Memory Degradation Analysis in Private and Public Cloud Environments

As businesses navigate the complexities of modern IT infrastructure, hybrid cloud solutions have emerged as a compelling choice, blending the strengths of both public and private cloud environments. The increased adoption of hybrid cloud is driven by the need for flexibility, scalability, and enhanced security, allowing organizations to optimize their operations and adapt to changing demands. By leveraging the best of both worlds, companies can maintain sensitive data on private clouds while taking advantage of the expansive resources and cost efficiencies offered by public clouds. This balanced approach not only ensures business continuity and disaster recovery but also supports the seamless integration of legacy systems with cutting-edge cloud technologies. Moreover, the hybrid cloud model offers organizations the agility to innovate rapidly, respond to market changes, and meet regulatory requirements without compromising performance or security. As more businesses recognize the strategic benefits of hybrid cloud, its adoption is expected to accelerate, making it a cornerstone of digital transformation initiatives. This article explores the rising trend of hybrid cloud adoption, highlighting its key advantages, challenges, and the role it plays in shaping the future of enterprise IT.

Cloud computing is one of the emerging technologies in last decade; this technology provides a service model to organizations and public users. Organization users and developers, they start and maintain their organization without any hardware and software infrastructure they can develop their company with the help of cloud technology. Cloud deployment is categorized into three types, private, public and hybrid cloud environment and here public and private cloud more secure comparatively hybrid cloud because when data is moving from private to public cloud security problem is occur. Data communication is most important task in a network environment so the proposed model is focus on data security in hybrid cloud environment. In a hybrid cloud the participation of private cloud and its nodes very important after that the data transmission is depends on active node, In this node selection is based on genetic algorithm. The result of proposed model is going to compare with private and public cloud security parameters.

Cloud Colonography is proposed in this paper, using different types of cloud computing environments. Databases from the Computed Tomographic Colonography (CTC) screening tests among several hospitals are explored. These networked databases are going to be available in the near future via cloud computing technologies. Associated Multiple Databases (AMD) was developed in this study to handle multiple CTC databases. When AMD is used for assembling databases, it can achieve very high classification accuracy. The proposed AMD has the potential to play a role of the core classifier in the cloud computing framework. AMD for multiple institutions databases yields high detection performance of polyps using Kernel principal component analysis (KPCA). Two cases in the proposed cloud platform are private and public. We adapted a university cluster as a private platform, and Amazon Elastic Compute Cloud (EC2) as a public. The computation time, memory usage, and running costs were compared using three representative databases between private and public cloud environments. The proposed parallel processing modules improved the computation time, especially in the public cloud environments. The successful development of a cloud computing environment that handles large amounts of data will make Cloud Colonography feasible for a new health care service.

With the popularity of video streaming, a new type of media player has been created called the adaptive video player that adjusts video quality based on available network bandwidth. Merging this technology with cloud computing will change the online video landscape by allowing providers to dynamically create media servers that take advantage of all the benefits of cloud computing. This however is not a straightforward endeavor as unlike a traditional data center; a cloud-based infrastructure is subject to a greater amount of performance variability. While the adaptive video player is designed to cope with variability in general, a video server in the cloud will be less optimal compared to one running on dedicated hardware. In this paper, we research maximizing the video streaming experience in the cloud from the adaptive video server perspective through TCP congestion control algorithms. Five major TCP congestion control variants are evaluated: Cubic, Bic, Vegas, H-TCP, and HighSpeed TCP. Additionally both private and public cloud environments are tested with the final evaluation based on video streaming performance as well as TCP friendliness.

It is a real challenge for cloud providers to offer cloud resources that are 'virtually unlimited and can be appropriated in any quantity at any time' in a cost-effective way. In this paper, we propose quantitative models that enable cloud providers to make an informed trade-off between cost and quality. Distinguishing between public and private cloud environments we consider infinite and finite source models respectively. In both cases either homogeneous or heterogeneous cloud resource requests are considered. These models can be applied to cloud dimensioning based on request blocking probability, an important SLA parameter. We derive a novel, insightful method that makes it possible to compute resource requirements in private clouds with heterogeneous resource requests. We show the importance of applying finite source models in the context of private clouds. We also use the proposed models to quantify the benefits of cloud federations.

The rapid adoption of cloud computing has significantly transformed the way organizations manage and store data, but it has also introduced a complex array of security challenges. As businesses increasingly migrate to public, private, and hybrid cloud environments, ensuring robust protection of sensitive data and compliance with regulatory frameworks has become paramount. This chapter delves into advanced cloud security and access control models, with a particular focus on the integration of cutting-edge encryption technologies, identity management solutions, and continuous compliance monitoring. Key aspects such as multi-cloud security, data sovereignty, and the role of emerging technologies like artificial intelligence (AI) in threat detection are explored to provide a comprehensive view of modern cloud security strategies. The chapter emphasizes the importance of dynamic, real-time security measures that can adapt to the fluid nature of cloud environments, ensuring organizations maintain data integrity, privacy, and compliance in an increasingly interconnected digital ecosystem. By examining these critical components of cloud security, this work aims to provide actionable insights into safeguarding digital infrastructures in a rapidly evolving technological landscape.

