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A Reinforcement Learning based Adaptive Container Scheduling Back-off Scheme for Reducing Cold Starts in FaaS Platforms
Sungho Kang, Junyeol Yu, Euiseong Seo
http://doi.org/10.5626/JOK.2024.51.3.191
Function as a Service(FaaS) is a cloud computing service model that virtualizes computing resources and provides them in units of functions. As it enables flexible and easy service deployment, its use is rapidly growing in a cloudnative architecture. However, the initial execution of a function requested by a user in a FaaS platform involves several initialization steps, and this initialization overhead, that is, cold start, delays function execution. Our proposal is that when there is a request to execute the same function as the running function, waiting rather than immediately processing the request can reduce the occurrence of a cold start. In this paper, we propose a FaaS request waiting policy model based on reinforcement learning that pursues the best choice between sending and waiting for a function execution request. As a result of the comparison experiment with Openwhisk, the frequency of cold start reduced by up to 57% and the average execution time of the function reduced by up to 81%.
Zero-Shot Solar Power Efficiency Prediction Method Considering PCC-Based Climate Similarity
Dongjun Kim, Sungwoo Park, Jaeuk Moon, Eenjun Hwang
http://doi.org/10.5626/JOK.2023.50.7.581
Thermal power generation is a power generation method that occupies a large proportion in Korea and abroad due to its low unit price. However, due to its disadvantage of emitting large amounts of harmful substances that can cause health and environmental problems, renewable energy is in the spotlight as an alternative power source. Among various renewable energy generation methods, solar power generation is receiving the most attention because of its advantages such as ease in maintenance. Various solar power generation forecasting studies are being conducted to improve the uncertainty of volatile solar power generation and ensure stability in power supply. However, existing studies have limitations in that they are only applicable when there is a sufficient amount of historical power generation data. Therefore, this paper proposes a solar power generation efficiency prediction method based on zero-shot learning that utilizes historical data of similar regions by concerning weather similarity to solve the cold-start problem, a problem that occurs in prediction when historical data in the target region are lacking. Comparison results revealed that the proposed method had better performance overall in the target area, with a one-hour-based method showing the best prediction performance among other criteria.
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