Private Cloud Research Articles

Chef Automation on Google Cloud

Abstract: This project is about Chef Automation. Generally if you want to install particular software or a package on a single server you can install it easily. Imagine you are maintaining an organization and you have thousands of servers running in your production environment. At this point, you cannot go and install packages or software’s on all thousand servers and maintaining all these servers is also a difficult job. With our project which is chef automation, you can automate all these processes or any installations on all thousand servers in a single click. Here we can save a lot of time and manpower by deploying the code to all servers. In chef automation we have three nodes: Server, Workstation and Client. Chef automation is nothing but automating the processes on client machines. First, we will be writing cookbooks on workstation (cookbooks are the actual programs that need to be run on client machines). These cookbooks are written in ruby programming language. After developing cookbooks these are needed to be pushed on to the server node. Keywords: Chef, Cloud Shell, Compute Engine, Cookbook, Google Cloud, My SQL, Recipe, Subnet, Virtual Machine, Virtual Private Cloud.

IoE based private multi-data center cloud architecture framework

This paper proposes an Internet of Everything (IoE) based private multi-data center cloud architecture framework to utilize the merits of multi-data centers. The inclusive security structure for the entire cloud data center presented here is to minimize the potential vulnerabilities from the adoption of the cloud. This work would leverage the novelty in allocating the distribution of data smartly and in a reliable manner to have the network for private cloud data centers. On the other hand, the architecture must have the ability to make out an enterprise's data and association applications in the cloud using the facility of contributing the opportunity. To overcome these shortcomings, a "Facility Overhead-aware multi-objective Dynamic Private Cloud Data Center Algorithm" (FOMDPC) is proposed with the merits of migration payload awareness, dynamic load balancing methodology, and facility infrastructure costs. The factors to be considered to determine the efficiency are time complexity and memory overhead. In addition, aggregated migration energy consumption and aggregated SLA violations are also taken into account.

Selecting cloud computing software for a virtual online laboratory supporting the Operating Systems course

The article provides a survey on cloud platforms suitable for a virtual online laboratory, which contains Linux online environments and is intended to support the Operating Systems course.The study justifies the choice of utilizing private cloud as a deployment model and IaaS as a service model and substantiates the decision to create specially tailored cloud environments adapted for educational needs in contrast to applying ready-made IaaS (Infrastructure as a Service) cloud services given by providers. The related works on cloud platforms for teaching operating systems are analyzed. The study also makes a review of the authors' previous research on virtualization tools and environments for the Operating Systems course and Cisco CyberSecurity Operations course. The basic and additional requirements for cloud computing software for virtual online laboratory supporting Operating Systems course have been elaborated. Finally, the work makes the comparison of Eucalyptus, OpenStack, CloudStack and OpenNebula cloud platforms and substantiates the selection among these cloud computing software the platforms of the first and the second choice.

An Efficient Algorithm for Mapping Deep Learning Applications on the NoC Architecture

Network-on-chip (NoC) is replacing the existing on-chip communication mechanism in the latest, very-large-scale integration (VLSI) systems because of their fault tolerant design. However, in addition to the design challenges, NoC systems require a mechanism for proper application mapping in order to produce maximum benefits in terms of application-level latency, platform energy consumption, and system throughput. Similarly, the neural-network (NN)-based artificial intelligence (AI) techniques for deep learning are gaining particular interest. These applications can be executed on a cloud-based system, but some of these applications have to be executed on private cloud to integrate the data privacy. Furthermore, the public cloud systems can also be made from these NoC platforms to have better application performance. Therefore, there is a need to optimally map these applications on existing NoC-based architectures. If the application is not properly mapped, then it can create a performance hazard that may lead to delay in calculations, increase in energy consumption, and decrease in the platform lifetime. Hence, the real-time applications requiring AI services can implement these algorithms in NoC-based architectures with better real-time performance. In this article, we propose a multilevel mapping of deep learning AI applications on the NoC architectures and show its results for the energy consumption, task distribution profile, latency, and throughput. The simulation is conducted using the OCTAVE, and the simulation results show that the performance of the proposed mapping technique is better than the direct mapping techniques.

Novel Method for Safeguarding Personal Health Record in Cloud Connection Using Deep Learning Models.

It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient's criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.

