Cloud Service Providers In Order Research Articles

E2LG: a multiscale ensemble of LSTM/GAN deep learning architecture for multistep-ahead cloud workload prediction

Efficient resource demand prediction and management are two main challenges for cloud service providers in order to control dynamic autoscaling and power consumption in recent years. The behavior of cloud workload time-series at subminute scale is highly chaotic and volatile; therefore, traditional machine learning-based time-series analysis approaches fail to obtain accurate predictions. In recent years, deep learning-based schemes are suggested to predict highly nonlinear cloud workloads, but sometimes they fail to obtain excellent prediction results. Hence, demands for more accurate prediction algorithm exist. In this paper, we address this issue by proposing a hybrid E2LG algorithm, which decomposes the cloud workload time-series into its constituent components in different frequency bands using empirical mode decomposition method which reduces the complexity and nonlinearity of prediction model in each frequency band. Also, a new state-of-the-art ensemble GAN/LSTM deep learning architecture is proposed to predict each sub band workload time-series individually, based on its degree of complexity and volatility. Our novel ensemble GAN/LSTM architecture, which employs stacked LSTM blocks as its generator and 1D ConvNets as discriminator, can exploit the long-term nonlinear dependencies of cloud workload time-series effectively specially in high-frequency, noise-like components. By validating our approach using extensive set of experiments with standard real cloud workload traces, we confirm that E2LG provides significant improvements in cloud workload prediction accuracy with respect to the mean absolute and standard deviation of the prediction error and outperforming traditional and state-of-the-art deep learning approaches. It improves the prediction accuracy at least 5% and 12% in average compared to the main contemporary approaches in recent papers such as hybrid methods which employs CNN, LSTM or SVR.

Secure data outsourcing in presence of the inference problem: issues and directions

ABSTRACT With the emergence of cloud computing paradigms, secure data outsourcing has become one of the crucial challenges which strongly imposes itself. Data owners place their data among cloud service providers in order to increase flexibility, optimize storage, enhance data manipulation and decrease processing time. Nevertheless, from a security point of view, access control is a major challenge in this situation seeing that the security policy of the data owner must be preserved when data is moved to the cloud. Nonetheless, the lack of a comprehensive and systematic review motivated us to construct this reviewing paper on this research problem. Here, we discuss current and emerging research on privacy and confidentiality concerns in data outsourcing and pinpoint potential issues that are still unresolved.

A mixed ideal and anti-ideal DEA model: an application to evaluate cloud service providers

Abstract The rapid development of cloud computing and the sharp increase in the number of cloud service providers (CSPs) have resulted in many challenges in the suitability and selection of the best CSPs according to quality of service requirements. The main objective of this study is to propose three novel models based on the enhanced Russell model to increase the discrimination power in the evaluation and selection of CSPs. The proposed models are designed based on the distances to two special decision-making units (DMUs), namely the ideal DMU and the anti-ideal DMU. There are two advantages to the proposed ranking methods. First, they consider both pessimistic and optimistic scenarios of data envelopment analysis, so they are more equitable than methods that are based on only one of these scenarios. The second strength of this approach is its discrimination power, enabling it to provide a complete ranking for all CSPs. The proposed method can help customers to choose the most appropriate CSP while at the same time, it helps software developers to identify inefficient CSPs in order to improve their performance in the marketplace.

Data security and storage in cloud using hybrid algorithms

Cloud Computing has already grabbed its roots in many industries. It has become a fascinating choice for small budget organizations, as On-demand resources are available on pay as you use basis. However, security of data being stored at cloud servers is still a big question for organizations in today’s digital era where information is money. Large organizations are reluctant to switch to cloud services since they have threat of their data being manipulated. Cloud service provider’s claim of providing robust security mechanism being maintained by third party, but still there are many reported incidents of security breach in cloud environment in past few years. Thus, there is need for ro-bust security mechanism to be adopted by cloud service providers in order for excelling cloud computing. Since there are n number of data’s in cloud, Storage of those data are to be placed with high rank of Significance. In Existing system, no efficient hybrid algorithms are used there by security and storage is compromised to significant ratio. We propose AES and Fully Homomorphic algorithm to encrypt the data, thereby file size get is compressed thereby increasing Data security and stack pile.

Secure Sharing of Personal Health Records in Cloud Computing using Attribute-based Broadcast Encryption

Personal Health Record (PHR) service is an emerging model for health information exchange. It allows patients to create, update and manage personal and medical information. Also they can control and share their medical information with other users as well as health care providers. PHR data is hosted to the third party cloud service providers in order to enhance its interoperability. However, there have been serious security and privacy issues in outsourcing these data to cloud server. For security, encrypt the PHRs before outsourcing. So many issues such as risks of privacy exposure, scalability in key management, flexible access and efficient user revocation, have remained the most important challenges toward achieving fine-grained, cryptographically enforced data access control. To achieve fine-grained and scalable data access control for client’s data, a novel patient-centric framework is used. This frame work is mainly focus on the multiple data owner scenario. A high degree of patient privacy is guaranteed simultaneously by exploiting multi authority ABE. This scheme also enables dynamic modification of access policies or file attributes, support efficient on demand user/attribute revocation. However some practical limitations are in building PHR system. If consider the workflow based access control scenarios, the data access right could be given based on users identities rather than their attributes, while ABE does not handle that efficiently. For solving these problem in this thesis proposed PHR system, based on Attribute Based Broadcast Encryption (ABBE).

SLA (Service Level Agreement) Driven Orchestration Based New Methodology for Cloud Computing Services

Cloud Computing is not a revolution; it’s an evolution of computer science and technology emerging by leaps and bounds, in order to merge all computer science tools and technologies. Cloud Computing technology is hottest to do research and explore new horizons of next generations of Computer Science. There are number of cloud services providers (Amazon EC2), Rackspace Cloud, Terremark and Google Compute Engine) but still enterprises and common users have a number of concerns over cloud service providers. Still there is lot of weakness, challenges and issues are barrier for cloud service providers in order to provide cloud services according to SLA (Service Level agreement). Especially, service provisioning according to SLAs is core objective of each cloud service provider with maximum performance as per SLA. We have identified those challenges issues, as well as proposed new methodology as “SLA (Service Level Agreement) Driven Orchestration Based New Methodology for Cloud Computing Services”. Currently, cloud service providers are using “orchestrations” fully or partially to automate service provisioning but we are trying to integrate and drive orchestration flows from SLAs. It would be new approach to provision cloud service and deliver cloud service as per SLA, satisfying QoS standards.