Modern applications, such as smart cities, home automation, and eHealth, demand a new approach to improve cloud application dependability and availability. Due to the enormous scope and diversity of the cloud environment, most cloud services, including hardware and software, have encountered failures. In this study, we first analyze and characterize the behaviour of failed and completed jobs using publicly accessible traces. We have designed and developed a failure prediction model to determine failed jobs before they occur. The proposed model aims to enhance resource consumption and cloud application efficiency. Based on three publicly available traces: the Google cluster, Mustang, and Trinity, we evaluate the proposed model. In addition, the traces were also subjected to various machine learning models to find the most accurate one. Our results indicate a significant correlation between unsuccessful tasks and requested resources. The evaluation results also revealed that our model has high precision, recall, and F1-score. Several solutions, such as predicting job failure, developing scheduling algorithms, changing priority policies, or limiting re-submission of tasks, can improve the reliability and availability of cloud services.

This paper presents the design and implementation of an e-health smart networked system. The system is aimed to prevent delays in the arrival of patients' medical information to the healthcare providers, particularly in accident and emergency situations, to stop manual data entering, and to increase beds capacity in hospitals, especially during public events where a large number of people are meeting in one place. The architecture for this system is based on medical sensors which measure patients physical parameters by using wireless sensor networks (WSNs). These sensors transfer data from patients' bodies over the wireless network to the cloud environment. Therefore, patients will have a high quality services because the e-heath smart system supports medical staff by providing real-time data gathering, eliminating manual data collection, enabling the monitoring of huge numbers of patients.

Most public and private cloud providers have experienced failure in one of their services that may affect numerous applications and websites. Thus, in order to understand the causes of different types of failures and remediate the issue, failure analysis is one of the most critical steps. Failure analysis has been developed based on monitoring the most significant metrics of the system in order to study the behavior and frequency changes in the systems. Then, the monitored data will be stored in log files to be utilized for analysis and prediction tasks. In this paper, we primarily focus on analyzing and interpreting the characteristic behavior of finished/failed jobs in association with physically available resources using a publicly available dataset, Google cluster trace. The primary objective of our work is to enhance the understanding of job failure in cloud computing environments. Our results show a clear correlation between failed jobs and requested resources including memory, CPU, and disk space. Based on our results, we find that many techniques can be applied to increase the reliability and availability of cloud applications, such as developing scheduling algorithms, predicting job failure, limiting task resubmission or changing the priority policies.

Managing large-scale gatherings, such as global festivals, sporting events, and religious congregations, presents substantial challenges in ensuring crowd safety and control. Innovative frameworks are essential to address these complexities effectively. The Integrated Intelligent Crowd Control and Management (IICCM) framework combines cutting-edge technologies, including Computer Vision (CV), Artificial Intelligence (AI), and the Internet of Things (IoT), to enhance participant safety and optimize crowd management. CV enables precise real time identification and tracking, AI analyzes crowd behavior to anticipate risks, and IoT gathers environmental data to improve crowd flow, alleviate congestion, and provide timely assistance. Additionally, the framework facilitates emergency evacuation planning by modeling crowd dynamics and identifying safe, efficient escape routes. Although suitable for diverse events, the Hajj pilgrimage—a uniquely large and dynamic annual gathering—provides a rigorous test case for the IICCM framework. Managing millions of participants from varied cultural and linguistic backgrounds highlights the system’s adaptability and robustness. By effectively addressing Hajj specific challenges, the IICCM framework demonstrates its scalability and applicability to other large-scale events. This research offers valuable insights for decision-makers seeking to implement advanced crowd management technologies.

Modern embedded systems generate a large volume of data that require a highly scalable and available framework that enables computing, storage, and data analysis. Many cloud providers offer unlimited storage as well as a flexible processing infrastructure, allowing for executing a large number of tasks with high performance. This paper presents a framework for extending the local resources of embedded systems, which are very limited in terms of computing, memory, and storage, using cloud computing. This framework provides scalability and high availability for embedded systems. Moreover, a scheduling algorithm is implemented to minimize the execution time and computation cost of using the cloud, and it is according to a logic which directly depends on the criticality and computation requirements of the tasks. The framework has been implemented using Windows Azure services. We evaluated the framework in term of performance. The results indicate that the framework performance has improved as well as the throughput efficiency increases by 55% after integrating the local resources, which include the embedded system, to the cloud.