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Hotmails Performance Tuning Best Practices Aladdin A. Nassar Hotmails Performance Champion Microsoft.

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Presentation on theme: "Hotmails Performance Tuning Best Practices Aladdin A. Nassar Hotmails Performance Champion Microsoft."— Presentation transcript:

1 Hotmails Performance Tuning Best Practices Aladdin A. Nassar Hotmails Performance Champion Microsoft

2 Lessons Learned We cannot defy the laws of physics The truth is always out there History is bound to repeat itself if we dont learn from it Newer technologies ~ bigger guns to shoot ourselves with Performance is like beauty – it only shines from the inside Performance effects are asymmetric Customers do not care what we think our performance is We will always find ways to outgrow our capacity It is very easy to lose the forest in the trees Some of the best performance gains are the simplest of all

3 Laws of Physics The more bytes you transfer, the longer they take to load HTML is much faster & more resilient than Javascript Web Applications are thin applications WW performance is bound by network latency TCP packets cannot travel faster than the speed of light Performance Tuning is an over-constrained problem Asymmetric bandwidths can bottleneck upstream Fewer bigger files download faster than many small files

4 Page Load Time (PLT) Components Data Center 1 st Mile Last Mile Midgress Backbone Server Rendering Network Client Rendering Server Rendering Network Client Rendering Network Client Rendering - Server vs. Client side Rendering - End-users System Config - Page Weight Up/Down (KB) - Bandwidth (Kbps) - Network Latency (ms) - Packet Loss (%) - SW & HW Architecture Data Flow



7 TCP Win is defined by the Receiver TCP Win (Wireless) = 8 KB TCP Win (Win XP) = 16 KB Packet Loss Latency Bound BW Bound Theoretical BW eBW Cap 1.5 x Network Latency (ms) Max eBW = 8,000 x TCP Win (KB)

8 Performance Best Practices Identify your performance bottlenecks & critical paths Trim down your page weights up/downstream Move your contents closer to your customers Edge Caching Edge Computing Network Routing Optimization Geohosting Instrument performance across your entire network Build a closed feedback loop – fine tune, measure, fine tune, measure, etc.

9 Trim Page Weights (Downstream) Trim down your features to the core minimum Render most of your content on the server side Trim down your image sizes by: Minimizing their usage Image Clustering Reducing their color palettes Delay load, slow down, cap and monitor ads Use Cache Control, Expiration Dates & eTags effectively Group your static content into fewer bigger files Optimize between inline and stand-alone JS & CSS Full Postbacks vs. atomic updates using Ajax

10 Trim Page Weights (Upstream) Down/up connection speeds ~ 5 which means your bottleneck is most likely upstream Trim down your cookies by: Eliminating them altogether by moving your static contents to a different domain Optimizing their use Moving them away from your root domain and root path / Compressing them Grouping multiple smaller files into fewer bigger ones (image clustering) Trim down the number of requests and redirects (round trips)

11 Bandwidth Efficiency Identify your critical path Spread your downloads across multiple hostnames Hostname spreading can hurt narrowband On Demand / JIT downloading Control the sequencing of your downloads Unblock & defer your Javascript Minimize the browsers think time Use appropriate tools to analyze bandwidth efficiency

12 The Big Picture Outgrowing Moores Law Performance Based Design (PBD) Effect of Ads on Performance Relative Performance Index (RPI) Performance Consortium PLT1 vs. PLT2 Myths Performance vs. Capacity Planning

13 Q&A

14 Performance Tools Fiddler HTTP Watch HTTP Analyzer WANSim NetMon + Add-Ons RTA / VRTA / BWA Gomez Backbone/Last Mile Keynote Backbone In-House Automation Tools JS Instrumentation

15 Round Trips Packets Fast Retransmit Fast Recovery Tahoe Reno

16 Reference: Akamai

17 Relative Performance Index (RPI) RPI = the Dow Jones of Client Performance It is a method not tied to any data source T = Tolerable Level F = Frustration Level RPI = [0 – 100%] RPI = G Y % T/F Levels: - Defined by Usability Studies + Competitors - Defined per transaction - Function of Page Content Value 100% 50% 0% PLT (sec) T F G % Y % R % Page Views Relative Weight

18 Ads Refresh Rates Ads Refresh Rates (x Click / y Sec) Revenues Performance Today = 1 click / 2 sec) Target ~ 2 clicks / 60 sec)



21 Outgrowing Moore's Law

22 Ads > Application

23 The End

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