregation) Arbitrary known node. Graph balancing not issued? Timestamps removes outdated information. (Clock synchronization issued) Not expected to leave gracefully Fixed cache size (refers to news items): c

33 References [1] Allavena, A., Demers, A., and Hopcroft, J. E Correctness of a gossip based membership protocol. In Proceedings of the Twenty-Fourth Annual ACM Symposium on Principles of Distributed Computing (Las Vegas, NV, USA, July 17-20, 2005). PODC '05. [2] Spyros Voulgaris, Daniela Gavidia, and Maarten Steen. CYCLON: Inexpensive Membership Management for Unstructured P2P Overlays. Journal of Network and Systems Management, Volume 13, Number 2, June 2005, pp (21). Springer. [3] Ganesh, A. J., Kermarrec, A., and Massoulié, L Peer-to-Peer Membership Management for Gossip-Based Protocols. IEEE Trans. Comput. 52, 2 (Feb. 2003), [4] Mark Jelasity, Wojtek Kowalczyk and Maarten van Steen. Newscast Computing. Internal report IR-CS-006 Vrije Universiteit Amsterdam, Department of Computer Science, November 2003.

CYCLON: Inexpensive Membership Management for Unstructured P2P Overlays

CYCLON: Inexpensive Membership Management for Unstructured P2P Overlays CYCLON: Inexpensive Membership Management for Unstructured P2P Overlays RUNNING HEAD: [the same as the title] Spyros Voulgaris 1, Daniela Gavidia 2, Maarten van Steen 3 Contact author: Spyros Voulgaris

More information

Monitoring Large-Scale Wireless Sensor Networks

Monitoring Large-Scale Wireless Sensor Networks Monitoring Large-Scale Wireless Sensor Networks using Epidemic Protocols Daniela Gavidia, Spyros Voulgaris, Maarten van Steen Vrije Universiteit Amsterdam Faculty of Science Department of Computer Science

More information

Correctness of a Gossip Based Membership Protocol

Correctness of a Gossip Based Membership Protocol Correctness of a Gossip Based Membership Protocol André Allavena andre@cs.cornell.edu Alan Demers ademers@cs.cornell.edu Department of Computer Science Cornell University Ithaca NY 4853 John E. Hopcroft

More information

Adapting Distributed Hash Tables for Mobile Ad Hoc Networks

Adapting Distributed Hash Tables for Mobile Ad Hoc Networks University of Tübingen Chair for Computer Networks and Internet Adapting Distributed Hash Tables for Mobile Ad Hoc Networks Tobias Heer, Stefan Götz, Simon Rieche, Klaus Wehrle Protocol Engineering and

More information

IMPACT OF DISTRIBUTED SYSTEMS IN MANAGING CLOUD APPLICATION

IMPACT OF DISTRIBUTED SYSTEMS IN MANAGING CLOUD APPLICATION INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND SCIENCE IMPACT OF DISTRIBUTED SYSTEMS IN MANAGING CLOUD APPLICATION N.Vijaya Sunder Sagar 1, M.Dileep Kumar 2, M.Nagesh 3, Lunavath Gandhi

More information

ON THE CORRECTNESS OF GOSSIP-BASED MEMBERSHIP PROTOCOLS

ON THE CORRECTNESS OF GOSSIP-BASED MEMBERSHIP PROTOCOLS ON THE CORRECTNESS OF GOSSIP-BASED MEMBERSHIP PROTOCOLS A Dissertation Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of

More information

Distributed Computing over Communication Networks: Topology. (with an excursion to P2P)

Distributed Computing over Communication Networks: Topology. (with an excursion to P2P) Distributed Computing over Communication Networks: Topology (with an excursion to P2P) Some administrative comments... There will be a Skript for this part of the lecture. (Same as slides, except for today...

More information

Facebook: Cassandra. Smruti R. Sarangi. Department of Computer Science Indian Institute of Technology New Delhi, India. Overview Design Evaluation

Facebook: Cassandra. Smruti R. Sarangi. Department of Computer Science Indian Institute of Technology New Delhi, India. Overview Design Evaluation Facebook: Cassandra Smruti R. Sarangi Department of Computer Science Indian Institute of Technology New Delhi, India Smruti R. Sarangi Leader Election 1/24 Outline 1 2 3 Smruti R. Sarangi Leader Election

More information

Balancing Gossip Exchanges in Networks with Firewalls

Balancing Gossip Exchanges in Networks with Firewalls Balancing Gossip Exchanges in Networks with Firewalls João Leitão INESC-ID/IST jleitao@gsd.inesc-id.pt Robbert van Renesse Cornell University rvr@cs.cornell.edu Luís Rodrigues INESC-ID/IST ler@ist.utl.pt

More information

Anonymous Communication in Peer-to-Peer Networks for Providing more Privacy and Security

Anonymous Communication in Peer-to-Peer Networks for Providing more Privacy and Security Anonymous Communication in Peer-to-Peer Networks for Providing more Privacy and Security Ehsan Saboori and Shahriar Mohammadi Abstract One of the most important issues in peer-to-peer networks is anonymity.

