Information-Agnostic Flow Scheduling for Commodity Data Centers Wei - - PowerPoint PPT Presentation

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Information-Agnostic Flow Scheduling for Commodity Data Centers Wei - - PowerPoint PPT Presentation

Dec 03, 2022 42 likes •505 views

Information-Agnostic Flow Scheduling for Commodity Data Centers Wei Bai , Li Chen, Kai Chen, Dongsu Han (KAIST), Chen Tian (NJU), Hao Wang Sing Group @ Hong Kong University of Science and Technology USENIX NSDI 2015, Oakland, USA 1 Data Center

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Wei Bai, Li Chen, Kai Chen, Dongsu Han (KAIST), Chen Tian (NJU), Hao Wang Sing Group @ Hong Kong University of Science and Technology

Information-Agnostic Flow Scheduling for Commodity Data Centers

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USENIX NSDI 2015, Oakland, USA

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Data Center Transport

  • Cloud applications

– Desire low latency for short messages

  • Goal: Minimize flow completion time (FCT)

– Many flow scheduling proposals…

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The State-of-the-art Solutions

  • PDQ [SIGCOMM’12]
  • pFabric [SIGCOMM’13]
  • PASE [SIGCOMM’14]
  • …

All assume prior knowledge of flow size information to approximate ideal preemptive Shortest Job First (SJF) with customized network elements

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The State-of-the-art Solutions

  • PDQ [SIGCOMM’12]
  • pFabric [SIGCOMM’13]
  • PASE [SIGCOMM’14]
  • …

All assume prior knowledge of flow size information to approximate ideal preemptive Shortest Job First (SJF) with customized network elements

Not feasible for some applications

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The State-of-the-art Solutions

  • PDQ [SIGCOMM’12]
  • pFabric [SIGCOMM’13]
  • PASE [SIGCOMM’14]
  • …

All assume prior knowledge of flow size information to approximate ideal preemptive Shortest Job First (SJF) with customized network elements

Hard to deploy in practice

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Question

Without prior knowledge of flow size information, how to minimize FCT in commodity data centers?

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Design Goal 1

Without prior knowledge of flow size information, how to minimize FCT in commodity data centers?

Information-agnostic: not assume a priori knowledge of flow size information available from the applications

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Design Goal 2

Without prior knowledge of flow size information, how to minimize FCT in commodity data centers?

FCT minimization: minimize average and tail FCTs of short flows & not adversely affect FCTs of large flows

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Design Goal 3

Without prior knowledge of flow size information, how to minimize FCT in commodity data centers?

Readily-deployable: work with existing commodity switches & be compatible with legacy network stacks

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Question

Without prior knowledge of flow size information, how to minimize FCT in commodity data centers?

Our answer: PIAS

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PIAS’S DESIGN

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Design Rationale

  • PIAS performs Multi-Level Feedback Queue

(MLFQ) to emulate Shortest Job First

Priority 1 Priority 2 Priority K

……

High Low

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Design Rationale

  • PIAS performs Multi-Level Feedback Queue

(MLFQ) to emulate Shortest Job First

Priority 1 Priority 2 Priority K

……

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Simple Example Illustrating PIAS

Congestion

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Flow 1 with 10 packets and flow 2 with 2 packets arrive Priority 3

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Flow 1 and 2 transmit simultaneously Priority 3

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Flow 2 finishes while flow 1 is demoted to priority 2 Priority 3

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Flow 3 with 2 packets arrives Priority 3

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Flow 3 and 1 transmit simultaneously Priority 3

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Flow 3 finishes while flow 1 is demoted to priority 3 Priority 3

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Flow 4 with 2 packets arrives Priority 3

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Flow 4 and 1 transmit simultaneously Priority 3

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Priority 3 Flow 4 finishes while flow 1 is demoted to priority 4

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Simple Example Illustrating PIAS

Priority 1 Priority 2 Priority 4 Priority 3 Eventually, flow 1 finishes in priority 4

With MLFQ, PIAS can emulate Shortest Job First without prior knowledge of flow size information

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How to implement?

