However, there is no consensus in regard to a plausible and inclusive definition of a domain-specific KG. Therefore, KGs continue to be used as the main means of tackling a plethora of real-life problems in various domains. This is leveraged by the underlying structure of the KG which underpins a better comprehension, reasoning and interpretation of knowledge for both human and machine. ![]() Knowledge Graphs (KGs) have made a qualitative leap and effected a real revolution in knowledge representation. The ARIMA model gives best results for fluctuations based long-term GPU spot price prediction. The ETS model provides better results for smoothness based long-term GPU spot price prediction. The AR model provides best results for variance based middle-term and long-term GPU instance price prediction. The results comparisons between AR, ETS, ARIMA, and GARCH models show clearly that the GARCH model provides better results for dynamic based short-term and middle-term GPU instance price prediction. This research provides a comparative exploration regarding the use of these prediction models across distinct regions in a cost-efficient way. This research used the linear autoregressive (AR) model, ARIMA model, exponential smoothing (ETS) model, and generalized autoregressive conditional heteroskedasticity (GARCH) for predicting upcoming GPU spot instance pricing. But choosing a time series prediction model with different environments including variance and volatility is a challenging task, and none of the extant literature provides such an exploratory analysis. As such, the use of time series prediction models is more suitable under these circumstances. However, deep-learning price-reduction methods are complex, require high computational power, and they cannot provide accurate price forecasts due to the varying and volatile environment. They also used classical univariate time series approaches including autoregressive integrated moving average (ARIMA) and autoregressive moving average (ARMA) models. In the existing literature, researchers used machine learning approaches including artificial neural networks (ANN), long short-term memory (LSTM) networks, and random forecasts. This research will help researchers to exploit GPU spot instances across distinct regions in a cost-efficient way. Our second aim is to study the unique characteristics of GPU spot instances. The main aim of this research is to predict upcoming GPU spot instance pricing using a time series prediction model. The pricing of the GPU spot instances dynamically changes over time based on the long-term demand and supply of cloud resources in the spot market. When there is less demand for cloud computing instances, the unused resources are provided at low-cost pricing as GPU spot instances. Amazon web services provide resources equipped with graphics processing unit (GPU) devices, which are commonly used for parallel processing applications.
0 Comments
Leave a Reply. |