More information

Proactive gossip-based management of semantic overlay networks

Proactive gossip-based management of semantic overlay networks CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. 2007; 19:2299 2311 [Version: 2002/09/19 v2.02] Proactive gossip-based management of semantic overlay networks Spyros

More information

Araneola: A Scalable Reliable Multicast System for Dynamic Environments

Araneola: A Scalable Reliable Multicast System for Dynamic Environments Araneola: A Scalable Reliable Multicast System for Dynamic Environments Roie Melamed IBM Haifa Research Laboratory Idit Keidar EE Department, Technion A preliminary version of this paper appears in Proceedings

More information

The PageRank Citation Ranking: Bring Order to the Web

The PageRank Citation Ranking: Bring Order to the Web The PageRank Citation Ranking: Bring Order to the Web presented by: Xiaoxi Pang 25.Nov 2010 1 / 20 Outline Introduction A ranking for every page on the Web Implementation Convergence Properties Personalized

More information

Design and Implementation of a P2P Cloud System

Design and Implementation of a P2P Cloud System Design and Implementation of a P2P Cloud Ozalp Babaoglu, Moreno Marzolla, Michele Tamburini {babaoglu, marzolla}@cs.unibo.it, miketambu@gmail.com Our Goal Assemble a Cloud out of individual devices E.g.

More information

International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 349 ISSN 2229-5518

International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 349 ISSN 2229-5518 International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 349 Load Balancing Heterogeneous Request in DHT-based P2P Systems Mrs. Yogita A. Dalvi Dr. R. Shankar Mr. Atesh

More information

A Case for Dynamic Selection of Replication and Caching Strategies

A Case for Dynamic Selection of Replication and Caching Strategies A Case for Dynamic Selection of Replication and Caching Strategies Swaminathan Sivasubramanian Guillaume Pierre Maarten van Steen Dept. of Mathematics and Computer Science Vrije Universiteit, Amsterdam,

More information

Video Streaming with Network Coding

Video Streaming with Network Coding Video Streaming with Network Coding Kien Nguyen, Thinh Nguyen, and Sen-Ching Cheung Abstract Recent years have witnessed an explosive growth in multimedia streaming applications over the Internet. Notably,

More information

EPIDEMIC-BASED SELF-ORGANIZATION IN PEER-TO-PEER SYSTEMS SPYROS VOULGARIS

EPIDEMIC-BASED SELF-ORGANIZATION IN PEER-TO-PEER SYSTEMS SPYROS VOULGARIS EPIDEMIC-BASED SELF-ORGANIZATION IN PEER-TO-PEER SYSTEMS SPYROS VOULGARIS COPYRIGHT c 2006 BY SPYROS VOULGARIS VRIJE UNIVERSITEIT EPIDEMIC-BASED SELF-ORGANIZATION IN PEER-TO-PEER SYSTEMS ACADEMISCH PROEFSCHRIFT

More information

Proactive gossip-based management of semantic overlay networks

Proactive gossip-based management of semantic overlay networks CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. 2007; 19:2299 2311 Published online 21 August 2007 in Wiley InterScience (www.interscience.wiley.com)..1225 Proactive

More information

Evolution of Peer-to-Peer Systems

Evolution of Peer-to-Peer Systems EE 657 Lecture 9 on Sept. 28, 2007 Evolution of Peer-to-Peer Systems Peer-To-Peer Computing: Part 1 : P2P Platforms, Overlay Networks, and Gnutella Prof. kai Hwang University of Southern California Taylor

More information

A SURVEY OF P2P OVERLAYS IN VARIOUS NETWORKS

A SURVEY OF P2P OVERLAYS IN VARIOUS NETWORKS A SURVEY OF P2P OVERLAYS IN VARIOUS Mrs. A. Anitha Dr. J. JayaKumari Department of computer science & engineering Department of Electronics & communication Engineering anidathi@yahoo.co.in jkumaribharat@yahoo.com

More information

Server Consolidation in Clouds through Gossiping

Server Consolidation in Clouds through Gossiping Server Consolidation in Clouds through Gossiping Moreno Marzolla, Ozalp Babaoglu, Fabio Panzieri Università di Bologna, Dipartimento di Scienze dell Informazione Mura A. Zamboni 7, I-40127 Bologna, Italy

