Location and Time Aware Real Time Cloud Service Recommendation System Based on Multilayer Perceptron

Abstract

Cloud computing is the on-demand availability of internet-based computing services, especially software, large amounts of data storage, operating systems, and other computing resources. Service Level Agreement (SLA) violation is the most critical problem in cloud computing. SLA violation creates many problems for cloud service providers and cloud customers. Due to this, cloud customer gets low-quality cloud service. Thus, designing an effective cloud service recommendation algorithm is a critical research problem in cloud computing. The primary objective of this research is to determine the optimal cloud service from functionally equivalent cloud services that better fit the user's requirements (latency, throughput, response time, and cost). The efficiency of cloud services varies according to the time and location of the virtual machine. First, this proposed method determines the correlation between active user requirements and cloud services. Second, strongly correlated services are separated into two clusters based on the virtual machine's location and the cloud service's data transmission rate. For this purpose, two lightweight clustering algorithms have been proposed. A modified multilayer perceptron algorithm has been developed to recommend the optimal cloud service to the active user from the two clusters. The open-source WS-Dream dataset is used to train and validate the proposed MLP. The training efficiency, prediction accuracy, and performance of the proposed MLP-based cloud service recommendation system are experimentally compared to the existing cloud service recommendation systems analysed in the literature study [20,2,22,23]. Compared to existing cloud service recommendation approaches, the MAE and RMSE values of the proposed cloud service recommendation system are less than one. In terms of accuracy, the suggested method obtains a precession of 94 %, a recall of 97 %, and an F1-Measure of 96 %, all of these are significantly better than the existing cloud service recommendation methods. Finally, experimental results prove that the overall performance of the proposed method's throughput (increase 10 MBPS), latency (reduce 10 ms), the response time (reduce 17 ms) and service recommendation time (reduce 5ms) is more robust than existing methods.

Handling Sparse Rating Matrix for E-commerce Recommender System Using Hybrid Deep Learning Based on LSTM, SDAE and Latent Factor

“…In the last decade, deep learning has become the famous method that successfully implemented and achieved tremendous result in the realm of computer science research, such as image processing, sentence classification, and voice recognition [19] [20]. Several researcher specifically in the recommender system field are trying to adopt the deep learning approach to their model, such as collaborative deep learning (CDL) [21] and multi layer perceptron (MLP) [22], CDL adopted stack denoising auto encoder (SDAE) aims to extract item document representation. As we know, SDAE is a feed forward neural network that is very useful in handling missing values.…”