Hypergeometrically Represented Responsive and Reliable Cloud Service Model for Personal and Private Clouds

Hypergeometrically Represented Responsive and Reliable Cloud Service Model for Personal and Private Clouds

Secure PAAS environment over hybrid cloud using load-balanced Docker containers

The purpose of this research is to provide cloud service providers (CSP) a secure platform that can be accessed for configuring secure, silent software deployment on the hybrid cloud using load-balanced Docker Containers (LBDC) that are configured with different PAAS software solutions. As many researchers in the past have worked on providing PASS platforms using either a public cloud or private cloud less research is carried out on the hybrid cloud systems and researchers who have worked on the hybrid cloud have followed a different approach to providing PAAS to organizations in need but lack in securing the PAAS platform. This research will provide cloud service providers a secure deployment of the PAAS platform for the developers on the go and will reduce the overhead of CSP to deploy the PAAS services from scratch. Compared to the other researcher’s contribution this research work provides a load-balanced containerized secure PASS platform that can be scaled on-demand and be secure while scaling the resources for peak workloads. The admins of the hybrid cloud can access and deploy the Docker containers based on the requirement laid down by the developers. Once the Admin configures the Docker container with the required PAAS services he can then easily invoke the LBDC in the hybrid cloud and deploy the PAAS platform for the developers to work on in seconds. LBDC’s will help in scalable deployment of Secure PAAS services for different locations and machines simultaneously thus reducing the cost and time required for hybrid virtual machines to get up and running for the development process by the developers of the company. CSP’s can achieve less turnaround time for deployment of secure PAAS environments for organizations.

5G architecture for hybrid and multi-cloud environments

The amazing growth of cloud infrastructure that we see today is the result of many years of open-source innovation combined with the efforts of hyperscale cloud providers such as Amazon Web Services, Microsoft Azure and Google Cloud Platform, as well as large and medium-size communication service providers that have created their own private clouds in-house.

Energy-Efficient Resource Allocation and Migration in Private Cloud Data Centre

The level of difficulty that can be envisioned in a cloud data center will not grow with convention. As a result, all hosts should have a standard and pervasive collection of memory and communication characteristics in order to lower ownership costs and operate virtual machine instances. This solution includes fundamental foundations and integrated component basics that will allow an IT or federal agency to embrace cloud computing domestically via private virtual cloud data centers. These private cloud data centers would later be developed to purchase and develop IT services on the outside. They are well aware of the obstacles to cloud computing’s acceptance, including concerns about credibility, privacy, interoperability, and marketplaces. In addition, this procedure describes critical standards and collaborations to address these issues. Ultimately, it offers a coherent response to deploying safe data centers using cloud computing services from both a technological and an IT strategic standpoint. To foster creativity, invention, learning, and enterprise, a private data center and cloud computing must be established to combine the activities of different research teams. In the framework of energy-efficient distribution of resources in private cloud data center architecture, we focus on system structure investigations. On the other hand, we want to equip private cloud providers with the current design and performance analysis for energy-efficient resource allocation. The methodology should be adaptable enough to support a wide range of computing systems, as well as on-demand and extensive resource providing approaches, cloud environment scheduling, and bridging the gap between private cloud users and a complete image of offers.

Chaos as a Software Product Line—A platform for improving open hybrid‐cloud systems resiliency

AbstractNowadays, cloud‐native software architectures have a significant relevance due to the speed and agility they provide. These properties lead relevant organizations in different industries, like video streaming (Netflix), car‐sharing (Uber, Cabify), banking (BBVA, HSBC), and governmental agencies (NASA, FBI, CERN, ESA) to heavily rely on cloud‐native software to run their business‐critical applications. Additionally, including fault injection actions in the production infrastructure allows companies to have consistent environments, to improve applications dependability against unexpected failures, to provide better user experience, and to improve the overall system quality. Thus, cloud computing technologies allow development teams to rapidly create complex systems and to continuously deploy them, at a global scale. This work describes Pystol, a novel fault injection platform—represented as a Software Product Line—to analyze the effects caused by a wide spectrum of adverse conditions. Pystol is designed to be executed on top of cloud‐native environments, either in private or public clouds. The proposed architecture shows a way for representing feature models based on Unified Model Language (in short, UML) component diagrams. Furthermore, we present a thorough empirical study carried out in real‐world environments, providing promising results.

