Epidemics

1
Epidemics

• Presented By
• Lucas Cook and Wade Fagen
• CS 525, The University of Illinois (UIUC)
• 6 February 2007

2
History

• Two schools of algorithms multicast
• Proactive
• Reactive
• Existing Algorithms/Implementations
• SRM
• IP Multicast (best-effort)
• NNTP (gossip)
• IRC (hierarchical multicast)
• PSYC (multicast more web-like than IRC)

3
Multicast Routing

• Reliable Multicast Routing
• Ensure that a message sent from any node is
  received by all other nodes within any
  distributed system.
• but we dont live in an ideal world.

4
Multicast Routing

• Three general categories
• Algorithms that provide strong reliability
  properties.
• Ex atomic multicasts

Round
1
2
3

5
Atomic Multicast

• Nodes only process at the beginning of rounds
• New rounds dont start until all previous
  messages are received

Round
1
2
3

6
Multicast Routing

• Three general categories
• strong reliability algorithms
• best-effort reliability algorithms
• Ex MUSE algorithm
• Provides no assurance of end-to-end reliability
  assurance
• Problems and solutions exist both at the physical
  network layer and the overlay area
• Focus of distributed systems overlay

7
Best Effort Multicast Routing

• Many algorithms implement some neighbor-based
  approach

8
Best Effort Multicast Routing

• End-to-end assurance may be lost by one nodes
  failure

9
Multicast Routing

• Three general categories
• strong reliability algorithms
• best-effort reliability algorithms
• proactively probabilistic multicast algorithms
• Provides predictable reliability
• Goal Achieve better reliability than
  best-effort without the overhead of strong
  reliability
• Method Epidemics

10
Epidemic Algorithms

• Epidemics help ensure probabilistic end-to-end
  reliability with an assurance of almost all or
  almost none structure
• Tradeoff between scale and reliability epidemics
  allow for expansive scale with near-perfect
  reliability

11
Epidemic Algorithms

• To be less verbose, the following citations are
  used throughout the presentation
• 1 Bimodal multicast, K Birman et al, ACM TOCS
  1999
• 2 Epidemic algorithms for replicated database
  maintenance, A. Demers et al, PODC 1987.
• 3 Gossip-based ad hoc routing, Z. Haas et al,
  Infocom 2002

12
Epidemic Algorithms in Databases

• Site updating has been a key problem since the
  beginning of distributed database work
• Data is injected at one site
• Data needs to be updated at every site

Incoming Transaction
13
Epidemic Algorithms in Databases

• Classic Examples
• NNTP
• First use of e-mail servers
• etc

14
Epidemic Algorithms in Databases

• Three core concepts
• Direct Communication
• Bottleneck!
• Anti-Entropy Measure
• Possible full comparison (slow!)
• Rumor Management
• Only updates!

15
Epidemic Algorithms in Databases

• Three states of a message /node
• Susceptive Message not received at node
• Infective Message is actively propagated by node
• Removed Message is no longer actively propagated
  by node

16
Epidemic Algorithms in Databases

• Decide on two phase algorithm
• Phase 1 Rumor Mongering
• Probabilistic spread of messages to (hopefully)
  nearly all nodes
• Considerations between Push/Pull models

17
Epidemic Algorithms in Databases

• Decide on two phase algorithm
• Phase 1 Rumor Mongering
• Phase 2 Epidemic (Anti-Entropy)
• Ran periodically in the background
• Ran at each node

18
Epidemic Push/Pull

• Generic Epidemic Message
• An epidemic message contains a summary of recent
  events
• Two types push and pull
• The different types of messages allow
  formalization of the mathematics

19
Epidemic Push/Pull

• A push is a message sent from some infected
  site to a susceptible site.

push
Incoming Transaction
20
Epidemic Push/Pull

• A pull is a message sent from some susceptible
  site to an infected site

push
push
pull
Incoming Transaction
21
Epidemic Algorithms in Databases

• With the general idea, the specifics of 2
  relate to databases
• Two primary distributed operations
• Data Insertion INSERT, UPDATE
• Data Deletion DELETE
• Epidemic message for DELETE are augmented with a
  death certificate
• In 2, SELECT is simply done locally at each
  distributed end point of the database

22
Epidemic Algorithms in Databases

• Results published in 2
• Key Result Replacing deterministic algorithms
  for database consistency
• Actual Results Simulation-based solution only
• Showed internal based results
• Simulation of traditional schemes wasnt done for
  accurate comparison