Priority 1 Priority 2 Priority K

……

  • Strict priority queueing on switches
  • Packet tagging as a shim layer at end hosts
  • 𝐿 priorities:

𝑄𝑗 1 ≤ 𝑗 ≤ 𝐿

  • 𝐿 − 1 demotion thresholds:

𝛽𝑘 (1 ≤ 𝑘 ≤ 𝐿 − 1)

  • The threshold to demote priority

from 𝑄

𝑘−1 to 𝑄 𝑘 is 𝛽𝑘−1

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How to implement?

Priority 1 Priority 2 Priority K

……

  • Strict priority queueing on switches
  • Packet tagging as a shim layer at end hosts

1

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How to implement?

Priority 1 Priority 2 Priority K

……

  • Strict priority queueing on switches
  • Packet tagging as a shim layer at end hosts

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How to implement?

Priority 1 Priority 2 Priority K

……

  • Strict priority queueing on switches
  • Packet tagging as a shim layer at end hosts

2

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  • Thresholds depend on:

– Flow size distribution – Traffic load

  • Solution:

– Solve a FCT minimization problem to calculate demotion thresholds

  • Problem:

– Traffic is highly dynamic

Determine Thresholds

Traffic variations -> Mismatched thresholds

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Impact of Mismatches

High Low

10MB 10MB 20KB

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  • When the threshold is perfect (20KB)

Impact of Mismatches

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  • When the threshold is too small (10KB)

Impact of Mismatches

Increased latency for short flows

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  • When the threshold is too large (1MB)

Impact of Mismatches

Increased latency for short flows

Leverage ECN to keep low buffer occupation

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  • When the threshold is too small (10KB)

Handle Mismatches

ECN can keep low latency If we enable ECN

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  • When the threshold is too large (1MB)

Handle Mismatches

ECN can keep low latency If we enable ECN

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PIAS in 1 Slide

  • PIAS packet tagging

– Maintain flow states and mark packets with priority

  • PIAS switches

– Enable strict priority queueing and ECN

  • PIAS rate control

– Employ Data Center TCP to react to ECN

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Testbed Experiments

  • PIAS prototype

– http://sing.cse.ust.hk/projects/PIAS

  • Testbed Setup

– A Gigabit Pronto-3295 switch – 16 Dell servers

  • Benchmarks

– Web search (DCTCP paper) – Data mining (VL2 paper) – Memcached

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Small Flows (<100KB)

Web Search Data Mining

Compared to DCTCP, PIAS reduces average FCT of small flows by up to 47% and 45%

47% 45%

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NS2 Simulation Setup

  • Topology

– 144-host leaf-spine fabric with 10G/40G links

  • Workloads

– Web search (DCTCP paper) – Data mining (VL2 paper)

  • Schemes

– Information-agnostic: PIAS, DCTCP and L2DCT – Information-aware: pFabric

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Overall Performance

Web Search Data Mining

PIAS has an obvious advantage over DCTCP and L2DCT in both workloads.

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Small Flows (<100KB)

Simulations confirm testbed experiment results

Web Search Data Mining

40% - 50% improvement

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Comparison with pFabric

PIAS only has 4.9% performance gap to pFabric for small flows in data mining workload

Web Search Data Mining

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Conclusion

  • PIAS: practical and effective

– Not assume flow information from applications – Enforce Multi-Level Feedback Queue scheduling – Use commodity switches & legacy network stacks Information-agnostic FCT minimization Readily deployable

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Thanks!

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Starvation

  • Measurement

– 5000 flows, 5.7 million MTU-sized packets – 200 timeouts, 31 two consecutive timeouts

  • Solutions

– Per-port ECN pushes back high priority flows when many low priority flow get starved – Treating a long-term starved flow as a new flow

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Persistent Connections

  • Solution: periodically reset flow states based
  • n more behaviors of traffic

– When a flow idles for some time, we reset the bytes sent of this flow to 0. – Define a flow as packets demarcated by incoming packets with payload within a single connection

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