Web applications created using web languages such as HTML, CSS, and JavaScript are widely used regardless of execution environments, owing to their portability and popularity. Since JavaScript language is conventionally used for complex computations in large-scale web apps, many optimization techniques have been proposed to accelerate the JavaScript performance. However, most of these optimizations speed up JavaScript engines at the expense of consuming more memory. Hence, memory consumption becomes an additional concern in the JavaScript field. Based on the research on memory status, we unearthed that a substantial portion of the heap memory is allocated for JavaScript source code, particularly in lightweight JavaScript engines, which may range from 13.2% to a maximal 52.5% of the entire heap. To resolve this memory issue, this article suggests a new memory optimization method, called dynamic code compression that dynamically compresses the source code and keeps its compressed form instead of the source string. A novel heuristic is proposed to timely compress the source code. We also re-design the internal structure of the JavaScript engine to efficiently compress the source code without incurring any conflict. Using our code compression method, we could reduce the entire heap memory by up to 43.3% and consistently downsize the overall heap size. From an evaluation of standard benchmarks, our approach showed just 2.7% degradation in performance with negligible compression overhead, proving the feasibility of the code compression technique.

https://doi.org/10.1002/spe.3186