Talal H. Noor^*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1273-1299, 2024, DOI:10.32604/csse.2024.052510 - 13 September 2024

Abstract In recent years, container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical resources. However, there is a noticeable absence of techniques for predicting microservice performance in current research, which impacts cloud service users’ ability to determine when to provision or de-provision microservices. Predicting microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention, which potentially leads to user confusion. In this paper, we propose, develop, and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction (MPDP). MPDP… More >

Bin Li^*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2729-2748, 2023, DOI:10.32604/fdmp.2023.029649 - 25 June 2023

Abstract In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas horizontal wells after fracturing, a new sophisticated approach is proposed in this study. This new model stems from the combination several techniques, namely, artificial neural network (ANN), particle swarm optimization (PSO), Imperialist Competitive Algorithms (ICA), and Ant Clony Optimization (ACO). These are properly implemented by using the geological and engineering parameters collected from 317 wells. The results show that the optimum PSO-ANN model has a high accuracy, obtaining a R² of 0.847 on the testing. The partial dependence More >

Abeer I. Alhujaylan^{1, 2, *}, Manar I. Hosny¹

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1025-1051, 2020, DOI:10.32604/cmc.2020.08742

Abstract Mobile commerce (m-commerce) contributes to increasing the popularity of electronic commerce (e-commerce), allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any time. As demand for e-commerce increases tremendously, the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer satisfaction. One important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem (MVPPDP), where a selected set of pickup and delivery customers need to be served within certain allowed trip time. In this paper, we proposed hybrid clustering algorithms More >