More information

Dr Markus Hagenbuchner markus@uow.edu.au CSCI319. Distributed Systems

Dr Markus Hagenbuchner markus@uow.edu.au CSCI319. Distributed Systems Dr Markus Hagenbuchner markus@uow.edu.au CSCI319 Distributed Systems CSCI319 Chapter 8 Page: 1 of 61 Fault Tolerance Study objectives: Understand the role of fault tolerance in Distributed Systems. Know

More information

Web Email DNS Peer-to-peer systems (file sharing, CDNs, cycle sharing)

Web Email DNS Peer-to-peer systems (file sharing, CDNs, cycle sharing) 1 1 Distributed Systems What are distributed systems? How would you characterize them? Components of the system are located at networked computers Cooperate to provide some service No shared memory Communication

More information

Brewer s Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services

Brewer s Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services Brewer s Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services Seth Gilbert Nancy Lynch Abstract When designing distributed web services, there are three properties that

More information

Group Membership for Peer-to-Peer Communication

Group Membership for Peer-to-Peer Communication Group Membership for Peer-to-Peer Communication Roberto Baldoni and Sara Tucci Piergiovanni Dipartimento di Informatica e Sistemistica Università di Roma La Sapienza Via Salaria 113, 198 Roma, Italia.

More information

Exploiting peer group concept for adaptive and highly available services

Exploiting peer group concept for adaptive and highly available services Exploiting peer group concept for adaptive and highly available services Muhammad Asif Jan Centre for European Nuclear Research (CERN) Switzerland Fahd Ali Zahid, Mohammad Moazam Fraz Foundation University,

More information

PEER TO PEER FILE SHARING USING NETWORK CODING

PEER TO PEER FILE SHARING USING NETWORK CODING PEER TO PEER FILE SHARING USING NETWORK CODING Ajay Choudhary 1, Nilesh Akhade 2, Aditya Narke 3, Ajit Deshmane 4 Department of Computer Engineering, University of Pune Imperial College of Engineering

More information

Mapping the Gnutella Network: Macroscopic Properties of Large-Scale Peer-to-Peer Systems

Mapping the Gnutella Network: Macroscopic Properties of Large-Scale Peer-to-Peer Systems Mapping the Gnutella Network: Macroscopic Properties of Large-Scale Peer-to-Peer Systems Matei Ripeanu, Ian Foster {matei, foster}@cs.uchicago.edu Abstract Despite recent excitement generated by the peer-to-peer

More information

Cassandra A Decentralized, Structured Storage System

Cassandra A Decentralized, Structured Storage System Cassandra A Decentralized, Structured Storage System Avinash Lakshman and Prashant Malik Facebook Published: April 2010, Volume 44, Issue 2 Communications of the ACM http://dl.acm.org/citation.cfm?id=1773922

More information

QUALITY OF SERVICE METRICS FOR DATA TRANSMISSION IN MESH TOPOLOGIES

QUALITY OF SERVICE METRICS FOR DATA TRANSMISSION IN MESH TOPOLOGIES QUALITY OF SERVICE METRICS FOR DATA TRANSMISSION IN MESH TOPOLOGIES SWATHI NANDURI * ZAHOOR-UL-HUQ * Master of Technology, Associate Professor, G. Pulla Reddy Engineering College, G. Pulla Reddy Engineering

More information

A Simultaneous Solution for General Linear Equations on a Ring or Hierarchical Cluster

A Simultaneous Solution for General Linear Equations on a Ring or Hierarchical Cluster Acta Technica Jaurinensis Vol. 3. No. 1. 010 A Simultaneous Solution for General Linear Equations on a Ring or Hierarchical Cluster G. Molnárka, N. Varjasi Széchenyi István University Győr, Hungary, H-906

More information

How To Create A P2P Network

How To Create A P2P Network Peer-to-peer systems INF 5040 autumn 2007 lecturer: Roman Vitenberg INF5040, Frank Eliassen & Roman Vitenberg 1 Motivation for peer-to-peer Inherent restrictions of the standard client/server model Centralised

More information

Distributed Dynamic Load Balancing for Iterative-Stencil Applications

Distributed Dynamic Load Balancing for Iterative-Stencil Applications Distributed Dynamic Load Balancing for Iterative-Stencil Applications G. Dethier 1, P. Marchot 2 and P.A. de Marneffe 1 1 EECS Department, University of Liege, Belgium 2 Chemical Engineering Department,

More information

CS5412: TIER 2 OVERLAYS

CS5412: TIER 2 OVERLAYS 1 CS5412: TIER 2 OVERLAYS Lecture VI Ken Birman Recap 2 A week ago we discussed RON and Chord: typical examples of P2P network tools popular in the cloud Then we shifted attention and peeked into the data