A Multi-Technique Approach to Exploring the Main Influences of Information Exchange Monitoring Tolerance

The privacy and security of online transactions and information exchange has always been a critical issue of e-commerce. However, there is a certain level of tolerance (a share of 36%) when it comes to so-called governments’ rights to monitor electronic mail messages and other information exchange as resulting from the answers of respondents from 51 countries in the latest wave (2017–2020) of the World Values Survey. Consequently, the purpose of this study is to discover the most significant influences associated with this type of tolerance and even causal relationships. The variables have been selected and analyzed in many rounds (Adaptive Boosting, LASSO, mixed-effects modeling, and different regressions) with the aid of a private cloud. The results confirmed most hypotheses regarding the overwhelming role of trust, public surveillance acceptance, and some attitudes indicating conscientiousness, altruistic behavior, and gender discrimination acceptance in models with good-to-excellent classification accuracy. A generated prediction nomogram included 10 ten most resilient influences. Another one contained only 5 of these 10 that acted more as determinants resisting reverse causality checks. In addition, some sociodemographic controls indicated significant variables afferent to the highest education level attained, settlement size, and marital status. The paper’s novelty stands on many robust techniques supporting randomly and nonrandomly cross-validated and fully reproducible results based on a large amount and variety of evidence. The findings also represent a step forward in research related to privacy and security issues in e-commerce.

A Review on Cloud Computing Technology, Cloud Deployment and Service Delivery Models

Abstract: With the advent of Cloud Computing technology, organizations are rapidly shifting their IT systems from traditional client/server to cloud computing model where everything is offered as a service whether it’s a software, hardware or storage capacity. Cloud computing uses the concept of pay as per use basis where users do not need to pay for infrastructure, installation and its maintenance. Anyone can access the desired service from the cloud anytime, anywhere in the world on demand basis. The utilization of this innovative technology makes collaboration easier among companies or organizations and has the potential to create financial and operational benefits. This study helps organizations and individuals understand how cloud computing can provide them with reliable, personalized and cost-efficient services in a wide variety of applications. Keywords: Cloud Computing; Public Cloud; Private Cloud; Hy

Analysis on Cloud Computing Architectures

Cloud computing is an evolving technique and emerged the entire IT industry in recent years. It transformed the IT landscape and brought the world in the hands of public. One can access the resources available on cloud on pay-as-you-use basis, like applications and infrastructure provided by cloud providers in the form of applications already deployed by cloud providers for use by the cloud users. These services are developed, tested and deployed before they brought into use. Another feature of cloud computing is its massive storage infrastructure made available for database and data supplied by the user. The cloud has several unique architectures and many more are still evolving. The services like SaaS, PaaS and the IaaS are deployed on private, public, community and hybrid clouds. In this paper we discuss the present developments in the cloud computing architecture and also try to present feasible guidance for further processing. Papers published in journals, conferences, white papers are analyzed. We attempt to identify, examine and explain the current trends and development in cloud computing architecture. However, only 13% of the papers examined discussed Others-as-a-Service, while only 26% of the papers reviewed considered issues relating to the major actors involved in cloud computing. This will beneficial to cloud providers, users, and researchers alike

A Systematic Literature Review on Security Challenges In A Hybrid Cloud Database

Cloud computing paved the way to many technical facilities for companies to develop their business needs in more effective and efficient manner. Combining private and public cloud into so called Hybrid cloud has made a positive leap in business by allowing applications and data to be shared among enterprises. However, many challenges and issues have arisen when adopting the hybrid cloud to manage, store and process data. The most critical one of these challenges is the security of the adopted Hybrid cloud. This research presented a comprehensive study about the security challenges in the Hybrid cloud computing as well as the suggested solutions. The study used a Systematic Literature Review (SLR) process to collect, review and summarize published articles from IEEE and Springer Nature databases and between 2020 and 2021. As a result, there were 7 eligible articles selected according to the search criteria and fully reviewed. The findings have revealed that there are four main challenges which are Data Security, Access Control, Privacy, Data Leakage and Cy- ber Attacks. Future studies should be conducted using different databases to have further investigation regarding the security in Hybrid clouds.

Internet of vehicles security situation awareness based on intrusion detection protection systems

With the rise of technologies such as mobile Internet, 5G networks and artificial intelligence, the development of Internet of Vehicle Information Security (ICVS) has become the mainstream and direction for the future development of the automotive industry. ICVS, people, roads, clouds, and APP constitute a complex network of vehicles. As part of the Internet, vehicle networking will inevitably face various complex information security threats and risks. This paper aims to design a kind of security situation awareness of Internet of vehicles based on intrusion detection protection systems (IDPS). By collecting the security data of car, app and private cloud for big data analysis, the whole smart car security situation awareness system is constructed. The system can be used to analyze potential threats, send out warnings, and carry out emergency responses.

