Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
SIP extensions for the IP Multimedia Subsystem wikipedia , lookup
Internet protocol suite wikipedia , lookup
Airborne Networking wikipedia , lookup
List of wireless community networks by region wikipedia , lookup
Recursive InterNetwork Architecture (RINA) wikipedia , lookup
Distributed operating system wikipedia , lookup
Zero-configuration networking wikipedia , lookup
(gatorlog.com) CS 525 Advanced Distributed Systems Spring 09 (epath.org) Indranil Gupta Lecture 7 More on Epidemics (or “Tipping Point Protocols”) February 12, 2009 1 Question… What fraction of main roads need to be randomly knocked out before source and destination are completely cut off? Source Destination 2 Tipping Point! Critical Value? Answer = 0.5 Source Destination (Comes from Percolation Theory) 3 “Tipping Point” [Malcolm Gladwell, The Tipping Point, Little Brown and Company, ISBN: 0316346624] Tipping is that (magic) moment when an idea, trend or social behavior crosses a threshold, and spreads like wildfire. 4 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) 5 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] 6 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 7 (from Lecture 1) Epidemic Multicast Infected Protocol rounds (local clock) b random targets per round Gossip Message (UDP) Uninfected 8 Epidemic Multicast (Push) Infected Protocol rounds (local clock) b random targets per round Gossip Message (UDP) Uninfected 9 Epidemic Multicast (Pull) Infected Gossip Message (UDP) Uninfected Protocol rounds (local clock) b random targets per round 10 Pull > Push pi – Probability that a node is susceptible after the ith round pi pi 1 1 2 i p Pull 1 pi 1 n n 1 pi Push • Pull converges faster than push, thus providing better delay • Push-pull hybrid variant possible (see Karp and Shenker’s “Randomized Rumor Spreading”) 11 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 12 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 13 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 14 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 15 Performance Evaluation Tipping Point Behavior 16 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? 17 Bimodal Multicast Kenneth P. Birman et. al. ACM TOCS 1999 [Some slides borrowed from presentation by: W. Fagen and L. Cook] 18 “Traditional” Multicast Protocols 19 Vs. Pbcast Traditional Multicast • Atomicity: All or none delivery • • Multicast stability: Reliable immediately delivery of messages • Scalability: Bad. Costs >= quadratic with group size. • • Ordering • • Pbcast 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 20 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 21 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 22 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 23 Bimodality of Pbcast Logarithmic Y-axis Almost none Almost all 24 Latency for Delivery Logarithmic growth 25 Throughput Comparison 26 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? 27 PBBF: Probability-Based Broadcast Forwarding Cigdem Sengul and Matt Miller ICDCS 2005 and ACM TOSN 2008 (Originated from a 525 Project) 28 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? 29 IEEE 802.11 PSM A real, stable MAC protocol (similar results for SMAC, 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 30 Protocol Extreme #1 N1 N1 N2 A N2 N3 D A D A N3 A = ATIM Pkt D = Data Pkt 31 Protocol Extreme #2 N1 N1 N2 N3 A N2 N3 D D D A = ATIM Pkt D = Data Pkt 32 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 33 • Phase transition when: pq + (1-p) ≈ 0.8-0.85 • Larger than traditional bond percolation threshold – Boundary effects – Different metric • Still shows phase transition Fraction of Broadcasts Received by 99% of Nodes Analysis: ReliabilityTipping Point! q 34 Application: Energy and Latency Energy Joules/Broadcast Latency Average 5-Hop Latency Increasing p q q ≈ 1 + q * [(BI - AW)/AW] Ns2 simulation: 50 nodes, uniform placement, 10 avg. neighbors 35 Energy Adaptive PBBF Achievable Region Latency 36 Adaptive PBBF (TOSN paper) • Dynamically adjusting p and q to converge to userspecified 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 1.0 0.5 q p 0.0 Time 37 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) 38 Question… Are there other tipping point protocols…? Source Destination 39 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 40