More information

Lecture 6 Content Distribution and BitTorrent

Lecture 6 Content Distribution and BitTorrent ID2210 - Distributed Computing, Peer-to-Peer and GRIDS Lecture 6 Content Distribution and BitTorrent [Based on slides by Cosmin Arad] Today The problem of content distribution A popular solution: BitTorrent

More information

Distributed Data Stores

Distributed Data Stores Distributed Data Stores 1 Distributed Persistent State MapReduce addresses distributed processing of aggregation-based queries Persistent state across a large number of machines? Distributed DBMS High

More information

Trace Driven Analysis of the Long Term Evolution of Gnutella Peer-to-Peer Traffic

Trace Driven Analysis of the Long Term Evolution of Gnutella Peer-to-Peer Traffic Trace Driven Analysis of the Long Term Evolution of Gnutella Peer-to-Peer Traffic William Acosta and Surendar Chandra University of Notre Dame, Notre Dame IN, 46556, USA {wacosta,surendar}@cse.nd.edu Abstract.

More information

Scalable Source Routing

Scalable Source Routing Scalable Source Routing January 2010 Thomas Fuhrmann Department of Informatics, Self-Organizing Systems Group, Technical University Munich, Germany Routing in Networks You re there. I m here. Scalable

More information

Scalable P2P Overlays of Very Small Constant Degree: an Emerging Security Threat

Scalable P2P Overlays of Very Small Constant Degree: an Emerging Security Threat Scalable P2P Overlays of Very Small Constant Degree: an Emerging Security Threat Márk Jelasity 1 and Vilmos Bilicki 2 1 University of Szeged and Hungarian Academy of Sciences, Hungary, jelasity@inf.u-szeged.hu

More information

Peer-to-Peer Networks. Chapter 6: P2P Content Distribution

Peer-to-Peer Networks. Chapter 6: P2P Content Distribution Peer-to-Peer Networks Chapter 6: P2P Content Distribution Chapter Outline Content distribution overview Why P2P content distribution? Network coding Peer-to-peer multicast Kangasharju: Peer-to-Peer Networks

More information

PROPOSAL AND EVALUATION OF A COOPERATIVE MECHANISM FOR HYBRID P2P FILE-SHARING NETWORKS

PROPOSAL AND EVALUATION OF A COOPERATIVE MECHANISM FOR HYBRID P2P FILE-SHARING NETWORKS PROPOSAL AND EVALUATION OF A COOPERATIVE MECHANISM FOR HYBRID P2P FILE-SHARING NETWORKS Hongye Fu, Naoki Wakamiya, Masayuki Murata Graduate School of Information Science and Technology Osaka University

More information

Intelligent Agents for Routing on Mobile Ad-Hoc Networks

Intelligent Agents for Routing on Mobile Ad-Hoc Networks Intelligent Agents for Routing on Mobile Ad-Hoc Networks Y. Zhou Dalhousie University yzhou@cs.dal.ca A. N. Zincir-Heywood Dalhousie University zincir@cs.dal.ca Abstract This paper introduces a new agent-based

More information

Fault-Tolerant Framework for Load Balancing System

Fault-Tolerant Framework for Load Balancing System Fault-Tolerant Framework for Load Balancing System Y. K. LIU, L.M. CHENG, L.L.CHENG Department of Electronic Engineering City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong SAR HONG KONG Abstract:

More information

Dynamic Load Balancing for Cluster-based Publish/Subscribe System

Dynamic Load Balancing for Cluster-based Publish/Subscribe System Dynamic Load Balancing for Cluster-based Publish/Subscribe System Hojjat Jafarpour, Sharad Mehrotra and Nalini Venkatasubramanian Department of Computer Science University of California, Irvine {hjafarpo,

More information

Modeling an Agent-Based Decentralized File Sharing Network

Modeling an Agent-Based Decentralized File Sharing Network Modeling an Agent-Based Decentralized File Sharing Network Alex Gonopolskiy Benjamin Nash December 18, 2007 Abstract In this paper we propose a distributed file sharing network model. We take inspiration

More information

CHAPTER 2 MODELLING FOR DISTRIBUTED NETWORK SYSTEMS: THE CLIENT- SERVER MODEL

CHAPTER 2 MODELLING FOR DISTRIBUTED NETWORK SYSTEMS: THE CLIENT- SERVER MODEL CHAPTER 2 MODELLING FOR DISTRIBUTED NETWORK SYSTEMS: THE CLIENT- SERVER MODEL This chapter is to introduce the client-server model and its role in the development of distributed network systems. The chapter

More information

Storage Systems Autumn 2009. Chapter 6: Distributed Hash Tables and their Applications André Brinkmann

