Making Magic Happen: Using Predictive Fetching in AJAX / RIA applications Jonathan Boutelle Uzanto

Who am I? www.jonathanboutelle.com www.uzanto.com www.themindcanvas.com

www.jonathanboutelle.com

www.uzanto.com

www.themindcanvas.com

Vanilla Web Application ( circa 2000 ) Http request Initial Html User action Html (with data embedded) User action Html (with data embedded) … HTML Server Dbase User action requiring data User action requiring data

Rich Internet Application ( circa 2003 ) Http request for app App (ActionScript & Flash) downloads Data UI Client Manager Server Dbase User action requiring data

Rich Internet Application ( circa 2004 ) Http request for app App (ActionScript & Flash) downloads User action requiring data Optionally preload data UI Client Manager Server Dbase

AJAX Application ( circa 2005 ) Http request for app User action requiring data Optionally preload data UI Client Manager Server Dbase App (Javascript & HTML) downloads

Building a model of user behavior Build naïve model Validate and refine model Be metric driven Responsiveness: % user data requests that were met with pre-fetched data Efficiency: the % of pre-fetched data that ended up being used

Build naïve model

Validate and refine model

Be metric driven

Responsiveness: % user data requests that were met with pre-fetched data

Efficiency: the % of pre-fetched data that ended up being used

Download if value > cost Value = value of reduced latency * odds the data will be needed Cost = cost of download * odds the data won’t be needed

Value = value of reduced latency * odds the data will be needed

Cost = cost of download * odds the data won’t be needed

Value vs Cost Odds a given piece of content will be needed $ Value = value of reduced latency * odds the data will be needed Cost = cost of download * odds the data won’t be needed