Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

YouTube videos are no longer supported on SlideShare

View original on YouTube

Trafficshifting: Avoiding Disasters &
Improving Performance at Scale
Michael Kehoe
Staff Site Reliability Engineer
LinkedIn
2
Overview
• Problem Statement
• Solution – How LinkedIn trafficshift’s
• Datacenter shifting
• PoP steering
• Challenges ...
Upcoming SlideShare
Couchbase Connect 2016
Couchbase Connect 2016
Loading in …3
×
1 of 36

APRICOT 2017: Trafficshifting: Avoiding Disasters & Improving Performance at Scale

0

Share

Download to read offline

LinkedIn serves traffic for its 467 million members from four data centers and multiple PoPs spread geographically around the world. Serving live traffic from from many places at the same time has taken us from a disaster recovery model to a disaster avoidance model where we can take an unhealthy data center or PoP out of rotation and redistribute its traffic to a healthy one within minutes, with virtually no visible impact to users. The geographical distribution of our infrastructure also allows us to optimize the end-user's experience by geo routing users to the best possible PoP and datacenter.

This talk provide details on how LinkedIn shifts traffic between its PoPs and data centers to provide the best possible performance and availability for its members. We will also touch on the complexities of performance in APAC, how IPv6 is helping our members and how LinkedIn stress tests data centers verify its disaster recovery capabilities.

More Related Content

You Might Also Like

Related Books

Free with a 30 day trial from Scribd

See all

Related Audiobooks

Free with a 30 day trial from Scribd

See all

APRICOT 2017: Trafficshifting: Avoiding Disasters & Improving Performance at Scale

  1. 1. Trafficshifting: Avoiding Disasters & Improving Performance at Scale Michael Kehoe Staff Site Reliability Engineer LinkedIn
  2. 2. 2 Overview • Problem Statement • Solution – How LinkedIn trafficshift’s • Datacenter shifting • PoP steering • Challenges of APAC region • IPv4 vs IPv6 • Questions
  3. 3. $ whoami 3 Michael Kehoe • Staff Site Reliability Engineer (SRE) @ LinkedIn • Production-SRE team • Funny accent = Australian + 3 years American
  4. 4. $ whatis SRE 4 Michael Kehoe • Site Reliability Engineering • Operations for the production application environment • Responsibilities include • Architecture design • Capacity planning • Operations • Tooling • Responsibilities include DNS/ CDN management & Traffic infrastructure
  5. 5. 5 Terminology • PoP - Where LinkedIn terminates incoming requests. • Fabric – Datacenter with full LinkedIn production stack deployed • Loadtest – Stress test of a Fabric – to simulate a disaster scenario
  6. 6. Disaster Recovery 6 Problem Statement • Fail between Fabrics • Performance of applications is degraded • Validate disaster recovery (DR) scenario • Expose bugs and suboptimal configurations via loadtest • Planned maintenance • Fail between PoP’s • Mitigate impact of a 3rd party provider maintenance/ failure (e.g. transport links) • Software/ Configuration Bugs
  7. 7. Performance 7 Problem Statement • Fabric Assignment • Assign preferred and secondary fabric to all members based on: • Member location • Capacity • PoP/ CDN steering • Use GeoDNS to steer user to ‘best’ PoP • Use RUM DNS to steer users to ’best’ CDN
  8. 8. United States Performance (Global) 8 Problem Statement
  9. 9. APAC Performance (APAC cities) 9 Problem Statement
  10. 10. Delta US & APAC 10 Problem Statement
  11. 11. Site Speed 11 Problem Statement • Site Speed affects User Engagement • User Engagement affects page-views & transactions • Bottom Line: Site Speed has an impact on revenue
  12. 12. LinkedIn’s Traffic Architecture 12 Solution
  13. 13. LinkedIn’s Traffic Architecture 13 Solution
  14. 14. Fabric shifting 14 Solution • Stickyrouting • Using a Hadoop job, we calculate a primary and secondary datacenter for the user based on location • This data is stored in a Key-Value store (Espresso) • Stickyrouting serves this information over a RESTful interface to our Edge PoP’s
  15. 15. Fabric shifting 15 Solution • Different traffic types are partitioned and controlled separately • Logged-In vs Logged-out • CDN’s • Monitoring • Microsites • Logged-in users are placed into ‘buckets’ • Buckets are marked online/ offline to move site traffic
  16. 16. Fabric shifting 16 Solution • Stickyrouting – Benefits • Ensure we serve the request as close to the user as possible • Capacity management for datacenters • We can assign a percentage of users to a datacenter • Enables personal data routing (PDR) • Only store data where we need it
  17. 17. Fabric shifting Automation 17 Solution
  18. 18. Fabric shifting Automation 18 Solution
  19. 19. Fabric Shifting 19 Solution
  20. 20. Fabric Shifting Load tests 20 Solution
  21. 21. Fabric Shifting Loadtests 21 Solution
  22. 22. LinkedIn’s Traffic Architecture 22 Solution
  23. 23. LinkedIn’s PoP Distribution 23 Solution
  24. 24. LinkedIn’s PoP Architecture 24 Solution • Using IPVS - Each PoP announces a unicast address and a regional anycast address • APAC, EU and NAMER anycast regions • Use GeoDNS to steer users to the ‘best’ PoP • DNS will either provide users with an anycast or unicast address for www.linkedin.com • US and EU members is nearly all anycast • APAC is all unicast
  25. 25. LinkedIn’s PoP DR 25 Solution • Sometimes need to fail out of PoP’s • 3rd party provider issues (e.g. transit links going down) • Infrastructure maintenance • Withdraw anycast route announcements • Fail healthchecks on proxy to drain unicast traffic
  26. 26. LinkedIn’s PoP Performance 26 Solution • PoP DNS Steering • LinkedIn currently uses GeoDNS for routing • Piloting RumDNS • Pick the best PoP based on network, not country • CDN Steering • Mix CDN’s to get best performance • Constantly evaluate performance/ availability • Automatically adjust CDN weighting
  27. 27. LinkedIn’s PoP Performance 27 Solution US CDN request time 50th percentile 24 hours
  28. 28. Working around fiber cuts 28 APAC Challenges • Case Study: Fail out of India PoP due to fiber cuts Connection Time for Indian members (90th percentile)
  29. 29. ASN 15802 ASN 5384 GeoDNS Suboptimal PoP’s 29 APAC Challenges Source: http://www.submarinecablemap.com/#/submarine-cable/bay-of-bengal-gateway-bbg SingaporeMumbai 45 ms 220 ms 70 ms ASN 15802 RTT to Singapore is (220+70) 290ms (all at 50th percentile)
  30. 30. GeoDNS Suboptimal PoP’s 30 APAC Challenges London Dublin SingaporeMumbai 160 ms 45 ms ASN 15802 ASN 5384 70 ms 35 ms 350 ms Hong Kong160 ms
  31. 31. GeoDNS Suboptimal PoP’s 31 APAC Challenges 600 700 800 900 1000 1100 1200
  32. 32. Performance & Adoption 32 IPv4 vs IPv6 • IPv6 performs better for our members • Less request time-outs on IPv6 for mobile users • Mobile carriers are adopting IPv6 faster • Win for LinkedIn and our members! • In July 2014 (IPv6 launch): 3% of traffic was IPv6 • Today: ~12% of traffic is IPv6
  33. 33. Key Takeaways 33 Conclusion • Application level traffic engineering is extremely important for content providers • RUM data is extremely useful for finding anomalies • Route traffic based on performance, not just location • IPv6 performs better for LinkedIn users
  34. 34. 34 Questions?