Storage Systems Autumn 2009. Chapter 6: Distributed Hash Tables and their Applications André Brinkmann Storage Systems Autumn 2009 Chapter 6: Distributed Hash Tables and their Applications André Brinkmann Scaling RAID architectures Using traditional RAID architecture does not scale Adding news disk implies

More information

Big Data & Scripting storage networks and distributed file systems

Big Data & Scripting storage networks and distributed file systems Big Data & Scripting storage networks and distributed file systems 1, 2, adaptivity: Cut-and-Paste 1 distribute blocks to [0, 1] using hash function start with n nodes: n equal parts of [0, 1] [0, 1] N

More information

Keywords: Big Data, HDFS, Map Reduce, Hadoop

Keywords: Big Data, HDFS, Map Reduce, Hadoop Volume 5, Issue 7, July 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Configuration Tuning

More information

Locality Based Protocol for MultiWriter Replication systems

Locality Based Protocol for MultiWriter Replication systems Locality Based Protocol for MultiWriter Replication systems Lei Gao Department of Computer Science The University of Texas at Austin lgao@cs.utexas.edu One of the challenging problems in building replication

More information

Gossiping Software Distribution

Gossiping Software Distribution Willem Toorop and Arthur van Kleef {wtoorop,akleef}@os3.nl March 29, 2010 Abstract In this report the scalability issues of existing Software Distribution methods are addressed. Gossiping protocols are

More information

In-Network Coding for Resilient Sensor Data Storage and Efficient Data Mule Collection

In-Network Coding for Resilient Sensor Data Storage and Efficient Data Mule Collection In-Network Coding for Resilient Sensor Data Storage and Efficient Data Mule Collection Michele Albano Jie Gao Instituto de telecomunicacoes, Aveiro, Portugal Stony Brook University, Stony Brook, USA Data

More information

A DECENTRALIZED WIKI ENGINE FOR COLLABORATIVE WIKIPEDIA HOSTING

A DECENTRALIZED WIKI ENGINE FOR COLLABORATIVE WIKIPEDIA HOSTING A DECENTRALIZED WIKI ENGINE FOR COLLABORATIVE WIKIPEDIA HOSTING Guido Urdaneta, Guillaume Pierre, Maarten van Steen Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands guidou@few.vu.nl,

More information

DATABASE DESIGN - 1DL400

DATABASE DESIGN - 1DL400 DATABASE DESIGN - 1DL400 Spring 2015 A course on modern database systems!! http://www.it.uu.se/research/group/udbl/kurser/dbii_vt15/ Kjell Orsborn! Uppsala Database Laboratory! Department of Information

More information

Fundamentals of Mobile and Pervasive Computing

Fundamentals of Mobile and Pervasive Computing Fundamentals of Mobile and Pervasive Computing Frank Adelstein Sandeep K. S. Gupta Golden G. Richard III Loren Schwiebert Technische Universitat Darmstadt FACHBEREICH INFORMATIK B1BLIOTHEK Inventar-Nr.:

More information

Database Replication with Oracle 11g and MS SQL Server 2008

Database Replication with Oracle 11g and MS SQL Server 2008 Database Replication with Oracle 11g and MS SQL Server 2008 Flavio Bolfing Software and Systems University of Applied Sciences Chur, Switzerland www.hsr.ch/mse Abstract Database replication is used widely

More information

Distributed File Systems

Distributed File Systems Distributed File Systems Mauro Fruet University of Trento - Italy 2011/12/19 Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 1 / 39 Outline 1 Distributed File Systems 2 The Google File System (GFS)

More information

The Advantages and Disadvantages of Network Computing Nodes

The Advantages and Disadvantages of Network Computing Nodes Big Data & Scripting storage networks and distributed file systems 1, 2, in the remainder we use networks of computing nodes to enable computations on even larger datasets for a computation, each node

More information

Distributed Systems. Tutorial 12 Cassandra

Distributed Systems. Tutorial 12 Cassandra Distributed Systems Tutorial 12 Cassandra written by Alex Libov Based on FOSDEM 2010 presentation winter semester, 2013-2014 Cassandra In Greek mythology, Cassandra had the power of prophecy and the curse

More information

Graph Theory and Complex Networks: An Introduction. Chapter 08: Computer networks

Graph Theory and Complex Networks: An Introduction. Chapter 08: Computer networks Graph Theory and Complex Networks: An Introduction Maarten van Steen VU Amsterdam, Dept. Computer Science Room R4.20, steen@cs.vu.nl Chapter 08: Computer networks Version: March 3, 2011 2 / 53 Contents

More information

Using Peer to Peer Dynamic Querying in Grid Information Services

Using Peer to Peer Dynamic Querying in Grid Information Services Using Peer to Peer Dynamic Querying in Grid Information Services Domenico Talia and Paolo Trunfio DEIS University of Calabria HPC 2008 July 2, 2008 Cetraro, Italy Using P2P for Large scale Grid Information

