Abstract
<title>Abstract</title> <p>Serverless computing has emerged as a key paradigm in modern application development, enabling scalable, on-demand execution with minimal operational overhead while allowing developers to focus on application logic rather than infrastructure management. Despite these advantages, serverless computing still suffers from cold start latency, which can lead to performance degradation and cascading failures under varying workload conditions. Existing approaches, including resource over-provisioning and warm up strategies designed to mitigate cold starts, rely on workload and heuristic assumptions and do not perform consistently across scenarios with varying workload intensities and system configurations. These approaches primarily focus on reducing cold start latency rather than systematically analysing the conditions under which cascading failures emerge. The proposed research prioritises a deeper understanding of cold start and cascading failure problems in serverless computing that iteratively evolves and evaluates fault configurations to identify worst case operating conditions in serverless environments. In this work, a Genetic Algorithm-based framework that integrates the principles of chaos engineering and resilience engineering has been proposed to analyse system behaviour by intentionally injecting adverse conditions in order to analyse failure propagation characteristics. In addition , a resilience boundary is introduced to quantify the workload threshold at which a serverless system transitions from a resilient to an unstable operating state, enabling quantitative evaluation of different cold start mitigation strategies under identical fault conditions. Experimental evaluation shows that the proposed framework effectively identifies high impact fault scenarios and provides a systematic approach for assessing the robustness of serverless workflows. The results further demonstrate that mitigation techniques such as pre warming, provisioned concurrency, and snapshot-based execution improve system resilience by extending the workload range over which stable system operation can be maintained.</p>