kddi - openstack summit 2016/red hat nfv mini summit

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  • KDDI Research Inc. Proprietary and Confidential

    Troubles prediction and detection based on Distributed Monitoring & Analytics frameworkYuki Kasuya KDDI Research

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    Agenda

    1

    2

    3

    4

    Motivation / Problem

    Solution / Architecture

    Use case

    Conclusion

    2

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    1. Motivation / Problem

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    RemoteOperation Center

    Development Division

    nRequire reliability

    nMany operators needed

    nHigh cost operationl change to low cost

    4

    Current operation style

    Data Center24hours / 365daysHW replacement

    daytimesoftware bug

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    Driver: Changing operation style

    ReactiveOperation

    PeriodicOperation

    ProactiveOperation

    Cost Reduction

    Agility

    Proactive

    Reactive

    24/7 maintenance

    9am - 5pmmaintenance

    Automation

    5

    before broken,do prevention

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    What is key point?

    n Auto healing process

    1. Fault detection by monitoring system

    1. Recovery plan by OSS/Orchestrator

    1. Auto healing by Orchestrator

    For fast recovery, real-time fault detection / prediction is key point.

    6

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    Problems for proactive operation

    NFVI NFVI

    VMVNFpollerpoller

    verbosedata

    verbosedata

    notifier

    evaluator

    collector

    DBn Centralized monitoring architecture

    l Difficult to real-time(fine-grained) monitoring

    l High load to collect a lot of data

    n Generally, delay of collecting data affects several area.Now, the time has come to consider to enhance the architecture.

    7

    delay

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    2. Solution / Architecture

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    n Distribute each function into computing nodes

    l Monitoring process is complete in each computing node

    l Real-time(Fine-grained) monitoring

    l Scale with the number of computing nodes

    Distributed Monitoring and Analytics (DMA)

    NFVI NFVI

    VMVNF

    notifier

    evaluator

    collector

    analyzer

    evaluator

    collector

    analyzer

    evaluator

    collector

    DB

    DB DB

    analyticsresult concisedata

    pollerpoller

    9

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    Architecture detail

    Poller/Notification

    libvirtAPI

    SNMP Get

    SNMP Trap

    CollectorDatabase

    MeterTranslat

    or

    Evaluator

    AnalyticsEngineFault

    Detection(Predictio

    n)

    Statistical Analysis

    Alarm Correlation

    Transmitter

    Fault Alarm

    StatisticalData

    OpenStackAPI

    ...

    Perf. Data

    Perf.data (CPU/Memory...)

    Guest.

    (Alarm)Faultdata

    Perf.data (CPU/Memory...)

    Host.

    (Alarm)Faultdata

    Fault Data

    ...

    NFVI

    10

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    Centralized Distributed

    Fault (predict, silent)

    Account

    Performance

    (Micro burst)

    Target area

    n Centralizedl cloud platform

    n Distributedl NFVl carrier grade network

    11

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    3. Use case

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    Use Case 1: Prediction using machine learning

    T. Niwa et al., Universal Fault Detection for NFV using SOM-based Clustering, APNOMS 2015.

    Distributed machine learning

    13

    Demo @MWC2016

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    Use Case 2: Detect micro burst traffic

    Finer performance data processing

    M. Miyazawa et al., In-network real-time performance monitoring with distributed event processing, NOMS 2014.

    Centralized approach cannot detect the bursts. DMA can achieve x1000 finer monitoring.

    - - 8

    - - 5

    -B75 1 B A 1A11 5 8 A5 C1

    B AA 1 8

    051 A8 5 5 8 7 8 5ADA8 8 1A8 8 55 5

    Time%(sec)

    0

    20

    40

    60

    80

    100

    0 20 40 60 80 100 120 140

    Band

    width%U

    tiliza

    tion%(%

    )

    5 A 1 8 5 1 175 5 A

    8 A 82BA5 1 175 5 A

    C5 H.8 2B A H

    70

    18Mbps

    DMA approach

    Centralized approach

    14

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    4. Conclusion

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    nChange to proactive / low cost operation

    nFault detection / prediction is key point

    nDistributed Monitoring and Analytics is suitable for proactive operation

    Conclusion

    16

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    nStart to discuss how to integrate DMA to OpenStack

    Join us

    17

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