With the rapid growth of cloud services, it is crucial to have strong assessment methods in place to rate these services according to their performance, dependability, and security. This study introduces a holistic methodology that utilizes advanced deep learning (DL) algorithms to prioritize and evaluate cloud services. Our model incorporates many assessment criteria, including latency, throughput, availability, and security measures. These criteria are trained using a varied collection of performance measurements from cloud services. We validate the effectiveness of our methodology by comprehensive experiments, attaining greater precision and significance in ranking compared to conventional approaches. The DL model underwent evaluation using a testing set, resulting in a mean absolute error (MAE) of 0.15 in ranking scores. The algorithm regularly achieved superior results compared to conventional ranking approaches, particularly in situations where performance measures varied. Through the incorporation of security metrics, the model successfully assessed and ranked cloud service providers (CSPs) based not only on their performance, but also on their ability to withstand security threats. The DL technique exhibited more flexibility and contextual awareness in its rankings, hence showcasing its superiority in adjusting to real-time data. The research conducted a comparison between DL-based rankings and conventional methodologies and industry standards, demonstrating its superiority in effectively adjusting to real-time data. The study technique entails gathering data from many CSPs to construct a resilient framework for evaluating cloud services using DL models. The data is obtained from publicly available performance statistics, cloud monitoring tools, user evaluations, and problem reports. The collection comprises both structured and unstructured data, including essential performance and accuracy indicators.

This article asserts that use of IoT (Internet of Things) devices have significantly increased over the last decade, specifically smartphones as compared to desktops, and laptops have become an integral part of our everyday lives. Smartphone applications operate in dynamic environments and generate huge and vast amount of context events such as screen orientation, location, battery life, and network connectivity throughout the day. This work proposes a modified method of Compact Prediction Tree (CPT) called Context Data Compact Prediction Tree (CD-CPT) to predict real-world context data for multiple users. The experiments conducted used Transition Directed Acyclic Graph (TDAG) and All-k Order Markov (AKOM) algorithms to generate short-term predictions based on current context events and compare with baseline models such as Prediction by Pattern Mining (PPM), Dependency Graph (DG), CPT, and CPT+.

Service selection has emerged as a prominent challenge due to the flourishing demand for computing services and the dynamic nature of its resources. The increasing demand for cloud services makes it challenging to choose a provider offering equal services and facilities at costs that match those of competing providers. Apart from educating customers in the process of choosing cloud services, trust mechanisms include user reviews, reputation systems, and certifications assist to boost consumers’ confidence in cloud services. The service measurement index (SMI) offers a disciplined framework combining both functional and non-functional quality of service indicators concurrently, therefore easing decision-making. The main emphasis of the research is on the fundamental elements influencing the choice of cloud services in the present environment, the identification of extra characteristics of cloud services transcending SMI, and the identification of the most suitable approach for some services. By means of the measurement of customer enjoyment and experience, QoS traits provide some insight on the impact of trust mechanisms on service acceptance. Comparisons of SMI and QoS measurements before and after trust mechanism deployment provide insightful analysis. Empirical research guides these comparisons. This study aims to clarify the interactions among QoS, trust mechanisms, and cloud service adoption as well as highlight the implications these elements have for customers and service providers. Furthermore, presented in this paper is an algorithm using a comprehensive method to trust estimation in order to ascertain the degree of confidence worthiness of certain people.