How does double exponential smoothing affect the performance of Knative autoscaler?Â
Best insight from top research papers
Double exponential smoothing has been shown to improve the performance of the Knative autoscaler . The serving module of Knative uses a moving average method to calculate the number of pods, which is based on past data and may not accurately account for future changes. By using double exponential smoothing, the calculation of the number of pods can be optimized. Preliminary experiments have demonstrated that the results of double exponential smoothing are better than those of the moving average method . This suggests that incorporating double exponential smoothing into the Knative autoscaler can enhance its ability to scale serverless apps effectively and accurately.
Answers from top 5 papers
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| Papers (5) | Insight |
|---|---|
8Â Citations | The provided paper does not mention anything about Knative autoscaler or double exponential smoothing. |
22 May 2003 | The provided paper does not mention anything about the performance of Knative autoscaler. |
14 Aug 2022 | The provided paper is about forecasting the production of Toyota Avanza using the double exponential smoothing method with the golden section algorithm. It does not discuss the performance of Knative autoscaler. |
01 Jun 2020 5Â Citations | Double exponential smoothing improves the performance of the Knative autoscaler by optimizing the calculation of the number of pods, resulting in better results compared to the moving average method. |
22 Mar 2003 | The provided paper does not mention anything about the performance of Knative autoscaler. |
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