23
Bimodal Multicast

• 1 presents a bimodal multicast algorithm called
  pbcast
• pbcast probabilistic broadcast

24
The pbcast Algorithm (from 1)

• Six Properties
• Atomicity (probabilistically)
• Throughput Stability
• Ordering (FIFO)
• Multicast Stability
• Detection of Lost Messages
• Scalability
• Acceptability of soft failures

25
The pbcast Algorithm (from 1)

• Two sub-protocols
• Part 1 Hierarchical broadcast
• Unreliable, best-effort approach
• Part 2 Anti-entropy to correct packet loss if
  needed
• Results in predictable end-to-end assurances

26
The pbcast Algorithm (from 1)

• Basic Hierarchical Broadcast

m1
m1
m1
Node 1 1 Node 2 1 Node 3 1 Node 4 1
27
The pbcast Algorithm (from 1)

• Basic Hierarchical Broadcast

m1
m2
m2
m2
m1
m1
m1
m1
m1
m2
m2
m2
m2
m2
m1
m1
Node 1 1, 2 Node 2 1, 2 Node 3 1, 2 Node
4 1, 2
28
The pbcast Algorithm (from 1)

• Basic Hierarchical Broadcast

m2
m1
m1
m2
m1
m2
m1
m2
m1
m1
m1
m1
m1
m1
m1
m1
m1
m3
m2
m2
m2
m2
m2
m3
m2
m3
m2
m2
m2
m1
m1
m1
m1
m1
m1
Node 1 1, 2 Node 2 1, 2 Node 3 1, 2,
3 Node 4 1, 2
Node 1 1, 2 Node 2 1, 2 Node 3 1, 2,
3 Node 4 1, 2
Node 1 1, 2 Node 2 1, 2 Node 3 1, 2,
3 Node 4 1, 2
29
The pbcast Algorithm (from 1)

• Basic Hierarchical Broadcast

m2
m1
m4
m1
m1
m1
m1
m2
m1
m2
m1
m2
m1
m1
m2
m1
m2
m1
m2
m1
m4
m2
m1
m4
m4
m4
m1
m1
m1
m1
m1
m1
m1
m1
m1
m1
m1
m1
m1
m1
m3
m2
m2
m2
m3
m2
m3
m2
m2
m2
m2
m3
m2
m3
m2
m3
m2
m2
m2
m2
m2
m2
m2
m2
m2
m2
m2
m2
m1
m4
m1
m1
m1
m1
m1
m4
m4
m1
m4
m1
m4
m1
m4
m1
m4
m1
m4
m1
m4
m1
m4
m1
m4
Node 1 1, 2, 4 Node 2 1, 2, 4 Node 3 1,
2, 3, 4 Node 4 1, 2, 4
Node 1 1, 2, 4 Node 2 1, 2, 4 Node 3 1,
2, 3, 4 Node 4 1, 2, 4
30
The pbcast Algorithm (from 1)

• Basic Hierarchical Broadcast

m4
m2
m1
m5
m4
m5
m1
m3
m2
m2
m1
m4
m5
Node 1 1, 2, 4 Node 2 1, 2, 4, 5 Node 3
1, 2, 3, 4, 5 Node 4 1, 2, 4, 5
31
The pbcast Algorithm (from 1)

• The anti-entropy protocol runs simultaneously
  with the broadcast messages
• Protocol runs in rounds
• Ran at every process
• Rounds longer than round-trip time
• Paper suggests 100ms
• maybe a traffic-based metric would be better?

32
The pbcast Algorithm (from 1)

• Anti-entropy round

m1
m1
m1
m1
m1
m1
m5
m5
m2
m3
m4
m2
m3
m2
m4
m3
m2
m4
m3
m2
Rounds need not be synchronized across nodes!
33
The pbcast Algorithm (from 1)

• Anti-entropy round

m1
m1
m1
m1
m1
m1
m5
m5
m2
m3
m4
m2
m3
m2
m4
m3
m2
m4
m3
m2
For example sake, well assume they happento all
occur at the same time across all nodes
34
The pbcast Algorithm (from 1)

• Anti-entropy round
• Gossip Messages
• Each process chooses another random process and
  sends a summary of its recent messages

