Reliability Of Cloud Computing Research Articles

Efficient Container Scheduling With Hybrid Deep Learning Model for Improved Service Reliability in Cloud Computing

Efficient Container Scheduling With Hybrid Deep Learning Model for Improved Service Reliability in Cloud Computing

Achieving Reliability in Cloud Computing by a Novel Hybrid Approach.

Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing's challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system's ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.

АНАЛИЗ ПОДХОДОВ К ОБЕСПЕЧЕНИЮ БЕЗОПАСНОСТИ ОБЛАЧНЫХ СЕРВИСОВ

The article is devoted to the analysis of modern approaches that ensure the security of cloud services. Since cloud computing is one of the fastest growing areas among information technology, it is extremely important to ensure the safety and reliability of processes occurring in the clouds and to secure the interaction between the client and the provider of cloud services. Given that fears about data loss and their compromise are one of the main reasons that some companies do not transfer their calculations to the clouds. The object of research and analysis of this work are cloud services, which are provided by various cloud service providers. The aim of the study of this work is to compare existing approaches that provide information security for cloud services, as well as offer a new approach based on the principle of diversity. There are many approaches that ensure their safety, using both traditional and cloud-specific. The multi-cloud approach is one of the most promising strategies for improving reliability by reserving cloud resources on the servers of various cloud service providers. It is shown that it is necessary to use diversity to ensure the reliability and safety of critical system components. The principle of diversity is to use a unique version of each resource thanks to a special combination of a cloud computing provider, the geographical location of data centers, cloud service presentation models, and cloud infrastructure deployment models. The differences between cloud providers and which combination of services are preferable to others in terms of productivity are discussed in detail. In addition, best practices for securing cloud resources are reviewed. As a result, this paper concludes that there is a problem of insufficient security and reliability of cloud computing and how to reduce threats in order to avoid a common cause failure and, as a result, loss of confidential data or system downtime using diversity of cloud services.

Reliability in cloud computing system: a review

As a new computing paradigm, cloud computing has attracts extensive concerns from both academic and industrial fields. Based on resource virtualization technology, cloud computing provides users with services in the forms of infrastructure, platform and software in a pay-as-you-go manner. In the meanwhile, since cloud computing provides highly scalable computing resources, more and more enterprises and organizations choose cloud computing platforms to deploy their scientific or commercial applications. However, with the increasing number of cloud users, cloud data centers continuously expand and the architecture becomes increasingly complex, leading to growing runtime failures in cloud computing systems. Therefore, how to ensure the system reliability in cloud computing systems with large scale and complex architecture has become a huge challenge. This paper first summarizes various failures in cloud systems, introduces several methods to evaluate the reliability of cloud computing, and describes some key fault management mechanisms. Since fault management techniques inevitably increase energy consumption of cloud systems, this paper reviews current researches on the trade-off between reliability and energy efficiency in cloud computing. In the end, we propose some major challenges in current research of cloud computing reliability and concludes our paper.

An Associative Optimizing Method on Reliability and Cost in Clouds

Cloud computing enables users to use shared system resources in an efficient pay-as-you-go manner. However, the reliability of cloud computing is a challenging proposition. Cloud users cannot improve the reliability of their cloud environment from the hardware and system level. On the other hand, due to the cost and complexity of the system, improving the reliability of specific users using practical methods is not straightforward for the operators of cloud systems. In this paper, we attempt to optimize the reliability of the cloud system by using the correlation between cost and reliability. We conduct a comprehensive and detailed analysis of the reliability of clouds, extract vital features that can be used to improve system reliability and consider cost constraints. We propose the reliability model and virtual machine (VM) provisioning/request model; based on this model, we propose an SLA-oriented reliability assurance scheme to continuously optimize the reliability of clouds. The experimental results not only indicate the validity of the model and schemes proposed in this paper but also provide a new perspective for the continuous optimization of cloud system reliability.

FUZZY LOGIC-BASED ALGORITHM RESOURCE SCHEDULING FOR IMPROVING THE RELIABILITY OF CLOUD COMPUTING

Cloud computing is an important infrastructure for distributed systems with the main objective of reducing the use of resources. In a cloud environment, users may face thousands of resources to run each task. However, allocation of resources to tasks by the user is an impossible endeavor. Accurate scheduling of system resources results in their optimal use as well as an increase in the reliability of cloud computing. This study designed a system based on fuzzy logic and followed by an introduction of an efficient and precise algorithm for scheduling resources for improving the reliability of cloud computing. Waiting and turnaround times of the proposed method were compared to those of previous works. In the proposed method, the waiting time is equal to 26.99 and the turnaround time is equal to 82.99. According to the results, the proposed method outperforms other methods in terms of waiting time and turnaround time as well as accuracy.

Factors that Determine the Adoption of Cloud Computing

Cloud computing has spread within enterprise faster than many other IT innovations. In cloud computing, computer services are accessed over the Internet in a scalable fashion, where the user is abstracted in varying degrees from the actual hardware and software and pays only for resources used. This paper examines the adoption of cloud computing in various regions of the world, as well as the potential of cloud computing to impact computing in developing countries. The authors propose that cloud computing offers varying benefits and appears differently in regions across the world, enabling many users to obtain sophisticated computing architectures and applications that are cost-prohibitive to acquire locally. The authors examine issues of privacy, security, and reliability of cloud computing and discuss the outlook for firms and individuals in both developing and developed countries seeking to utilize cloud computing for their computing needs.