Efficient Gossip and Bimodal Multicast Protocols in Distributed Systems

Gossip protocols are vital in spreading information efficiently in large systems, with log(N) time complexity for dissemination. Bimodal multicast uses IP multicast for message transmission, lacking reliability and flow control. The cost of IP multicast is low, but ensuring reliability can be achieved by integrating gossip mechanisms. By gossiping about multicasts and exchanging digests instead of actual messages, the accuracy and efficiency of bimodal multicast can be enhanced.

• Distributed Systems
• Gossip Protocols
• Bimodal Multicast
• IP Multicast
• Reliability

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1. CS5412 Spring 2016 1 CS5412: BIMODAL MULTICAST ASTROLABE Lecture XIX Ken Birman

2. Gossip 201 2 Recall from early in the semester that gossip spreads in log(system size) time But is this actually fast ? 1.0 % infected 0.0 Time CS5412 Spring 2016

3. Gossip in distributed systems 3 Log(N) can be a very big number! With N=100,000, log(N) would be 12 So with one gossip round per five seconds, information needs one minute to spread in a large system! Some gossip protocols combine pure gossip with an accelerator A good way to get the word out quickly CS5412 Spring 2016

4. Bimodal Multicast 4 To send a message, this protocol uses IP multicast We just transmit it without delay and we don t expect any form of responses Not reliable, no acks No flow control (this can be an issue) In data centers that lack IP multicast, can simulate by sending UDP packets 1:1 without acks CS5412 Spring 2016

5. Whats the cost of an IP multicast? 5 In principle, each Bimodal Multicast packet traverses the relevant data center links and routers just once per message So this is extremely cheap... but how do we deal with systems that didn t receive the multicast? CS5412 Spring 2016

6. Making Bimodal Multicast reliable 6 We can use gossip! Every node tracks the membership of the target group (using gossip, just like with Kelips, the DHT we studied early in the semester) Bootstrap by learning some node addresses from some kind of a server or web page But then exchange of gossip used to improve accuracy CS5412 Spring 2016

7. Making Bimodal Multicast reliable 7 Now, layer in a gossip mechanism that gossips about multicasts each node knows about Rather than sending the multicasts themselves, the gossip messages just talk about digests , which are lists Node A might send node B I have messages 1-18 from sender X I have message 11 from sender Y I have messages 14, 16 and 22-71 from sender Z Compactly represented... This is a form of push gossip CS5412 Spring 2016

8. Making Bimodal Multicast reliable 8 On receiving such a gossip message, the recipient checks to see which messages it has that the gossip sender lacks, and vice versa Then it responds I have copies of messages M, M and M that you seem to lack I would like a copy of messages N, N and N please An exchange of the actual messages follows CS5412 Spring 2016

9. Optimizations 9 Bimodal Multicast resends using IP multicast if there is evidence that a few nodes may be missing the same thing E.g. if two nodes ask for the same retransmission Or if a retransmission shows up from a very remote node (IP multicast doesn t always work in WANs) It also prioritizes recent messages over old ones Reliability has a bimodal probability curve: either nobody gets a message or nearly everyone does CS5412 Spring 2016

10. lpbcast variation 10 In this variation on Bimodal Multicast instead of gossiping with every node in a system, we modify the Bimodal Multicast protocol It maintains a peer overlay : each member only gossips with a smaller set of peers picked to be reachable with low round-trip times, plus a second small set of remote peers picked to ensure that the graph is very highly connected and has a small diameter Called a small worlds structure by Jon Kleinberg Lpbcast is often faster, but equally reliable! CS5412 Spring 2016

11. Speculation... about speed 11 When we combine IP multicast with gossip we try to match the tool we re using with the need Try to get the messages through fast... but if loss occurs, try to have a very predictable recovery cost Gossip has a totally predictable worst-case load This is appealing at large scales How can we generalize this concept? CS5412 Spring 2016

12. A thought question 12 What s the best way to Count the number of nodes in a system? Compute the average load, or find the most loaded nodes, or least loaded nodes? Options to consider Pure gossip solution Construct an overlay tree (via flooding , like in our consistent snapshot algorithm), then count nodes in the tree, or pull the answer from the leaves to the root CS5412 Spring 2016