More information

How To Understand The Power Of Anemic Communication In A Networked System

How To Understand The Power Of Anemic Communication In A Networked System Invited Talk: Epidemic Protocols for Extreme-scale Computing Global Knowledge without Global Communication Dr. Giuseppe Di Fatta G.DiFatta@reading.ac.uk Wednesday, September 24, 2014 Outline extreme-scale

More information

Designing a Cloud Storage System

Designing a Cloud Storage System Designing a Cloud Storage System End to End Cloud Storage When designing a cloud storage system, there is value in decoupling the system s archival capacity (its ability to persistently store large volumes

More information

Astaro Deployment Guide High Availability Options Clustering and Hot Standby

Astaro Deployment Guide High Availability Options Clustering and Hot Standby Connect With Confidence Astaro Deployment Guide Clustering and Hot Standby Table of Contents Introduction... 2 Active/Passive HA (Hot Standby)... 2 Active/Active HA (Cluster)... 2 Astaro s HA Act as One...

More information

Cloud Computing at Google. Architecture

Cloud Computing at Google. Architecture Cloud Computing at Google Google File System Web Systems and Algorithms Google Chris Brooks Department of Computer Science University of San Francisco Google has developed a layered system to handle webscale

More information

Giving life to today s media distribution services

Giving life to today s media distribution services Giving life to today s media distribution services FIA - Future Internet Assembly Athens, 17 March 2014 Presenter: Nikolaos Efthymiopoulos Network architecture & Management Group Copyright University of

More information

Autonomous Fault Detection and Recovery System in Large-scale Networks

Autonomous Fault Detection and Recovery System in Large-scale Networks Autonomous Fault Detection and Recovery System in Large-scale Networks Raheel Ahmed Memon 1, Yeonseung Ryu, Abdul Qadir Rahimoo Abstract In networks like Combat ship data network, the Ethernet is required

More information

RESEARCH ISSUES IN PEER-TO-PEER DATA MANAGEMENT

RESEARCH ISSUES IN PEER-TO-PEER DATA MANAGEMENT RESEARCH ISSUES IN PEER-TO-PEER DATA MANAGEMENT Bilkent University 1 OUTLINE P2P computing systems Representative P2P systems P2P data management Incentive mechanisms Concluding remarks Bilkent University

More information

The Sierra Clustered Database Engine, the technology at the heart of

The Sierra Clustered Database Engine, the technology at the heart of A New Approach: Clustrix Sierra Database Engine The Sierra Clustered Database Engine, the technology at the heart of the Clustrix solution, is a shared-nothing environment that includes the Sierra Parallel

More information

The Service Availability Forum Specification for High Availability Middleware

The Service Availability Forum Specification for High Availability Middleware The Availability Forum Specification for High Availability Middleware Timo Jokiaho, Fred Herrmann, Dave Penkler, Manfred Reitenspiess, Louise Moser Availability Forum Timo.Jokiaho@nokia.com, Frederic.Herrmann@sun.com,

More information

Dependability in Web Services

Dependability in Web Services Dependability in Web Services Christian Mikalsen chrismi@ifi.uio.no INF5360, Spring 2008 1 Agenda Introduction to Web Services. Extensible Web Services Architecture for Notification in Large- Scale Systems.

More information

Costs and Benefits of Reputation Management Systems

Costs and Benefits of Reputation Management Systems Costs and Benefits of Reputation Management Systems Roberto G. Cascella University of Trento Dipartimento di Ingegneria e Scienza dell Informazione Via Sommarive 14, I-381 Povo (TN), Italy cascella@disi.unitn.it

More information

MapReduce Jeffrey Dean and Sanjay Ghemawat. Background context

MapReduce Jeffrey Dean and Sanjay Ghemawat. Background context MapReduce Jeffrey Dean and Sanjay Ghemawat Background context BIG DATA!! o Large-scale services generate huge volumes of data: logs, crawls, user databases, web site content, etc. o Very useful to be able

More information

Autoconfiguration and maintenance of the IP address in ad-hoc mobile networks

Autoconfiguration and maintenance of the IP address in ad-hoc mobile networks 1 Autoconfiguration and maintenance of the IP address in ad-hoc mobile networks M. Fazio, M. Villari, A. Puliafito Università di Messina, Dipartimento di Matematica Contrada Papardo, Salita Sperone, 98166

More information

Integrated Application and Data Protection. NEC ExpressCluster White Paper

Integrated Application and Data Protection. NEC ExpressCluster White Paper Integrated Application and Data Protection NEC ExpressCluster White Paper Introduction Critical business processes and operations depend on real-time access to IT systems that consist of applications and

