Function-as-a-Service is a cloud computing paradigm offering an event-driven
execution model to applications. It features serverless attributes by
eliminating resource management responsibilities from developers and offers
transparent and on-demand scalability of applications. Typical serverless
applications have stringent response time and scalability requirements and
therefore rely on deployed services to provide quick and fault-tolerant
feedback to clients. However, the FaaS paradigm suffers from cold starts as
there is a non-negligible delay associated with on-demand function
initialization. This work focuses on reducing the frequency of cold starts on
the platform by using Reinforcement Learning. Our approach uses Q-learning and
considers metrics such as function CPU utilization, existing function
instances, and response failure rate to proactively initialize functions in
advance based on the expected demand. The proposed solution was implemented on
Kubeless and was evaluated using a normalised real-world function demand trace
with matrix multiplication as the workload. The results demonstrate a
favourable performance of the RL-based agent when compared to Kubeless' default
policy and function keep-alive policy by improving throughput by up to 8.81%
and reducing computation load and resource wastage by up to 55% and 37%,
respectively, which is a direct outcome of reduced cold starts.

通过使用强化学习减少冷启动频率，以提高函数即时初始化效果的解决方案的实现与评估。