Reducing cloud infrastructure costs through task management

The transition of more and more companies from their own computing infrastructure to the clouds is due to a decrease in the cost of maintaining it, the broadest scalability, and the presence of a large number of tools for automating activities. Accordingly, cloud providers provide an increasing number of different computing resources and tools for working in the clouds. In turn, this gives rise to the problem of the rational choice of the types of cloud services in accordance with the peculiarities of the tasks to be solved. One of the most popular areas of effort for cloud consumers is to reduce rental costs. The main base of this direction is the use of spot resources. The article proposes a method for reducing the cost of renting computing resources in the cloud by dynamically managing the placement of computational tasks, which takes into account the possible underutilization of planned resources, the forecast of the appearance of spot resources and their cost. For each task, a state vector is generated that takes into account the duration of the task and the required deadline. Accordingly, for a suitable set of computing resources, an availability forecast vectors are formed at a given time interval, counting from the current moment in time. The technique proposes to calculate at each discrete moment of time the most rational option for placing the task on one of the resources and the delay in starting the task on it. The placement option and launch delays are determined by minimizing the rental cost function over the time interval using a genetic algorithm. One of the features of using spot resources is the auction mechanism for their provision by a cloud provider. This means that if there are more preferable rental prices from any consumer, then the provider can warn you about the disconnection of the resource and make this disconnection after the announced time. To minimize the consequences of such a shutdown, the technique involves preliminary preparation of tasks by dividing them into substages with the ability to quickly save the current results in memory and then restart from the point of stop. In addition, to increase the likelihood that the task will not be interrupted, a price forecast for the types of resources used is used and a slightly higher price is offered for the auction of the cloud provider, compared to the forecast. Using the example of using the Elastic Cloud Computing (EC2) environment of the cloud provider AWS, the effectiveness of the proposed method is shown.

Crude Oil Price Forecast Based on Deep Transfer Learning: Shanghai Crude Oil as an Example

Crude oil is an important fuel resource for all countries. Accurate predictions of oil prices have important economic and social values. However, the price of crude oil is highly nonlinear under the influence of many factors, so it is very difficult to predict accurately. Shanghai crude oil futures were officially listed in March 2018. It is of great significance to accurately predict the price of Shanghai crude oil futures for guiding China’s domestic production practice. Forecasting the price of Shanghai crude oil futures is even more difficult because of the lack of price data due to the short listing time. In order to solve this problem, this paper proposes using Long Short-Term Memory Network (LSTM) based on transfer learning to predict the price of crude oil in Shanghai. The basic idea is to take advantage of the correlation between Brent crude oil and Shanghai crude oil, use Brent crude oil for training in the early stage, and then use Shanghai crude oil to fine-tune the network. The empirical results show that the LSTM model based on transfer learning has strong generalization ability and high prediction accuracy.

Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices

The literature argues that an accurate price prediction of agricultural goods is a quintessence to assure a good functioning of the economy all over the world. Research reveals that studies with application of deep learning in the tasks of agricultural price forecast on short historical agricultural prices data are very scarce and insist on the use of different methods of deep learning to predict and to this reaction of filling the gap, this study employs five versions of LSTM deep learning techniques for the task of five agricultural commodities prices prediction on univariate time series dataset of Rice, Wheat, Gram, Banana, and Groundnut spanning January 2000 to July 2020. The study obtained good forecasting results for all five commodities employing all the five LSTM models. The study validated the results with lower values of error metrics, MAE, MAPE, MSE, and RMSE and two paired t-test with hypothesis and confidence level of 95% as a measure of robustness. The study predicted the one month ahead future price for all the five commodities and compared it with actual prices using said LSTM models and obtained promising results.

TECHNOECONOMIC ANALYSIS OF REFINING NIGERIAN LIQUEFIED NATURAL GAS CONDENSATE

Condensate refining is among the strategies proposed to solve the light oil glut around the globe. The Nigerian Liquefied Natural Gas (NLNG), which is the Nigerian government’s best performing investment in the natural gas value chain, produces plant condensate as a by-product. In this paper, the economics of a refinery designed to use NLNG plant condensate is evaluated under an optimistic oil price forecast and a pessimistic oil price trend. A gasoline producing refinery configuration was chosen for this study, and it comprises of a naphtha splitter, a Penex isomerisation unit and a Continuous Catalytic Reforming (CCR) unit. The product yields and plant costs were determined by established correlations and industry estimates. The proposed refinery will convert 40,000 bpd plant condensate into 96% gasoline, 3% LPG and 1% hydrogen, and economic indicators such as Net Present Value (NPV), Internal Rate of Return (IRR) and Profitability Index (PI) were used to assess the economic viability of the refinery. The optimistic scenario of oil price forecast resulted in an NPV of $ 531.90 million, an IRR of 20.09% and a PI of 3.16, while the pessimistic scenario gave an NPV of $16.26 million, an IRR of 11.16% and a PI of 1.07. These results prove that a condensate refinery with the proposed configuration is economically feasible and interested investors in Nigeria’s refining space should explore this possibility.

Predicting price intervals under exogenously induced stress

We present an experimental protocol to examine the relationship between exogenously induced stress and confidence in a setting applicable to financial markets. Confidence will be measured by a prediction interval for a one period ahead price forecast, based on a series of 100 previous prices; narrower (wider) prediction intervals will be indicative of greater (lower) confidence. Stress will be induced using the Cold Pressor Arm Wrap, a variation of the Cold Pressor Test. Risk attitudes, and personality traits are also considered as mediating factors.

Forecasting Bitcoin price using time opinion mining and bi-directional GRU

Bitcoin is an innovative decentralized digital currency without intermediaries. Bitcoin price prediction is a demanding need in the present situation. This paper makes an investigation on the Bitcoin price forecast with a Bi-directional Gated Recurrent Unit (GRU) time series method, combined with opinion mining based on Twitter and Reddit feeds. An hourly basis sentimental analysis through the implementation of Natural Language Processing presents a positive impact of sentimental analysis on the Bitcoin price prediction. For prediction, RNN, long-short memory, GRU has been utilized. Unidirectional and Bi-directional versions of all three networks with and without sentimental analysis were implemented for comparison. Of all the techniques implemented Bi-directional GRU along with sentimental analysis gives a minimum RMSE and Minimum absolute percentage error of 1108.33 and 7.384%. Thus, the framework including Bi-Directional GRU along with Sentimental Analysis provides better results than the State-of-art methods.