Cloud Computing heads towards first among the top most developed technologies and with a better panorama in successive years by companies and organizations. This paper deeply analyses the Cloud Computing security hazards in layer wise such as Service layer (IaaS, SaaS, PaaS) network layer, storage layer and deployment models such as Public, Private and Hybrid cloud environments. In this analysis the traditional and approved security solutions were discussed and its procedure evaluating parameters are denoted here. In this paper new proposal for all the cloud layers issues are going to sort out in single package of solution is discussed.

Cloud computing is an enchanting technology for the allocation of resources on-demand. The cloud Systems mostly hinges upon Virtualization techniques for the resource allocation purposes. All the Cloud Services that service providers put forward are chargeable and are charging the users depending on demand. So its important for them to improve the scalability factor to provide a superlative service to the users. As a solution, recently the providers began to use an auto scaling mechanism which can scale the resources according to the need of them. Auto Scaling mechanism on Virtual machines will impart a streamlined use of cloud resources. It is considered as an effective Resource management technique that utilize data center in structured manner although, the already existing scaling methods just take into account CPU utilization only. Hence it is needed to consider an Auto Scaling method that guarantees the execution of applications within the deadline. So here we concentrate on an auto scaling method that handles Bag-of-Tasks jobs in Private and Public cloud environment. The results of Simulation shows how effectively the resources are allocated considering the deadline as parameter.

Automation of cybersecurity processes has become crucial with large scale deployment of sensitive workloads in regulated on-prem, private, and public cloud environments. Regulatory and standards bodies such as Payment Card Industry (PCI), Federal Financial Institutions Examination Council (FFIEC), International Organization for Standardization (ISO), and others govern the minimal set of cybersecurity controls that an organization must implement. To meet such requirements while maintaining business agility, organizations need to modernize from manual document based compliance management to automated processes for continuous compliance. This modernized process is called compliance-as-code. In this paper, we present an architecture for compliance-as-code based on a standardized framework. We identify several design choices and our rationale behind those. Specifically, we introduce a system for manipulating compliance information in a standardized manner and a data interchange protocol for inter-operable communication of compliance information. We demonstrate the scalability of our approach and briefly describe deployment and experimental results in real world settings.

A multi-cloud world requires a multi-cloud security approach

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

Bioinformatics is a developing interdisciplinary science which combines information technologies into biological researches. The techniques from this emerging field have shown great potential in many business areas including drug design, agriculture, and so on. Meanwhile, this new computational field has also been one of the largest consumers of computational power, as the analyses in bioinformatics are often extremely computationally or data intensive. Although there are already several projects which have done tentative exploration on deploying bioinformatics applications to cloud environments, the deployment is ad-hoc and restricted to a single private cloud environment. Moreover, the complexity of various demands of bench biologists and bioinformaticians also brings new challenges to bioinformatics cloud development. In this paper, we first identify the key participants and their interactions in a public bioinformatics cloud environment, where bioinformatic analyses are consumed as services on top of a cloud infrastructure. After that, we propose a research framework to discuss the domain-specific technical challenges in delivering such a solution. Finally, we summarize the existing related research efforts based on our framework and introduce our ongoing Web Lab project.

In cloud computing, private cloud tends to exhibit high controllability but lack scalability, whereas public cloud is just the opposite. The hybrid cloud formed by combining them can effectively balance controllability and scalability, and has been widely adopted in industry for executing workflows. The network connecting public and private clouds goes through Internet and its bandwidth is rather limited in comparison with that inside clouds. Hence, cross-cloud data transmission can become a bottleneck of executing workflows in such environment. Moreover, multiple data communications may contend for bandwidth resources, thereby incurring delay. To address these concerns, this work establishes a scheduling model for workflow execution across public and private clouds, where a queueing mode is innovatively employed to address potential network communication contention. A contention-cognizant list scheduling (CCLS) heuristic equipped with task duplication is devised to minimize workflow makespan. It adopts a novel task sorting attribute to schedule tasks and cross-cloud data communications by using available computation and communication resources, and employs task duplication to obviate the needs for certain data communications. Experiments are conducted with realistic workflows and diverse settings, and the results verify the superiority of CCLS over the existing ones as it can always achieve the best makespan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A crucial challenge for workflow scheduling across public and private clouds is that the cross-cloud bandwidth resource is relatively limited and multiple data communications may contend for it in practice. However, this communication contention issue has largely been neglected in existing investigations. To advance the state of the art, this paper proposes a communication contention-cognizant scheduling approach based on a queueing mode to minimize the makespan for workflow execution across public and private clouds. The proposed approach can be readily put into use and experimental results show that it performs better than traditional scheduling approaches that fail to consider communication contention.

Recent years have witnessed the trend of leveraging cloud-based services for large scale content storage, processing, and distribution. Security and privacy are among top concerns for the public cloud environments. Towards end-to-end content security, we propose and implement CloudSeal, a scheme for securely sharing and distributing content via the public cloud. CloudSeal ensures the confidentiality of content in the public cloud environments with flexible access control policies for subscribers and efficient content distribution via content delivery network.

The purpose of this chapter is to provide insight into the internal audit challenges that a large Dutch bank was confronted with as the consequence of the IT transformation that was focused on leveraging the opportunities offered in the public cloud. Our work contributes to the body of literature by providing a conceptual framework that can help institutions navigate their assurance requirements in the public cloud. Furthermore, we provide practical attention points from the field for IT auditors. Finally, discussion points are shared with respect to the IT auditing of the traditional on-premise and the public cloud environment.