PLATO:%20Predictive%20Latency-Aware%20Total%20Ordering

1
PLATO Predictive Latency-Aware Total Ordering

• Mahesh Balakrishnan
• Ken Birman
• Amar Phanishayee

2
Total Ordering

• a.k.a Atomic Broadcast
• delivering messages to a set of nodes in the same
  order
• messages arrive at nodes in different orders
• nodes agree on a single delivery order
• messages are delivered at nodes in the agreed
  order

3
Modern Datacenters

• Applications
• E-tailers, Finance, Aerospace
• Service-Oriented Architectures,
  Publish-Subscribe, Distributed Objects, Event
  Notification
• Totally Ordered Multicast!
• Hardware
• Fast high-capacity networks
• Failure-prone commodity nodes

4
Total Ordering in a Datacenter
Updates are Totally Ordered
Replicated Service
Totally Ordered Multicast is used to consistently
update Replicated Services Latency of Multicast
? System Consistency Requirement order
multicasts consistently, rapidly, robustly
5
Multicast Wishlist

• Low Latency!
• High (stable) throughput
• Minimal, proactive overheads
• Leverage hardware properties
• HW Multicast/Broadcast is fast, unreliable
• Handle varying data rates
• Datacenter workloads have sharp spikes and
  extended troughs!

6
State-of-the-Art

• Traditional Protocols
• Conservative
• Latency-Overhead tradeoff
• Example Fixed Sequencer
• Simple, works well
• Optimistic Total Ordering
• deliver optimistically, rollback if incorrect
• Why this works No out-of-order arrival in LANs
• Optimistic total ordering for datacenters?

7
PLATO Predictive Ordering

• In a datacenter, broadcast / multicast occurs
  almost instantaneously
• Most of the time, messages arrive in same order
  at all nodes.
• Some of the time, messages arrive in different
  orders at different nodes.
• Can we predict out-of-order arrival?

8
Reasons for Disorder Swaps
Typical Datacenter Diameter 50-500 microseconds
Out-of-order arrival can occur when the
inter-send interval between two messages is
smaller than the diameter of the network
9
Reasons for Disorder Loss

• Datacenter networks are over-provisioned
• Loss never occurs in the network
• Datacenter nodes are cheap
• Loss occurs due to end-host buffer overflows
  caused by CPU contention

10
Emulab Testbed (Utah)
11
Cornell Testbed
12
Disorder Emulab3
Percentage of swaps and losses goes up with data
rate
At 2800 packets per sec, 2 of all packet pairs
are swapped and 0.5 of packets are lost.
13
Disorder
14
Predicting Disorder

• Predictor Inter-arrival time of consecutive
  packets into user-space
• Why?
• Swaps simultaneous multicasts
• ? low inter-arrival time
• Loss kernel buffer overflow
• ? sequence of low inter-arrival times

15
Predicting Disorder

• 95 of swaps and 14 of all pairs are within 128
  µsecs

Inter-arrival time of swaps
Inter-arrival time of all pairs
Cornell Datacenter, 400 multicasts/sec
16
Predicting Disorder
17
PLATO Design

• Heuristic If two packets arrive within ? µsecs,
  possibility of disorder
• PLATO
• Heuristic Lazy Fixed Sequencer
• Heuristic works ? zero (?) latency
• Heuristic fails ? fixed sequencer latency

18
PLATO Design
API optdeliver, confirm, revoke Ordering
Layer Pending Queue Packets suspected to be
out-of-order, or queued behind suspected
packets Suspicious Queue Packets optdelivered
to the application, not yet confirmed
19
PLATO Design
20
Performance
? Fixed Sequencer ? PLATO
At small values of ?, very low latency of
delivery but more rollbacks
21
Performance
Latency of both Fixed-Sequencer and PLATO
decreases as throughput increases
22
Performance
Traffic Spike PLATO is insensitive to data rate,
while Fixed Sequencer depends on data rate
23
Performance
Latency is as good as static ? parameterization
?? is varied adaptively in reaction to rollbacks
24
Conclusion

• First optimistic total order protocol that
  predicts out-of-order delivery
• Slashes ordering latency in datacenter settings
• Stable at varying loads
• Ordering layer of a time-critical protocol stack
  for Datacenters