Editor's Notes

  • Good morning, my name is Michael Kehoe and in this presentation I’m going to talk about how LinkedIn shifts traffic between it’s PoP’s and datacenters to avoid disaster and improve site performance at scale
  • So this morning I want to talk about the problem that we’re trying to solve, particularly in the context of APAC which is extremely challenging for internet companies

    Then we’ll deep-dive into how LinkedIn solves these problems to improve our availability and site performance. Specifically we’ll look at:
    Datacenter shifting
    PoP steering


    We’ll look at some of the challenges of operating in the APAC region, briefly talk about IPv6 adoption and then I’ll take questions
  • So who am I?
    I’m a Staff Site Reliability Engineer (commonly referred to as SRE) at LinkedIn.
    I am on a team called Production-SRE, our team charter includes:
    Developing applications to improve MTTD and MTTR
    Build tools for efficient site issue troubleshooting, issue detection & correlation
    Assist in restoring stability to services during site critical issues

    Yes I have a slightly strange accent, it’s Australian with three 3 years of American.
  • Site Reliability Engineering
    A term coined by Ben Treynor from Google
    You may also find it being called Devops/ Appops or Production Engineering
    Skillset based of:
    Sysadmin
    Network Engineer
    Architect
    Troubleshooter
    Software Engineer
    Role consists of:
    Architecture design
    Capacity planning
    Application Operations – Keeping the site healthy
    Writing automation and tooling
    SRE role/ philosophy differs between companies. At LinkedIn, SRE’s are responsible for DNS/ CDN management and traffic infrastructure
  • So before we deep-dive, let’s go over some terminology

    PoP – Where LinkedIn terminates incoming requests to it’s datacenters. Spread geographically across the world
    Fabric – Datacenter where the full LinkedIn application stack is deployed. LinkedIn has 3 datacenters in the US and one in Singapore
    Loadtest – Where we stress test a Fabric to simulate a disaster.
  • What are the use-cases for shifting traffic for Disaster Recovery purposes?

