1 / 15

Enabling Flow-level Latency Measurements across Routers in Data Centers

Enabling Flow-level Latency Measurements across Routers in Data Centers. Parmjeet Singh, Myungjin Lee Sagar Kumar, Ramana Rao Kompella. Latency-critical applications in data centers. Guaranteeing low end-to-end latency is important Web search (e.g., Google’s instant search service)

mikasi
Télécharger la présentation

Enabling Flow-level Latency Measurements across Routers in Data Centers

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Enabling Flow-level Latency Measurements across Routers in Data Centers Parmjeet Singh, MyungjinLee SagarKumar, RamanaRaoKompella

  2. Latency-critical applications in data centers • Guaranteeing low end-to-end latency is important • Web search (e.g., Google’s instant search service) • Retail advertising • Recommendation systems • High-frequency trading in financial data centers • Operators want to troubleshoot latency anomalies • End-host latencies can be monitored locally • Detection, diagnosis and localization through a network: no native support of latency measurements in a router/switch

  3. Prior solutions • Lossy Difference Aggregator (LDA) • Kompella et al. [SIGCOMM ’09] • Aggregate latency statistics • Reference Latency Interpolation (RLI) • Lee et al. [SIGCOMM ’10] • Per-flow latency measurements More suitable due to more fine-grained measurements

  4. Deployment scenario of RLI • Upgrading all switches/routers in a data center network • Pros • Provide finest granularity of latency anomaly localization • Cons • Significant deployment cost • Possible downtime of entire production data centers • In this work, we are considering partial deployment of RLI • Our approach: RLI across Routers (RLIR)

  5. Overview of RLI architecture • Goal • Latency statistics on a per-flow basis between interfaces • Problem setting • No storing timestamp for each packet at ingress and egress due to high storage and communication cost • Regular packets do not carry timestamps Ingress I Router Egress E

  6. Overview of RLI architecture Linear interpolation line • Premise of RLI: delay locality • Approach 1) The injector sends reference packets regularly 2) Reference packet carries ingress timestamp 3) Linear interpolation: compute per-packet latency estimates at the latency estimator 4) Per-flow estimates by aggregating per-packet estimates L Reference Packet Injector Ingress I Egress E 2 1 2 1 Delay 1 L R R 2 Interpolated delay Latency Estimator Time

  7. Full vs. Partial deployment • Full deployment: 16 RLI sender-receiver pairs • Partial deployment: 4 RLI senders + 2 RLI receivers • 81.25 % deployment cost reduction Switch 1 Switch 3 Switch 5 Switch 2 Switch 4 Switch 6 RLI Sender (Reference Packet Injector) RLI Receiver (Latency Estimator)

  8. Case 1: Presence of cross traffic • Issue: Inaccurate link utilization estimation at the sender leads to high reference packet injection rate • Approach • Not actively addressing the issue • Evaluation shows no much impact on packet loss rate increase • Details in the paper Switch 1 Switch 3 Switch 5 Switch 2 Switch 4 Switch 6 Link utilization estimation on Switch 1 Cross Traffic Bottleneck Link RLI Sender (Reference Packet Injector) RLI Receiver (Latency Estimator)

  9. Case 2: RLI Sender side • Issue: Traffic may take different routes at an intermediate switch • Approach: Sender sends reference packets to all receivers Switch 1 Switch 3 Switch 5 Switch 2 Switch 4 Switch 6 RLI Sender (Reference Packet Injector) RLI Receiver (Latency Estimator)

  10. Case 3: RLI Receiver side • Issue: Hard to associate reference packets and regular packets that traversed the same path • Approaches • Packet marking: requires native support from routers • Reverse ECMP computation: ‘reverse’ engineer intermediate routes using ECMP hash function • IP prefix matching at limited situation Switch 1 Switch 3 Switch 5 Switch 2 Switch 4 Switch 6 RLI Sender (Reference Packet Injector) RLI Receiver (Latency Estimator)

  11. Deployment example in fat-tree topology RLI Sender (Reference Packet Injector) Reverse ECMP computation / IP prefix matching IP prefix matching RLI Receiver (Latency Estimator)

  12. Evaluation • Simulation setup • Trace: regular traffic (22.4M pkts) + cross traffic (70M pkts) • Simulator • Results • Accuracy of per-flow latency estimates 10% / 1% injection rate RLI Sender RLI Receiver Regular Traffic Reference packets Packet Trace Traffic Divider Switch1 Switch2 Cross Traffic Injector Cross Traffic

  13. Accuracy of per-flow latency estimates Bottleneck link utilization: 93% 67% 10% injection 10% injection 1% injection 1% injection CDF Relative error 1.2% 4.5% 18% 31%

  14. Summary • Low latency applications in data centers • Localization of latency anomaly is important • RLI provides flow-level latency statistics, but full deployment (i.e., all routers/switches) cost is expensive • Proposed a solution enabling partial deployment of RLI • No too much loss in localization granularity (i.e., every other router)

  15. Thank you! Questions?

More Related