On workflow scheduling for latency-sensitive edge computing applications

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


With the rapid proliferation of edge computing-based solutions, many edge computing applications use the cloud for data processing and analysis. However, latency-sensitive applications have low latency requirements and can be very bandwidth hungry, so processing their collected data through cloud servers is not efficient and cost-effective. For example, traffic management and health condition monitoring applications, require real-time data processing at the network edge to respond immediately to unexpected events. These applications are typically executed as workflows with dependent tasks that require careful scheduling to allocate the appropriate resources to the tasks so that the execution is done in a way that satisfies the users' target functions. In this work, we evaluate some traditional scheduling heuristics for the execution of workflow tasks in an edge-computing scenario. Our goal is to compare their performance in terms of execution time and cost and show that these heuristics, previously used in scheduling in the context of cloud environments, can also be used in edge computing scenarios. The results show that the MinMin and PSO scheduling algorithms offer the best results with regard to execution time and cost.

Original languageEnglish
Pages (from-to)958-963
Number of pages6
JournalProcedia Computer Science
Publication statusPublished - 2023
Event14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 - Leuven, Belgium
Duration: Mar 15 2023Mar 17 2023


  • Edge computing
  • cloud computing
  • latency
  • scheduling

ASJC Scopus subject areas

  • General Computer Science


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