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Optimize Website Speed WordPress
Using an endto- end implementation and a proof of concept installation in CloudLab, we show that Pytheas improves QoE over a system by up to 31% on average and 78% on percentile of persession QoE. Content providers are increasingly using data-driven mechanisms to maximize quality of experience (QoE). While prediction-based mechanisms show promising QoE improvements, they’re incomplete while they(1) suffer from a number of known biases (e.g., incomplete visibility) and (2) cannot reply to sudden changes (e.g., load changes). Drawing a parallel out of machine learning, we assert that data-driven QoE optimization needs to instead be cast as a real-time exploration and manipulation (E2) process instead of as a prediction issue. Many existing procedures formulate this process as a forecast problem of learning optimal decisions (e.g., server, bitrate, relay) based on detected QoE of recent sessions. Adopting E2 in system applications, however, introduces vital architectural (e.g., how to upgrade decisions in realtime with fresh data) and algorithmic (e.g., capturing complex connections between session features vs.
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WordPress First Load Slow
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- Turn off several unnecessary visual effects
- WP Engine’s Startup program
- Effective browser caching
- Shortcode Generator
- Minimizing request dimensions
- Chrome dev tools page pounds: 1.6Mb
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