More information

Some questions... Graphs

Some questions... Graphs Uni Innsbruck Informatik - 1 Uni Innsbruck Informatik - 2 Some questions... Peer-to to-peer Systems Analysis of unstructured P2P systems How scalable is Gnutella? How robust is Gnutella? Why does FreeNet

More information

A NETWORK CONSTRUCTION METHOD FOR A SCALABLE P2P VIDEO CONFERENCING SYSTEM

A NETWORK CONSTRUCTION METHOD FOR A SCALABLE P2P VIDEO CONFERENCING SYSTEM A NETWORK CONSTRUCTION METHOD FOR A SCALABLE P2P VIDEO CONFERENCING SYSTEM Hideto Horiuchi, Naoki Wakamiya and Masayuki Murata Graduate School of Information Science and Technology, Osaka University 1

More information

Modular Communication Infrastructure Design with Quality of Service

Modular Communication Infrastructure Design with Quality of Service Modular Communication Infrastructure Design with Quality of Service Pawel Wojciechowski and Péter Urbán Distributed Systems Laboratory School of Computer and Communication Sciences Swiss Federal Institute

More information

Analysis on Leveraging social networks for p2p content-based file sharing in disconnected manets

Analysis on Leveraging social networks for p2p content-based file sharing in disconnected manets Analysis on Leveraging social networks for p2p content-based file sharing in disconnected manets # K.Deepika 1, M.Tech Computer Science Engineering, Mail: medeepusony@gmail.com # K.Meena 2, Assistant Professor

More information

HPAM: Hybrid Protocol for Application Level Multicast. Yeo Chai Kiat

HPAM: Hybrid Protocol for Application Level Multicast. Yeo Chai Kiat HPAM: Hybrid Protocol for Application Level Multicast Yeo Chai Kiat Scope 1. Introduction 2. Hybrid Protocol for Application Level Multicast (HPAM) 3. Features of HPAM 4. Conclusion 1. Introduction Video

More information

International journal of Engineering Research-Online A Peer Reviewed International Journal Articles available online http://www.ijoer.

International journal of Engineering Research-Online A Peer Reviewed International Journal Articles available online http://www.ijoer. RESEARCH ARTICLE ISSN: 2321-7758 GLOBAL LOAD DISTRIBUTION USING SKIP GRAPH, BATON AND CHORD J.K.JEEVITHA, B.KARTHIKA* Information Technology,PSNA College of Engineering & Technology, Dindigul, India Article

More information

CROSS LAYER BASED MULTIPATH ROUTING FOR LOAD BALANCING

CROSS LAYER BASED MULTIPATH ROUTING FOR LOAD BALANCING CHAPTER 6 CROSS LAYER BASED MULTIPATH ROUTING FOR LOAD BALANCING 6.1 INTRODUCTION The technical challenges in WMNs are load balancing, optimal routing, fairness, network auto-configuration and mobility

More information

New Structured P2P Network with Dynamic Load Balancing Scheme

New Structured P2P Network with Dynamic Load Balancing Scheme New Structured P2P Network with Dynamic Load Balancing Scheme Atushi TAKEDA, Takuma OIDE and Akiko TAKAHASHI Department of Information Science, Tohoku Gakuin University Department of Information Engineering,

More information

BigData. An Overview of Several Approaches. David Mera 16/12/2013. Masaryk University Brno, Czech Republic

BigData. An Overview of Several Approaches. David Mera 16/12/2013. Masaryk University Brno, Czech Republic BigData An Overview of Several Approaches David Mera Masaryk University Brno, Czech Republic 16/12/2013 Table of Contents 1 Introduction 2 Terminology 3 Approaches focused on batch data processing MapReduce-Hadoop

More information

A Hierarchical Structure based Coverage Repair in Wireless Sensor Networks

A Hierarchical Structure based Coverage Repair in Wireless Sensor Networks A Hierarchical Structure based Coverage Repair in Wireless Sensor Networks Jie Wu Computer Science & Engineering Department Florida Atlantic University Boca Raton, FL 3343, USA E-mail: jie@cse.fau.edu

More information

Restricted and Mobile: Security in Mobile Wireless Sensor Networks Kısıtlı ve Hareketli: Mobil Telsiz Duyarga Ağlarında Güvenlik

Restricted and Mobile: Security in Mobile Wireless Sensor Networks Kısıtlı ve Hareketli: Mobil Telsiz Duyarga Ağlarında Güvenlik Restricted and Mobile: Security in Mobile Wireless Sensor Networks Kısıtlı ve Hareketli: Mobil Telsiz Duyarga Ağlarında Güvenlik Albert Levi levi@sabanciuniv..edu Sabancı University Computer Science and