    Fabric:
    Performance of applications is degraded
    Site may be slow or users get errors
    Validate disaster recovery
    Plan for disasters (natural/ infrastructure/ code)
    Expose code bugs and suboptimal configurations via loadtest
    When the application infrastructure is under stress, easier to expose sub optimal configuration/ code
    Planned maintenance
    Intrusive infrastructure maintenance that may cause impact
    PoP
    Transport provider maintenance
    More common in Asia given the large number of submarine cables we utilize
    Software bugs
  • So let’s look at the performance side of the equation.
    How can shifting traffic improve performance:
    Fabric:
    Members use the closest datacenter to them
    Manage capacity of a datacenter

    PoP:
    Steering Users to the best possible PoP gives us significant performance advantage
    By measuring CDN availability/ performance using RUM (talk about RUM and how it works), we can speed-up page-load-time by 50%
  • **** NOTE: Move to excel and remove values ***
    Average linkedin.com page load time for countries using US Data-centers (measured by Catchpoint – All Major Metro Nodes around the world)
  • Average linkedin.com page load time for countries using APAC Data-centres (measured by Catchpoint – Top 10 APAC metro nodes).
  • Delta between US and APAC performance. Average is 2.5s
  • LinkedIn has done extensive research on the impact site-speed has on user-engagement.
    From this research we know that slow page load times affects engagement and transaction

    This in-turn affects our revenue. This is imporant!
  • So what does LinkedIn’s traffic architecture look like
    DNS routes users to the ‘best’ PoP (more on that later)
    IPVS (IP Virtual Server, a Linux kernel module) announces Unicast and Anycast addresses for www.linkedin.com and terminates TCP connections
    ATS (Apache Traffic Server) terminates SSL sessions and proxies requests to datacenters
    Stickyrouting service (talk about in a minute) tells the PoP (specifically ATS) which datacenter/ fabric to send the request to
    ATS in the datacenter proxies requests to frontend services
  • Let’s talk about stickyrouting and Fabric-Shifting
  • We run an offline Hadoop job to calculate primary and secondary datacenters for users.
    Hadoop is a distributed computing mechanism that proceses large datasets
    We store this data in an in-house key-value store named Espresso
    Stickyrouting serves information over a RESTFul interface to our Edge-PoP’s
  • At LinkedIn, we partition our traffic into various classes so we can control them independently
    Logged-in vs Logged-out
    CDN traffic
    Monitoring traffic
    Microsites

    Logged-in users get assigned to a bucket (an arbitrary partition)
    We then online/ offline buckets in a fabric to manipulate the distribution of traffic between fabrics
  • Benefits:
    Serve the request as close to the user
    Capacity management - Ensure that data-centers aren’t overloaded
    Personal data routing – lowers cost to serve
  • My team built ’TrafficShift’ app to help automate datacenter routing’
    We’ve automated fail-outs of datacenters
    Also allows us to do automated load-testing of our datacenters
  • You can see, LTX1 (Texas datacenter) is failed out
  • Example of failing out of East Coast Datacenter
    Top graph – Online buckets
    Bottom graph – Distribution of traffic
  • Automation to validate DR
    Tell the engine which datacenter to stress, how much traffic, and what time periods and it will execute for us
    Traffic engine watches our alerting system to ensure we do not negatively impact the member experience
  • Let’s talk about how users connect to LinkedIn’s PoP’s
  • LinkedIn’s PoP locations
    Note that PoP in India is red – means it’s offline – talk about that further later
  • Sometimes need to fail out for 3rd party issues – remember the red dot on the PoP map.
    Steer users to the next-best PoP. In this case. India to Singapore
    Note the slow traffic tail-off in TMU1 – DNS TTL’s not being honored

    For Anycast traffic, we withdraw the prefix announcement
    For Unicast, Fail healthchecks that DNS providers use to check if we are serving from that site
  • Remember that red dot before.
    Sometimes by pure necessity, we need to fail out of PoP’s to mitigate impact or potential impact.
    In this case, move India traffic from India PoP to Singapore
    This does have an impact on client connect times and also page-load times.

  • UAE has 2 ASNs and GeoDNS routes both to India
    5384 – That’’s ok
    15802 – Not ok
  • RUM DNS recognizes optimal PoPs for ASN 15802
    Two better paths, Hong Kong and London/ Dublin
  • Drop in connect time after the change
  • IPv6 – performs up to 40% better

    We’ve grown from 3% IPv6 traffic in July 2014 to over 12% today
  • ×