Non-conventional Bio-Cryptic DOST Features for Private Cloud Secure Access Using Machine Learning Algorithms

Security issues in cloud computing is always challenging task for the researchers and practitioners. Especially in private cloud security is one of the critical issues to grant access to the remote server. Biometric authentication process will be one of the best solutions to grant access for private cloud server. This paper proposes a novel integrated technique for the secure cloud access by considering Bio-cryptic with DOES features, designated as ‘Bio-Cryptic DOST’ (BCDOST) method. The method is implemented in Matlab and trained with 6000 data samples and tested using 5000 biometric data samples that includes, finger, face, iris and palm biometric features. Overall, 98.7% has obtained on a K-fold cross-validation (k=5), and also the results was compared with the present DOST and 4 different customary strategies. supported the results, it's over that, the proposed methodology performed well with relation to accuracy and computation-time.

Availability evaluation of system service hosted in private cloud computing through hierarchical modeling process

Availability evaluation of system service hosted in private cloud computing through hierarchical modeling process

Performance prediction of deep learning applications training in GPU as a service systems

Data analysts predict that the GPU as a service (GPUaaS) market will grow to support 3D models, animated video processing, gaming, and deep learning model training. The main cloud providers already offer in their catalogs VMs with different type and number of GPUs. Because of the significant difference in terms of performance and cost of this type of VMs, correctly selecting the most appropriate one to execute the required job is mandatory to minimize the training cost. Motivated by these considerations, this paper proposes performance models to predict GPU-deployed neural networks (NNs) training. The proposed approach is based on machine learning and exploits two main sets of features, thus capturing both NNs properties and hardware characteristics. Such data enable the learning of multiple linear regression models that, coupled with an established feature selection technique, become accurate prediction tools, with errors below 12% on average. An extensive experimental campaign, performed both on public and in-house private cloud deployments, considers popular deep NNs used for image classification and speech transcription. The results show that prediction errors remain small even when extrapolating outside the range spanned by the input data, with important implications for the models’ applicability.

Забезпечення функціонування системи прогнозування атмосферних забруднень у хмарній інфраструктурі шляхом автоматизації створення віртуальних машин

The paper improves the web service Povitrya aimed at forecasting air pollution in case of man-made disasters by saving the configured CALPUFF atmospheric transport model in a pre-configured image and integrating into the system of software tools for the automatic creation of virtual machines from a pre-saved image after the user request. To do this, an image based on the Ubuntu operating system was configured in the Ukrainian National Grid Infrastructure, to-gether with the necessary CALPUFF atmospheric transport model libraries. Based on the saved image, one can quickly create and run a virtual machine and start the calculations. After the cal-culation, the Povitrya system removes the virtual machine, freeing up cloud resources for other tasks. The example of test calculations shows that due to the transfer of the module of calcula-tion of atmospheric transport to the cloud infrastructure it was possible to reduce the calculation time of the CALPUFF model by almost 2 times, and the download time of weather forecast data was reduced by more than 10 times. In total, the system calculation time after the user's request was reduced by 4 times for the test configuration. In addition, the resilience of the system to cloud infrastructure failures has been increased. The created archive with the operating system and configured for the CALPUFF model runs can also be transferred to other private clouds (for example, Amazon, Google Cloud Platform, Microsoft Azure) and their virtual machines could be used. Thus, the developed web system corresponds to modern trends in the implementation of cloud computing technologies, if necessary, allows scaling and can be adapted to other pri-vate or public cloud computing infrastructures. The system is available for registered users by the link: http://cloud-2.bitp.kiev.ua/ airsystem_english/airsystem.html.

Sustainable Intrusion Detection with New Attack Classification in Private Clouds

Traditional data-driven intrusion detection systems (IDSs) are typically based on the recognition of some specific features, regulations or patterns belonging to the well-defined known attacks, so they cannot separate new or unknown attacks from abnormities and may even confuse new attacks and legitimate behaviors. With the development of artificial intelligence (AI) technology, it becomes the mainstream technology to improve the detection performance of intrusion detection system. However, the available AI-driven IDSs can hardly classify different types of new attacks separated from abnormities, and they are usually not dedicated to the private cloud, edge or fog computing environments, where the update of the new attack recognition can be very different from that of the public environments. In this article, we present a novel sustainable and AI-driven intrusion detection scheme to support the classification of new attacks in the private clouds. We first adopt the convolutional neural network algorithm to recognize the known attacks, and then propose a new model of recognition and classification for unknown attacks based on network behaviors. We further propose a new approach to update the attacks recognition model for the private clouds. Finally, we provide extensive experiment results to demonstrate that our proposed scheme outperforms the previous IDSs in terms of attack detection accuracy, attack classification accuracy and updating efficiency.