More information

Reliable Adaptable Network RAM

Reliable Adaptable Network RAM Reliable Adaptable Network RAM Tia Newhall, Daniel Amato, Alexandr Pshenichkin Computer Science Department, Swarthmore College Swarthmore, PA 19081, USA Abstract We present reliability solutions for adaptable

More information

A Scalable Peer-to-Peer IPTV System

A Scalable Peer-to-Peer IPTV System A Scalable Peer-to-Peer IPTV System Meng-Ting Lu ac, Hung Nien ac, Jui-Chieh Wu b, Kuan-Jen Peng ac, Polly Huang ade, Jason J. Yao ac, Chih-Chun Lai f and Homer H. Chen acd Department of Electrical Engineering

More information

Research on Clustering Analysis of Big Data Yuan Yuanming 1, 2, a, Wu Chanle 1, 2

Research on Clustering Analysis of Big Data Yuan Yuanming 1, 2, a, Wu Chanle 1, 2 Advanced Engineering Forum Vols. 6-7 (2012) pp 82-87 Online: 2012-09-26 (2012) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/aef.6-7.82 Research on Clustering Analysis of Big Data

More information

Index Terms : Load rebalance, distributed file systems, clouds, movement cost, load imbalance, chunk.

Index Terms : Load rebalance, distributed file systems, clouds, movement cost, load imbalance, chunk. Load Rebalancing for Distributed File Systems in Clouds. Smita Salunkhe, S. S. Sannakki Department of Computer Science and Engineering KLS Gogte Institute of Technology, Belgaum, Karnataka, India Affiliated

More information

SuperViz: An Interactive Visualization of Super-Peer P2P Network

SuperViz: An Interactive Visualization of Super-Peer P2P Network SuperViz: An Interactive Visualization of Super-Peer P2P Network Anthony (Peiqun) Yu pqyu@cs.ubc.ca Abstract: The Efficient Clustered Super-Peer P2P network is a novel P2P architecture, which overcomes

More information

Building a Highly Available and Scalable Web Farm

Building a Highly Available and Scalable Web Farm Page 1 of 10 MSDN Home > MSDN Library > Deployment Rate this page: 10 users 4.9 out of 5 Building a Highly Available and Scalable Web Farm Duwamish Online Paul Johns and Aaron Ching Microsoft Developer

More information

Definition. A Historical Example

Definition. A Historical Example Overlay Networks This lecture contains slides created by Ion Stoica (UC Berkeley). Slides used with permission from author. All rights remain with author. Definition Network defines addressing, routing,

More information

SUITABLE ROUTING PATH FOR PEER TO PEER FILE TRANSFER

SUITABLE ROUTING PATH FOR PEER TO PEER FILE TRANSFER SUITABLE ROUTING PATH FOR PEER TO PEER FILE TRANSFER R. Naga Priyadarsini, S. Suma and V. Dhanakoti Department of Computer Science Engineering, Valliammai Engineering College, Kanchipuram, India ABSTRACT

More information

Parallel Programming at the Exascale Era: A Case Study on Parallelizing Matrix Assembly For Unstructured Meshes

Parallel Programming at the Exascale Era: A Case Study on Parallelizing Matrix Assembly For Unstructured Meshes Parallel Programming at the Exascale Era: A Case Study on Parallelizing Matrix Assembly For Unstructured Meshes Eric Petit, Loïc Thebault, Quang V. Dinh May 2014 EXA2CT Consortium 2 WPs Organization Proto-Applications

More information

RVS-Seminar Overlay Multicast Quality of Service and Content Addressable Network (CAN)

RVS-Seminar Overlay Multicast Quality of Service and Content Addressable Network (CAN) RVS-Seminar Overlay Multicast Quality of Service and Content Addressable Network (CAN) Luca Bettosini Universität Bern Outline > Goals / Motivation ( CAN ) > Content Addressable Network > CAN Multicast

More information

References: Adi Shamir, How to Share a Secret, CACM, November 1979.

References: Adi Shamir, How to Share a Secret, CACM, November 1979. Ad Hoc Network Security References: Adi Shamir, How to Share a Secret, CACM, November 1979. Jiejun Kong, Petros Zerfos, Haiyun Luo, Songwu Lu, Lixia Zhang, Providing Robust and Ubiquitous Security Support

More information

Bounded Cost Algorithms for Multivalued Consensus Using Binary Consensus Instances

Bounded Cost Algorithms for Multivalued Consensus Using Binary Consensus Instances Bounded Cost Algorithms for Multivalued Consensus Using Binary Consensus Instances Jialin Zhang Tsinghua University zhanggl02@mails.tsinghua.edu.cn Wei Chen Microsoft Research Asia weic@microsoft.com Abstract

More information