Serverless functions have emerged as a prominent paradigm in software deployment, providing automated resource scaling, resulting in demand-based operational expenses. One of the most significant challenges associated with serverless functionsis the cold start delay, preventing organisations with latency-critical web applications from adopting a serverless technology.

Existing research on the cold start problem primarily focuses on mitigating the delay by modifying and optimising serverless platform technologies. However, these solutions have predominantly yielded modest reductions in time delay. Consequently, the purpose of this study is to establish conditions and circumstances under which the cold start issue can be addressed through the type of approach presented in this study.

Through a design science research methodology, a software artefact named AdaptiveServerless Invocation Predictor (ASIP) was developed to mitigate the cold start issue through monitoring web application user traffic in real-time. Based on the user traffic, ASIP preemptively pre-initialises serverless functions likely to be invoked, to avoid cold start occurrences. ASIP was tested against a realistic workload generated by test participants.

Evaluation of ASIP was performed through analysing the reduction in time delay achieved and comparing this against existing cold start mitigation strategies. The results indicate that predicting serverless function invocations based on real-time traffic analysis is a viable approach, as a tangible reduction in response time was achieved. Conclusively, the cold start mitigation strategy assessed and presented in this study may not provide a sufficiently significant mitigation effect relative to the required implementation effort and operational expenses. However, the study has generated valuable insights regarding circumstantial factors concerning cold start mitigation. Consequently, this study provides a proof of concept for a more sophisticated version of the mitigation strategy developed in this study, with greater potential to provide a significant delay reduction without requiring substantial computational resources.