13. and the answer is 13 Gossip isn t very good for some of these tasks! There are gossip solutions for counting nodes, but they give approximate answers and run slowly Tricky to compute something like an average because of re-counting effect, (best algorithm: Kempe et al) On the other hand, gossip works well for finding the c most loaded or least loaded nodes (constant c) Gossip solutions will usually run in time O(log N) and generally give probabilistic solutions CS5412 Spring 2016

14. Yet with flooding easy! 14 Recall how flooding works 3 2 Labels: distance of the node from the root 1 3 2 3 Basically: we construct a tree by pushing data towards the leaves and linking a node to its parent when that node first learns of the flood Can do this with a fixed topology or in a gossip style by picking random next hops CS5412 Spring 2016

15. This is a spanning tree 15 Once we have a spanning tree To count the nodes, just have leaves report 1 to their parents and inner nodes count the values from their children To compute an average, have the leaves report their value and the parent compute the sum, then divide by the count of nodes To find the least or most loaded node, inner nodes compute a min or max Tree should have roughly log(N) depth, but once we build it, we can reuse it for a while CS5412 Spring 2016

16. Not all logs are identical! 16 When we say that a gossip protocol needs time log(N) to run, we mean log(N) rounds And a gossip protocol usually sends one message every five seconds or so, hence with 100,000 nodes, 60 secs But our spanning tree protocol is constructed using a flooding algorithm that runs in a hurry Log(N) depth, but each hop takes perhaps a millisecond. So with 100,000 nodes we have our tree in 12 ms and answers in 24ms! CS5412 Spring 2016

17. Insight? 17 Gossip has time complexity O(log N) but the constant can be rather big (5000 times larger in our example) Spanning tree had same time complexity but a tiny constant in front But network load for spanning tree was much higher In the last step, we may have reached roughly half the nodes in the system So 50,000 messages were sent all at the same time! CS5412 Spring 2016

18. Gossip vs Urgent? 18 With gossip, we have a slow but steady story We know the speed and the cost, and both are low A constant, low-key, background cost And gossip is also very robust Urgent protocols (like our flooding protocol, or 2PC, or reliable virtually synchronous multicast) Are way faster But produce load spikes And may be fragile, prone to broadcast storms, etc CS5412 Spring 2016

19. Introducing hierarchy 19 One issue with gossip is that the messages fill up With constant sized messages and constant rate of communication we ll inevitably reach the limit! Can we inroduce hierarchy into gossip systems? CS5412 Spring 2016

20. Astrolabe 20 Intended as help for applications adrift in a sea of information Structure emerges from a randomized gossip protocol This approach is robust and scalable even under stress that cripples traditional systems Developed at RNS, Cornell By Robbert van Renesse, with many others helping Technology was adopted at Amazon.com (but they build their own solutions rather than using it in this form) CS5412 Spring 2016

21. Astrolabe is a flexible monitoring overlay 21 Name Name Time Time Load Load Weblogic? Weblogic? SMTP? SMTP? Word Version Version Word swift swift 2011 2271 2.0 1.8 0 0 1 1 6.2 6.2 falcon falcon 1971 1971 1.5 1.5 1 1 0 0 4.1 4.1 cardinal cardinal 2004 2004 4.5 4.5 1 1 0 0 6.0 6.0 swift.cs.cornell.edu Periodically, pull data from monitored systems Name Name Time Time Load Load Weblogic ? ? Weblogic SMTP? SMTP? Word Version Version Word swift swift 2003 2003 .67 .67 0 0 1 1 6.2 6.2 falcon falcon 1976 1976 2.7 2.7 1 1 0 0 4.1 4.1 cardinal cardinal 2201 2231 3.5 1.7 1 1 1 1 6.0 6.0 CS5412 Spring 2016 cardinal.cs.cornell.edu

22. Astrolabe in a single domain 22 Each node owns a single tuple, like the management information base (MIB) Nodes discover one-another through a simple broadcast scheme ( anyone out there? ) and gossip about membership Nodes also keep replicas of one-another s rows Periodically (uniformly at random) merge your state with some else CS5412 Spring 2016

23. State Merge: Core of Astrolabe epidemic 23 Name Time Load Weblogic? SMTP? Word Version swift 2011 2.0 0 1 6.2 falcon 1971 1.5 1 0 4.1 cardinal 2004 4.5 1 0 6.0 swift.cs.cornell.edu Name Time Load Weblogic ? SMTP? Word Version swift 2003 .67 0 1 6.2 falcon 1976 2.7 1 0 4.1 cardinal 2201 3.5 1 1 6.0 CS5412 Spring 2016 cardinal.cs.cornell.edu

