RAPIER: Integrating Routing and Scheduling for Co?ow-aware Data Center Networks

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RAPIER Integrating Routing and Scheduling for
Co?ow-aware Data Center Networks

• Yangming Zhao (UESTC), Kai Chen (HKUST), Wei Bai
  (HKUST),
• Minlan Yu (USC), Chen Tian (HUST), Yanhui Geng
  (Huawei),
• Yiming Zhang (NUDT), Dan Li (Tsinghua), Sheng
  Wang (UESTC)
• zhaoyangming_at_uestc.edu.cn

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Coflow-aware Traffic Optimization

• Why traffic optimization in data center
  networks?
• Improve traffic scalability
• Improve QoS
• Why coflow-aware?
• Minimize average flow completion time
• Minimize average coflow completion time
• How to optimize network traffic?
• Routing (Hedera, Micro-TE)
• Scheduling (Varys, Baraat, pFabric)

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Motivation Example
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Motivation Example
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Motivation Example
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Desirable Properties of RAPIER
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Main idea

• Coflow-level Routing
• Distribute all the flows in a coflow evenly in
  the network
• Coflow-level Scheduling
• Minimal remaining time first principle
• Starvation-free
• Scheduling a coflow first if it is waiting for a
  long time
• Work-conserving
• Distribute all the bandwidth if there is a demand
  to serve
• Coexistence
• Route mice flows with ECMP and highest priority

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RAPIER in a Nutshell
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Minimize single coflow completion time
Non-linear with integer variable
Non-linear with integer variable
Non-linear without integer variable
Linear programming
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Relaxation and Rounding
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Bandwidth Allocation
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Implementation

• Central controller
• Algorithm 1
• End host enforcement modules
• OpenFlow based explicit routing
• Bandwidth enforcement

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Experiment on Testbed

• Pronto 3295 48-port Gigabit Ethernet switch with
  PicOS 2.04 system
• Each server has a 4-core Intel E5-1410 2.8GHz
  CPU, 8G memory, 500GB hard disk and 1G Ethernet
  NICs
• The OS of servers is Debian 6.0 64bit version
  with Linux 2.6.38.3 kernel

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Experiment Results
Coflow ID Flow ID source Destination Volume(GB) Coflow Completion Time(s) Coflow Completion Time(s) Coflow Completion Time(s)
Coflow ID Flow ID source Destination Volume(GB) RAPIER Routing Baseline
1 1 2 3 M1 M2 M3 M4 M5 M9 3.17 5.29 5.29 50.6 84.1 107.1
2 4 5 M8 M6 M6 M5 10.6 5.29 100.9 203.0 289.5
3 6 7 M7 M9 M4 M6 17.9 10.6 201.1 204.1 289.2
Average completion time Average completion time Average completion time Average completion time Average completion time 117.5 163.7 228.6
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Simulation Settings

• C/C based flow level simulator
• CPLEX 10.0 for solving LP
• Fattree?VL2 with 512 servers
• Flows in a coflow arrive simultaneously
• Inter-co?ow arrival rate follows a Poisson
  distribution

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Impact of coflow width

• Reduce average CCT by up to 79.44 in Fattree,
  and 55.55 in VL2
• Routing-only scheme performs better when coflow
  width is small.
• Scheduling-only scheme performs better when
  coflow width is large.

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Impact of co?ow number

• RAPIER keeps relatively stable performance with
  different co?ow number.
• Scheduling-only scheme is more effective in VL2
  than in Fattree

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Impact of inter-co?ow arrival interval

• The average CCT is decreased with the increase of
  average inter-co?ow arrival interval
• The same trend as scheduling-only scheme when the
  inter-coflow arrival interval is small
• The same trend as routing-only scheme when the
  inter-coflow arrival interval is large

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Simulation Results Summary

• In light-load scenario, routing contributes more
  by solving the flow path collision problem in
  ECMP.
• In heavy-load scenario, scheduling contributes
  more by determining the sending order of
  flows/coflows.
• RAPIER integrates both schemes and gets all the
  benefits from them.

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Conclusion

• RAPIER is a system which optimizes average co?ow
  completion time in DCNs by integrating routing
  and scheduling.
• RAPIER follows the minimal remaining time first
  to reduce the average coflow completion time.
• We implement the prototype of RAPIER
• Simulation results show that RAPIER can greatly
  reduce the average coflow completion time in DCNs.

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• The end!
• Thanks for your attention!