Offworker: An Offloading Framework for Parallel Web Applications

An Chi Liu, Yi Ping You*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


More and more applications are shifting from traditional desktop applications to web applications due to the prevalence of mobile devices and recent advances in wireless communication technologies. The Web Workers API has been proposed to allow for offloading computation-intensive tasks from applications’ main browser thread, which is responsible for managing user interfaces and interacting with users, to other worker threads (or web workers) and thereby improving user experience. Prior studies have further offloaded computation-intensive tasks to remote servers by dispatching web workers to the servers and demonstrated their effectiveness in improving the performance of web applications. However, the approaches proposed by these prior studies expose potential vulnerabilities of servers due to their design and implementation and do not consider multiple web workers executing in a concurrent or parallel manner. In this paper, we propose an offloading framework (called Offworker) that transparently enables concurrent web workers to be offloaded to edge or cloud servers and provides a more secure execution environment for web workers. We also design a benchmark suite (called Rodinia-JS), which is a JavaScript version of the Rodinia parallel benchmark suite, to evaluate the proposed framework. Experiments demonstrated that Offworker effectively improved the performance of parallel applications (with up to 4.8x of speedup) when web workers were offloaded from a mobile device to a server. Offworker introduced only a geometric mean overhead of 12.1% against the native execution for computation-intensive applications. We believe Offworker offers a promising and secure solution for computation offloading of parallel web applications.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2022 - 23rd International Conference, Proceedings
EditorsRichard Chbeir, Helen Huang, Fabrizio Silvestri, Yannis Manolopoulos, Yanchun Zhang, Yanchun Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783031208904
StatePublished - 2022
Event23rd International Conference on Web Information Systems Engineering, WISE 2021 - Biarritz, France
Duration: 1 Nov 20223 Nov 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13724 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Web Information Systems Engineering, WISE 2021


  • JavaScript
  • Offloading
  • Parallelism
  • Web workers


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