24. State Merge: Core of Astrolabe epidemic 24 Name Time Load Weblogic? SMTP? Word Version swift 2011 2.0 0 1 6.2 falcon 1971 1.5 1 0 4.1 cardinal 2004 4.5 1 0 6.0 swift.cs.cornell.edu swift 2011 2.0 cardinal 2201 3.5 Name Time Load Weblogic ? SMTP? Word Version swift 2003 .67 0 1 6.2 falcon 1976 2.7 1 0 4.1 cardinal 2201 3.5 1 1 6.0 CS5412 Spring 2016 cardinal.cs.cornell.edu

25. State Merge: Core of Astrolabe epidemic 25 Name Time Load Weblogic? SMTP? Word Version swift 2011 2.0 0 1 6.2 falcon 1971 1.5 1 0 4.1 cardinal 2201 3.5 1 0 6.0 swift.cs.cornell.edu Name Time Load Weblogic ? SMTP? Word Version swift 2011 2.0 0 1 6.2 falcon 1976 2.7 1 0 4.1 cardinal 2201 3.5 1 1 6.0 CS5412 Spring 2016 cardinal.cs.cornell.edu

26. Observations 26 Merge protocol has constant cost One message sent, received (on avg) per unit time. The data changes slowly, so no need to run it quickly we usually run it every five seconds or so Information spreads in O(log N) time But this assumes bounded region size In Astrolabe, we limit them to 50-100 rows CS5412 Spring 2016

27. Big systems 27 A big system could have many regions Looks like a pile of spreadsheets A node only replicates data from its neighbors within its own region CS5412 Spring 2016

28. Scaling up and up 28 With a stack of domains, we don t want every system to see every domain Cost would be huge So instead, we ll see a summary Name Time Load Weblogic ? Weblogic Load Load Load Time Time SMTP? Word Version Version Name Time Load SMTP? Word ? Name Time Weblogic ? Weblogic SMTP? Word Version Version swift 2011 2.0 0 1 6.2 Name Time SMTP? Word swift 2011 2.0 0 1 6.2 falcon 1976 Name 2.7 Time 1 0 ? Weblogic ? Weblogic 4.1 ? Weblogic SMTP? Word Version Version swift 2011 2.0 0 1 6.2 falcon 1976 Name 2.7 1 0 4.1 Load SMTP? Word cardinal cardinal 2201 swift 3.5 2011 1 1 0 6.0 1 swift 2011 2.0 0 1 6.2 falcon 1976 Name 2.7 1 0 ? 4.1 Load SMTP? Word Version 2201 swift 3.5 2011 1 1 0 6.0 1 2.0 6.2 falcon 1976 2.7 1 0 4.1 cardinal cardinal 2201 swift 3.5 2011 1 1 0 6.0 1 2.0 6.2 falcon 1976 2.7 1 0 4.1 2201 3.5 1 1 6.0 2.0 6.2 falcon 1976 2.7 1 0 4.1 cardinal cardinal 2201 3.5 1 1 6.0 falcon 1976 2.7 1 0 4.1 2201 3.5 1 1 6.0 cardinal 2201 3.5 1 1 6.0 CS5412 Spring 2016 cardinal.cs.cornell.edu

29. Astrolabe builds a hierarchy using a P2P protocol that assembles the puzzle without any servers 29 Dynamically changing query output is visible system-wide SQL query summarizes Name Name Name Avg Load Load Load Avg Avg WL contact WL contact WL contact SMTP contact SMTP contact SMTP contact SF SF SF 2.6 2.6 2.2 123.45.61.3 123.45.61.3 123.45.61.3 123.45.61.17 123.45.61.17 123.45.61.17 NJ NJ NJ 1.8 1.8 1.6 127.16.77.6 127.16.77.6 127.16.77.6 127.16.77.11 127.16.77.11 127.16.77.11 data Paris Paris Paris 3.1 3.1 2.7 14.66.71.8 14.66.71.8 14.66.71.8 14.66.71.12 14.66.71.12 14.66.71.12 Name Name Name Load Load Load Weblogic? Weblogic? Weblogic? SMTP? SMTP? SMTP? Word Version Version Version Word Word Name Name Name Load Load Load Weblogic? Weblogic? Weblogic? SMTP? SMTP? SMTP? Word Version Version Version Word Word swift swift swift 2.0 2.0 1.7 0 0 0 1 1 1 6.2 6.2 6.2 gazelle gazelle gazelle 1.7 1.7 4.1 0 0 0 0 0 0 4.5 4.5 4.5 falcon falcon falcon 1.5 1.5 2.1 1 1 1 0 0 0 4.1 4.1 4.1 zebra zebra zebra 3.2 3.2 0.9 0 0 0 1 1 1 6.2 6.2 6.2 cardinal cardinal cardinal 4.5 4.5 3.9 1 1 1 0 0 0 6.0 6.0 6.0 gnu gnu gnu .5 .5 2.2 1 1 1 0 0 0 6.2 6.2 6.2 New Jersey San Francisco CS5412 Spring 2016

