Abstract
Serverless computing has evolved as a prominent paradigm within cloud computing, providing on-demand resource provisioning and capabilities crucial to Science and Technology for Energy Transition (STET) applications. Despite the efficiency of auto-scalable approaches in optimizing performance and cost in distributed systems, their potential remains underutilized in serverless computing due to the lack of comprehensive approaches. So an auto-scalable approach has been designed using Q-learning, which enables optimal resource scaling decisions. This approach proves useful for adjusting resources dynamically to maximize resource utilization by automatically scaling up or down resources as needed. Further, the proposed approach has been validated using AWS Lambda with key performance metrics such as probability of cold start, average response time, idle instance count, energy consumption etc. The experimental results demonstrate that the proposed approach performs better than the existing approach by considering the above parameters. Finally, the proposed approach has also been validated to optimize the energy consumption of smart meters data
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