Accelerating page loads via streamlining JavaScript engine for distributed learning

Distributed learning based on JavaScript-based frontends is typically implemented at the endpoint to maximize performance. Yet, JavaScript-based frontends often experience suboptimal performance. To reconcile these disparities in performance between EDGE and endpoint deployments, strategic optimizat...

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Vydané v:Information sciences Ročník 675; s. 120713
Hlavní autori: Liang, Chen, Wang, Guoyu, Li, Ning, Wang, Zuo, Zeng, Weihong, Xiao, Fu-an, Tan, Yu-an, Li, Yuanzhang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 01.07.2024
Predmet:
JavaScript engine
Optimization
Page loads
Page loads
JavaScript engine
Optimization
ISSN:0020-0255, 1872-6291
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Shrnutí:Distributed learning based on JavaScript-based frontends is typically implemented at the endpoint to maximize performance. Yet, JavaScript-based frontends often experience suboptimal performance. To reconcile these disparities in performance between EDGE and endpoint deployments, strategic optimization is essential, particularly for preserving privacy in distributed learning. Real-time streaming optimizations are imperative to align the performance of disparate components for smooth integration. The reliance on JavaScript for various web functionalities can lead to increased resource consumption and slower page loads. Thus, we introduce a streamlined JavaScript engine designed to optimize structural patterns in JavaScript code, with three key enhancements. Firstly, we reduce the computational burden of the JavaScript engine necessary for setting up the browser's runtime environment. Secondly, we refine the parsing process for specific code patterns, boosting the efficiency of our lightweight engine. Thirdly, we streamline the Inter-Process Communication (IPC) to maintain high performance, even with limited memory resources. Our evaluations demonstrate that our approach reduces the median Total Computation Time (TCT) by 45.2%, and surpasses existing leading solutions, Siploader and Prepack, with improvements ranging from 1.13× to 1.39×.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2024.120713