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1 CS 525 Advanced Distributed Systems Spring 09 Indranil Gupta Lecture 7 More on Epidemics (or “Tipping Point Protocols”) February 12, 2009 (gatorlog.com)

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Presentation on theme: "1 CS 525 Advanced Distributed Systems Spring 09 Indranil Gupta Lecture 7 More on Epidemics (or “Tipping Point Protocols”) February 12, 2009 (gatorlog.com)"— Presentation transcript:

1 1 CS 525 Advanced Distributed Systems Spring 09 Indranil Gupta Lecture 7 More on Epidemics (or “Tipping Point Protocols”) February 12, 2009 (gatorlog.com) (epath.org)

2 2 Question… What fraction of main roads need to be randomly knocked out before source and destination are completely cut off? Destination Source

3 3 Critical Value? Answer = 0.5 Tipping Point! Source Destination (Comes from Percolation Theory)

4 4 “Tipping Point” [Malcolm Gladwell, The Tipping Point, Little Brown and Company, ISBN: ] Tipping is that (magic) moment when an idea, trend or social behavior crosses a threshold, and spreads like wildfire.

5 5 Epidemic Protocols A specific class of tipping point protocols Local behavior at each node – probabilistic Determines global, emergent behavior at the scale of the distributed system As one tunes up the local probabilities, the global behavior may undergo a threshold behavior (or, a phase change) Three papers: 1.Epidemic algorithms 2.Bimodal multicast 3.PBBF (sensor networks)

6 6 Epidemic Algorithms for Replicated Database Maintenance Alan Demers et. al. Xerox Palo Alto Research Center PODC 1987 [Some slides borrowed from presentation by: R. Ganti and P. Jayachandran]

7 7 Introduction Maintain mutual consistency of updates in a distributed and replicated database Used in Clearinghouse database – developed in Xerox PARC and used for many years First cut approaches –Direct mail: send updates to all nodes Timely and efficient, but unreliable –Anti-entropy: exchange database content with random site Reliable, but slower than direct mail and uses more resources –Rumor mongering: exchange only ‘hot rumor’ updates Less reliable than anti-entropy, but uses fewer resources

8 8 Epidemic Multicast Protocol rounds (local clock) Protocol rounds (local clock) b random targets per round b random targets per round Uninfected Uninfected Infected Infected Gossip Message (UDP) (from Lecture 1)

9 9 Epidemic Multicast (Push) Protocol rounds (local clock) Protocol rounds (local clock) b random targets per round b random targets per round Uninfected Uninfected Infected Infected Gossip Message (UDP)

10 10 Epidemic Multicast (Pull) Protocol rounds (local clock) Protocol rounds (local clock) b random targets per round b random targets per round Uninfected Uninfected Infected Infected Gossip Message (UDP)

11 11 Pull > Push Pull converges faster than push, thus providing better delay Push-pull hybrid variant possible (see Karp and Shenker’s “Randomized Rumor Spreading”) Pull Push p i – Probability that a node is susceptible after the i th round

12 12 Anti-entropy: Optimizations Maintain checksum, compare databases if checksums unequal Maintain recent update lists for time T, exchange lists first Maintain inverted index of database by timestamp; exchange information in reverse timestamp order, incrementally re-compute checksums

13 13 Epidemic Flavors Blind vs. Feedback –Blind: lose interest to gossip with probability 1/k every time you gossip –Feedback: Loss of interest with probability 1/k only when recipient already knows the rumor Counter vs. Coin –Coin: above variants –Counter: Lose interest completely after k unnecessary contacts. Can be combined with blind. Push vs. Pull

14 14 Deletion and Death Certificates Absence of item does not spread; On the contrary, it can get resurrected! Use of death certificates (DCs) – when a node receives a DC, old copy of data is deleted How long to maintain a DC? –Typically twice (or some multiple of) the time to spread the information –Alternately, use Chandy and Lamport snapshot algorithm to ensure all nodes have received –Certain sites maintain dormant DCs for a longer duration; re-awakened if item seen again

15 15 Performance Metrics Residue: Fraction of susceptibles left when epidemic finishes Traffic: (Total update traffic) / (No. of sites) Delay: Average time for receiving update and maximum time for receiving update Some results: –Counters and feedback improve delay –Pull provides lower delay than push

16 16 Performance Evaluation Tipping Point Behavior

17 17 Discussion Pick your favorite: Push vs. pull vs. push-pull –Name one disadvantage of each Direct mail vs. anti-entropy vs. rumor mongering –Name one disadvantage of each Random neigbhor picking –Disadvantage in wired networks? –In Sensor network?