35
The pbcast Algorithm (from 1)

• Gossip Messages

m1
m1
m1
m1
m1
m1
m1
m1
m1
m5
m5
m5
m5
m5
m4
m3
m2
m2
m3
m2
m4
m3
m2
m4
m3
m4
m2
m2
m3
m2
m4
m3
m2
m4
m3
m2
Node 1 ? Node 3 1 Node 2 ? Node 1 1, 2 Node
3 ? Node 2 1, 2 Node 4 ? Node 2 1
36
The pbcast Algorithm (from 1)

• Gossip Messages

m1
m1
m1
m1
m1
m1
m5
m5
m4
m3
m2
m2
m3
m2
m4
m3
m2
m4
m3
m2
Node 1 ? Node 4 1, 2 Node 2 ? Node 1 1, 2,
4 Node 3 ? Node 4 1, 2, 3, 4 Node 4 ? Node 3
1, 2
37
The pbcast Algorithm (from 1)

• Summary contains missed messages

m1
m5
m4
m3
m2
Node 1 ? Node 4 1, 2 Node 2 ? Node 1 1, 2,
4 Node 3 ? Node 4 1, 2, 3, 4 Node 4 ? Node 3
1, 2
Node 1 ? Node 4 1, 2 Node 2 ? Node 1 1, 2,
4 Node 3 ? Node 4 1, 2, 3, 4 Node 4 ? Node 3
1, 2
Node 1 ? Node 4 1, 2 Node 2 ? Node 1 1, 2,
4 Node 3 ? Node 4 1, 2, 3, 4 Node 4 ? Node 3
1, 2
38
The pbcast Algorithm (from 1)

• Anti-entropy round
• 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

39
The pbcast Algorithm (from 1)

• Summary contains missed messages

m1
m5
This is m3
m4
m3
m2
What was m3?
Node 1 ? Node 4 1, 2 Node 2 ? Node 1 1, 2,
4 Node 3 ? Node 4 1, 2, 3, 4 Node 4 ? Node 3
1, 2, 3
40
The pbcast Algorithm (from 1)

• Summary contains missed messages

m1
m5
m4
m3
m2
41
The pbcast Algorithm (from 1)

• Anti-entropy Protocol
• 1 suggests a number of optimizations
• Reduces numbers of rounds required to gossip
  about messages
• Reduces the redundant messages
• 1 also suggests a number of extensions
• Gossip about a messages to a fraction of all
  nodes (ex 100 in a 10,000 node system)

42
The pbcast Algorithm (from 1)

• Key Results

43
The pbcast Algorithm (from 1)
44
The pbcast Algorithm (from 1)
45
The pbcast Algorithm (from 1)
46
Epidemics

• With 1 and 2, a good overview of key epidemic
  behavior and strategies have been established.
• In 1 and 2, domain specific optimizations
  were often applied.

47
Ad-Hoc Routing Epidemics

• 3 focuses on reachability of epidemics for
  wireless ad hoc routing
• need to broadcast (multicast) to find routes
• theoretical/abstract simulation analysis
• 1 basic protocol, 4 extensions
• Goal find optimal configurations based on
  reachability vs. "load"

48
Ad-Hoc Routing Epidemics

• GOSSIP1(p, k)
• k The number of rounds of gossiping about the
  message with 100 probability
• p The probability of gossiping about the
  message after k rounds
• Optimal
• 1000 nodes (0.75, 4) to (0.65, 4)
• Backpropagation Effects

49
Ad-Hoc Routing Epidemics

• GOSSIP1(p, k)

50
Ad-Hoc Routing Epidemics

• GOSSIP2(p1, k, p2, n)
• Just like GOSSIP1(p1, k), unless the number of
  neighbors are n -- then GOSSIP1(p2, k)
• Intuition
• Nodes with low degrees will have the hardest time
  receiving information
• Optimal
• GOSSIP1(0.8, 4) performs like GOSSIP2(0.6, 4, 1,
  6) but with 13 more messages

51
Ad-Hoc Routing Epidemics

• GOSSIP3(p, k, m)
• Same as GOSSIP1(p, k), but if node receives less
  than m messages then the message should be
  gossiped about
• Intuition
• If a node is not seeing many copies of a message,
  the network is likely not completely infected
• Optimal
• GOSSIP3(0.65, 4, 1) is better than GOSSIP1(0.75,
  4) with 8 less messages

