Amazon Spot Instance Price Prediction with GRU Network
| Autor: | Shijun Liu, Dawei Kong, Li Pan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
050101 languages & linguistics
Price fluctuation Spot contract Mean squared error Computer science Amazon rainforest business.industry 05 social sciences Cloud computing 02 engineering and technology Price prediction Fixed price Statistics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences business Network approach |
| Zdroj: | CSCWD |
| DOI: | 10.1109/cscwd49262.2021.9437881 |
| Popis: | The Amazon cloud platform sells its idle resources to cloud users as Spot Instances, which provide an ultra-low discount compared to the price of on-demand instances. Unlike the pricing strategy of the on-demand and reserved instances that use fixed price, the price of Spot Instances is dynamically changed, which introduce an interesting research topic of price prediction. In this paper, we firstly analyze the actual price distribution on a 90 days Amazon spot price history data downloaded from the Amazon Cloud platform, by using the parameter k-AMSE to represent the price fluctuation of a spot instance which can reflect recent data fluctuations better than MSE(mean square error). Then, We analyzed the factors that affect the price fluctuation and presented a prediction model based on the GRU(Gated Recurrent Unit) network. We compare the proposed algorithm with others and evaluate it with RMSE (root mean square error) measurement. The experiment results show that the GRU network approach can perform over others with an accuracy rate of 1.58e-3. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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