30. Large scale: fake regions 30 These are Computed by queries that summarize a whole region as a single row Gossiped in a read-only manner within a leaf region But who runs the gossip? Each region elects k members to run gossip at the next level up. Can play with selection criteria and k CS5412 Spring 2016

31. Hierarchy is virtual data is replicated 31 Yellow leaf node sees its neighbors and the domains on the path to the root. Name Avg Load WL contact SMTP contact SF 2.6 123.45.61.3 123.45.61.17 Gnu runs level 2 epidemic because it has lowest load NJ 1.8 127.16.77.6 127.16.77.11 Paris 3.1 14.66.71.8 14.66.71.12 Falcon runs level 2 epidemic because it has lowest load Name Load Weblogic? SMTP? Word Version Name Load Weblogic? SMTP? Word Version swift 2.0 0 1 6.2 gazelle 1.7 0 0 4.5 falcon 1.5 1 0 4.1 zebra 3.2 0 1 6.2 cardinal 4.5 1 0 6.0 gnu .5 1 0 6.2 New Jersey San Francisco CS5412 Spring 2016

32. Hierarchy is virtual data is replicated 32 Green node sees different leaf domain but has a consistent view of the inner domain Name Avg Load WL contact SMTP contact SF 2.6 123.45.61.3 123.45.61.17 NJ 1.8 127.16.77.6 127.16.77.11 Paris 3.1 14.66.71.8 14.66.71.12 Name Load Weblogic? SMTP? Word Version Name Load Weblogic? SMTP? Word Version swift 2.0 0 1 6.2 gazelle 1.7 0 0 4.5 falcon 1.5 1 0 4.1 zebra 3.2 0 1 6.2 cardinal 4.5 1 0 6.0 gnu .5 1 0 6.2 New Jersey San Francisco CS5412 Spring 2016

33. Worst case load? 33 A small number of nodes end up participating in O(logfanoutN) epidemics Here the fanout is something like 50 In each epidemic, a message is sent and received roughly every 5 seconds We limit message size so even during periods of turbulence, no message can become huge. CS5412 Spring 2016

34. Who uses Astrolabe? 34 Amazon doesn t use Astrolabe in this identical form, but they built gossip-based monitoring systems based on the same ideas. They deploy these in S3 and EC2: throughout their big data centers! For them, Astrolabe-like mechanisms track overall state of their system to diagnose performance issues They also automate reaction to temporary overloads CS5412 Spring 2016

35. Example of overload handling 35 Some service S is getting slow Astrolabe triggers a system wide warning Everyone sees the picture Oops, S is getting overloaded and slow! So everyone tries to reduce their frequency of requests against service S What about overload in Astrolabe itself? Could everyone do a fair share of inner aggregation? CS5412 Spring 2016

36. Idea that one company had 36 Start with Astrolabe approach But instead of electing nodes to play inner roles, just assign them roles, left to right N-1 inner nodes, hence N-1 nodes play 2 aggre- gation roles and one lucky node just has one role What impact will this have on Astrolabe? CS5412 Spring 2016

37. 37Worlds worst aggregation tree! D L B J F N A C E G G gossips with H and learns e I K M O An event e occurs at H P learns O(N) time units later! A B C D E F G H I J K L M N O P CS5412 Spring 2016

38. What went wrong? 38 In this horrendous tree, each node has equal work to do but the information-space diameter is larger! Astrolabe benefits from instant knowledge because the epidemic at each level is run by someone elected from the level below CS5412 Spring 2016