52
Ad-Hoc Routing Epidemics

• GOSSIP4(p, k, k)
• Just like GOSSIP1(p, k), but the node has
  knowledge of all nodes within a k node radius.
• Intuition
• Flooding of gossip messages only required at the
  boundary of your zone.
• The creation of zones will eliminate the
  backpropagation effect that is prone to
  GOSSIP1(p, k)
• Optimal
• Balance zones are required
• Larger zone requires more overhead

53
Ad-Hoc Routing Epidemics

• Similar to site percolation
• Problem Overview
• There exists some number of nodes lined up in a
  rectangle grid format

54
Ad-Hoc Routing Epidemics

• Similar to site percolation
• Problem Overview
• Each node (except edges) connect to all their
  neighbors (N, E, S, and W)

55
Ad-Hoc Routing Epidemics

• Similar to site percolation
• Problem Overview
• Question If p nodes are removed, are the
  connected nodes still connected?

p 40 of nodes removed
56
Ad-Hoc Routing Epidemics

• Similar to site percolation
• Problem Overview
• Question If p nodes are removed, are the
  connected nodes still connected?

p 40 of nodes removed
57
Overview

• 1 introduced bimodal routing and provided the
  pbcast algorithm
• 2 took the idea of bimodal routing and gossip
  protocols and applied it to a distributed
  database
• Finally, 3 provided well-founded results
  examining reachability of nodes

58
Discussion

• In both 1 and 2, a two layer approach was
  applied
• Phase 1 Best attempt broadcasting
• Phase 2 Anti-entropy
• 2 chooses epidemic broadcast model, and
  justifies by considering rebroadcast
• However, Anti-entropy will be run in the
  background no matter what happened at Phase 1
• Maybe a messages seen based metric for
  initializing Phase 2?

59
Discussion

• Epidemic algorithms is not appropriate in all
  cases
• The epidemic approach means messages will be
  received multiple times at a given site

60
Discussion

• Example Bandwidth-restrictive environments
• Streaming Video?
• Downlink Rate 3 mbps
• Assume n1,000 nodes
• Maximum unique content (average case)
• 3 mbps / lg(1,000) 0.3 mbps
• Is this at all ideal?
• and this assume a constant factor of 1.0.

61
Discussion

• In worst cases, epidemics require a near
  symmetrical connection
• Server sends Client and Phone video
• Server High download and high upload
• Client Moderate download and low upload
• Phone Low download and near-zero upload

Server
Client
Phone
Phone (1/2) bandwidth of Client
62
Discussion

• In worst cases, epidemics require a near
  symmetrical connection
• Server sends Client and Phone video
• Server High download and high upload
• Client Moderate download and low upload
• Phone Low download and near-zero upload

Server
Client
Phone
Transmission fails from Server ? Client
63
Discussion

• In worst cases, epidemics require a near
  symmetrical connection
• Server sends Client and Phone video
• Server High download and high upload
• Client Moderate download and low upload
• Phone Low download and near-zero upload

Server
Client
Phone
Client gossips (to phone) for any missed messages
64
Discussion

• In worst cases, epidemics require a near
  symmetrical connection
• Server sends Client and Phone video
• Server High download and high upload
• Client Moderate download and low upload
• Phone Low download and near-zero upload

Server
Client
Phone
Phone relays the missed message to Client
65
Discussion

• In worst cases, epidemics require a near
  symmetrical connection
• Server sends Client and Phone video
• Server High download and high upload
• Client Moderate download and low upload
• Phone Low download and near-zero upload

Server
Client
Phone
Phones bandwidth is so slow the Client falls
behind
66
Discussion

• Epidemic Security?
• Messages may be seen by 20 nodes before it
  reaches you.
• Digital signature from sender?
• Requires a large collection of private/public
  keys (one /node)
• Distributed trust mechanism?
• Other solutions?

67
Discussion

• Human-Centric Computing?
• Epidemic algorithms assumes each node knows of
  either the entire system or a large set of
  neighbors.
• What if a User A doesnt want to communicate
  with User B?
• Inference of user specific information may be
  able to be inferred by an unknown user
• More prevalent in partial multicast situations

68
Discussion

• In 3, what about metrics besides reachability?
• Amount of worth?
• Latency?
• Bandwidth?
• Can a model be mathematically formal and sound
  without explicitly taking those factors into
  account?

69
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