HyperElasticity™ - the key to handling spike events in cloud platforms
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The document discusses how to handle spike events in web traffic. Standard cloud elasticity can adjust to gradual load changes but not unexpected spikes, which can lead to lost revenue from slow page loads. When traffic spikes, cloud platforms take 15 minutes to boot new server instances, during which pages are slow and users leave the site. They also bill in hourly increments, charging for unused resources long after spikes. To address this, the document recommends platforms with faster boot times under 10 minutes, sub-hourly billing increments, and metrics that provide timely performance feedback to optimize for spikes and calculate return on investment from recovered and lost revenue.
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- 1. * Handling Spike Events Reza Rassool, CTO Kwaai Oak
- 4. Recovered revenue (x$100) Lost revenue (x$100) Servers ROI% (x100) Page load time (s) Bounce Rate% (x10) Traffic (100s) 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 0 Kwaai Oak 1 2 3 4 * 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of the day 4 12/13/2013
- 6. Standard cloud elasticity can cope with slowly* varying loads But what about unexpected spikes? * Kwaai Oak 6 12/13/2013
- 7. 300.00 * Linux boot latency results in lost revenue 250.00 Total Traffic (x100) Servers 200.00 * Cloud billing Bounce rate % unfairly pads charges long after the spike is over. Cloud Cost 150.00 100.00 Not to scale 50.00 500 550 600 650 700 750 800 Linux Boot time 15mins Kwaai Oak 7 12/13/2013
- 8. * Faster booting nodes – see GCE * Sub hour billing * Google GCE (10 min) * MS Azure (1 min) * Kwaai Oak 8 12/13/2013
- 10. * Faster boots * Sub-hour billing * Also * Timely feedback metrics * User centric performance metrics * Load test (SOASTA) * Don’t be afraid to compute ROI * Lost revenue * Recovered revenue * Kwaai Oak 10 12/13/2013
Editor's Notes
- The property of a system to stretch in proportion to the application of a load and then to return to its original state as the load is removed.