39. Insight: Two kinds of shape 39 We ve focused on the aggregation tree But in fact should also think about the information flow tree CS5412 Spring 2016

40. Information space perspective 40 Bad aggregation graph: diameter O(n) D L B J F N H G E F B A C D L K I J N M O P A C E G I K M O A B C D E F G H I J K L M N O P Astrolabe version: diameter O(log(n)) I A A I E M C D A B G H E F A C E G I K M O K L M N I J O P A B C D E F G H I J K L M N O P CS5412 Spring 2016

41. Where will this approach go next? 41 Cornell research: Shared State Table (SST) Idea is to use RDMA to implement a super-fast shared state table for use in cloud data centers Tradeoff: simplified model but WAY faster CS5412 Spring 2016

42. Name Load Weblogic? SMTP? Word Version SST details gazelle 1.7 0 0 4.5 zebra 3.2 0 1 6.2 gnu .5 1 0 6.2 42 Starts like Astrolabe with a table for a region But no hierarchy, SST is like a one level Astrolabe And the implementation doesn t use gossip . To understand SST, you need to know about RDMA This is a new kind of hardware Allows DMA transfers over the optical network from sender memory to receiver memory, at full network data rates which can be 10Gbps or even 100Gbpsm CS5412 Spring 2016

43. RDMA Concept 43 RDMA is a new standard for Reliable DMA Memory Region Bits moved by the optical network directly from sender memory to receiver memory Sender Node Receiver Node CS5412 Spring 2016

44. RDMA acronyms: Enliven a party 44 RDMA: Short for Remote Direct Memory Access.. Offers two modes: Block transfers (can be very large): binary from sender to destination; sender specifies source and destination provides a receive buffer One-sided read: Receiver allows sender to do a read from memory without any software action on receiver side. One-sided write: same idea, but allows writes Three ways to obtain RDMA: RDMA on Infiniband network hardware: popular in HPC RDMA on fast Ethernet: also called RDMA/ROCE. Very new and we need experience to know how well this will work. Issue centers on a new approach to flow control. RDMA in software: SoftROCE. A fake RDMA for running RDMA code on old machines lacking RDMA hardware. Tunnels on TCP IB Verbs: this is the name for the software library used to access RDMA CS5412 Spring 2016

45. SST uses RDMA to replicate rows 45 With SST, when a node updates its row, RDMA is used to copy the row to other nodes Then the compute model offers predicates on the table, aggregation, and upcalls to event handlers As many as 1M or 2M, per node, per second This is so much faster than Astrolabe that comparison is kind of meaningless maybe 100,000x faster CS5412 Spring 2016

46. SST: Like Astrolabe, but one region, fast updates 46 Name Time Load Weblogic? SMTP? Word Version swift 2011 2.0 0 1 6.2 falcon 1971 1.5 1 0 4.1 cardinal 2004 4.5 1 0 6.0 swift.cs.cornell.edu Name Time Load Weblogic ? SMTP? Word Version swift 2003 .67 0 1 6.2 falcon 1976 2.7 1 0 4.1 cardinal 2201 3.5 1 1 6.0 CS5412 Spring 2016 cardinal.cs.cornell.edu

47. SST Predicate with Upcall 47 Basically, each program registers code like this: if (some if (some- -predicate predicate- -becomes true) becomes true) { { do this do this upcall upcall logic logic } } SST evaluates predicates and issues upcalls in parallel, on all machines, achieving incredible speed After machine A changes some value, a predicate on machine B might fire in 1us or less delay CS5412 Spring 2016

48. Use case comparison 48 Astrolabe SST Kind of slow Used at large scale to monitor big data centers, for data mining Uses hierarchical structure to scale, gossip as its communication protocol Crazy fast Used mostly at rack scale: maybe 64 or 128 nodes at a time Has a flat structure, and uses RDMA to transfer rows directly at optical network speeds of 10 or even 100Gbps CS5412 Spring 2016

49. Summary 49 First we saw a way of using Gossip in a reliable multicast (although the reliability is probabilistic) Astrolabe uses Gossip for aggregation Pure gossip isn t ideal for this and competes poorly with flooding and other urgent protocols But Astrolabe introduces hierarchy and is an interesting option that gets used in at least one real cloud platform SST uses a similar model but Implemented with one-sided RDMA reads, writes Less scalable, but runs 100,000x faster CS5412 Spring 2016