18 18 Bimodal Multicast Kenneth P. Birman et. al. ACM TOCS 1999 [Some slides borrowed from presentation by: W. Fagen and L. Cook]

19 19 “Traditional” Multicast Protocols

20 20 Vs. Pbcast Atomicity: All or none delivery Multicast stability: Reliable immediately delivery of messages Scalability: Bad. Costs >= quadratic with group size. Ordering Atomicity: Bimodal delivery guarantee, almost all or almost none (immediately) Multicast stability: Reliable eventual delivery of messages Scalability: Costs logarithmic w.r.t. network size. Throughput stability. Ordering Traditional MulticastPbcast

21 21 Pbcast: Probabilistic Broadcast Protocol Pbcast has two stages: 1.Unreliable, hierarchical, best-effort broadcast. Eg. IP Multicast 2.Two-phase anti-entropy protocol: runs simultaneously with the broadcast messages First phase detects message loss Second phase corrects such losses

22 22 The second stage Anti-entropy round: –Gossip Messages: Each process chooses another random process and sends a summary of its recent messages –Solicitation Messages: Messages sent back to the sender of the gossip message requesting a resend of a given set of messages (not necessarily the original source) –Message Resend: Upon reception of a solicitation message, the sender resends that message Protocol parameters at each node –# of rounds and # of processes contacted in each round –Product of above two parameters called fanout

23 23 Optimizations Soft-Failure Detection: Retransmission requests served only if received recently; protects against congestion caused due to redundant retransmissions Round Retransmission Limit: Limit the no. of retransmissions in a round; spread overhead in space and time Most-Recent-First Retransmission: prefer recent messages Independent Numbering of Rounds: Allows delivery and garbage collection to be entirely a local decision Multicast for Some Retransmissions

24 24 Bimodality of Pbcast Almost noneAlmost all Logarithmic Y-axis

25 25 Latency for Delivery Logarithmic growth

26 26 Throughput Comparison

27 27 Discussion Disadvantages of Bimodal Multicast? –When would wasteful messages be sent? What happens when –Rate of injection of multicasts is very very low? –IP multicast is very very reliable? –IP multicast is very very unreliable?

28 28 PBBF: Probability-Based Broadcast Forwarding Cigdem Sengul and Matt Miller ICDCS 2005 and ACM TOSN 2008 (Originated from a 525 Project)

29 29 Broadcast in an Ad-Hoc Network Ad-hoc sensor network (Grid example below) One node has a piece of information that it needs to broadcast: e.g., (1) code update, (2) query Simple approach: each node floods received message to all its neighbors –Disadvantages?

30 30 IEEE PSM A real, stable MAC protocol (similar results for S- MAC, T-MAC, etc.) Nodes are assumed to be synchronized Every beacon interval (BI), all nodes wake up for an ATIM window (AW) During the AW, nodes advertise any traffic that they have queued After the AW, nodes remain active if they expect to send or receive data based on advertisements; otherwise nodes return to sleep until the next BI

31 31 Protocol Extreme #1 A N1 N2 N3 D A = ATIM Pkt D = Data Pkt N2N1N3 A D A

32 32 Protocol Extreme #2 N1 N2 N3 D A = ATIM Pkt D = Data Pkt D D A N2N1N3

33 33 Probability-Based Broadcast Forwarding (PBBF) Introduce two parameters to sleep scheduling protocols: p and q When a node is scheduled to sleep, it will remain active with probability q When a node receives a broadcast, it rebroadcasts immediately with probability p –With probability (1-p), the node will wait and advertise the packet during the next AW before rebroadcasting the packet

34 34 Analysis: Reliability Phase transition when: pq + (1-p) ≈ Larger than traditional bond percolation threshold –Boundary effects –Different metric Still shows phase transition q p=0.25 p=0.37 p=0.5 p=0.75 Fraction of Broadcasts Received by 99% of Nodes Tipping Point!

35 35 Application: Energy and Latency Energy Joules/Broadcast q Latency Average 5-Hop Latency PBBF Increasing p q ≈ 1 + q * [(BI - AW)/AW] Ns2 simulation: 50 nodes, uniform placement, 10 avg. neighbors

36 36 Adaptive PBBF Energy Latency Achievable Region

37 37 Adaptive PBBF (TOSN paper) Dynamically adjusting p and q to converge to user- specified QoS metrics –Code updates prefer reliability overl latency –Queries prefer latency over reliability Can specify any 2 of energy, latency, and reliability Subject to those constraints, p and q are adjusted to achieve the highest reliability possible Time q p

38 38 Discussion PBBF: bond percolation (remove roads from city) Haas et al paper (Infocom): site percolation –Remove intersections/junctions (not roads) from city Site percolation and bond percolation have different thresholds and behaviors Hybrid possible? (like push-pull?) What about over-hearing optimizations? (like feedback)

39 39 Question… Are there other tipping point protocols…? Destination Source

40 40 Next Week Onwards Student Presentations start (see instructions) Reviews needed (see instructions) Project Meetings start (see newsgroup) –Think about which testbed you need access to: PlanetLab, Emulab, Cirrus Tomorrow: Yahoo! Training seminar


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