Psychological Dimensions in Trust-Based Models in Cloud Computing

Cloud computing is a new technique of Information Communication Technology because of its potential benefits such as reduced cost, accessible anywhere anytime as well as its elasticity and flexibility. In this Paper defines cloud Computing, Definition, Essential Characteristics, model of Cloud Computing, Components of Cloud, Advantages & Drawbacks of Cloud Computing and also describe cloud computing in libraries. Ø Keywords: Cloud Computing, SaaS, PaaS, IaaS, Components of the Cloud, Models of Cloud Computing, Benefits of Cloud Computing in Library Servicesand Disadvantages, Libraries And Cloud.

In recent years, secure image archival and retrieval model in cloud computing (CC) received significant attention to ensure data confidentiality and secured data transmission among cloud server and clients cloud storage and users. Traditional secure image retrieval (IR) techniques are not applicable to adapt with a large-scale IR in cloud environment. In order to overcome these issues, this article introduces an effective secure IR scheme using Inception with ResNet v2 (SIRS-IR) and Multiple Share Creation (MSC) scheme. The proposed method uses Inception with ResNet v2 model based feature extraction process. At the same time, the MSC process is utilized for multiple share generation and then encryption of shares takes place using double chaotic logistic map (DLCM) technique. Besides, the encrypted shares and the respective feature vectors are saved in the cloud server with the corresponding image identification number. During the IR process for the applied QI, the SIRS-IR model extracts the feature vectors and performs similarity measurement to retrieve the related images from the database interms of encrypted shares. Finally, the share decryption process is carried out for the reconstruction of original images with no loss of quality. Extensive experimentations were performed to verify the retrieval performance and image quality of the reconstructed images using Corel10K dataset. The obtained results stated that the presented SIRS-IR model is found to be superior to other methods in a considerable way.

Cloud computing is a set of IT services offered to users over the web on a rented base. Recently, cloud computing is considered as a new internet based paradigm for hosting and providing hardware and software services the consumers. The main services models for cloud computing includes: software, platform and infrastructure (as a service), to satisfy the needs of different kinds of organizations. Security in cloud computing is critical when developing services, especially in the education area, cloud computing will be the basic platform of the future E-learning environment. It provides secure data storage, favorable and easy to use internet services and strong computing infrastructure for e-learning environment. This paper will focus on the effectiveness of using e-learning through cloud computing systems. The research study shows that the cloud computing model is extremely valued for both students and instructors to achieve the course objective. The paper presents the advantage, limitation and the challenges of using cloud computing services (IaaS, SaaS, PaaS) as a good platform for e-learning. The descriptive statistics, i.e. of the selected dependent and independent variables that related to the research samples, will be presented by the authors. Arithmetic mean, standard deviation, coefficient of standard variation, etc., will be include in the descriptive statistics. The results of the research study are mainly based on data from questionnaires of survey that have been held by the authors from to February 25, to May 20, 2018, among a sample of adults.

WITHDRAWN: Efficient Secure Aware Scheduling Model for Enhancing Security and Workflow Model in Cloud Computing

Machine learning (ML)-centric resource management in cloud computing: A review and future directions

Many organizations in education, healthcare, banking and the corporate sector are tending towards the use of cloud computing technology due to its efficiency and human resource savings. Organizations do not need to purchase infrastructure or data centres and can rent access for various applications to use over a network. The services provided by cloud computing are pay-per-use based, which reduces the overall cost. Cloud computing depends entirely on internet service over a network, with customers able to access data through data centres maintained by ISPs such as Google or Amazon. Cloud computing has great potential but faces many challenges to ensure security and privacy. This chapter describes various models of cloud computing, such as IaaS, PaaS and SaaS. Security and privacy concerns faced by the cloud computing environment are also addressed.

Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across the Internet. Edge computing is an evolving computing paradigm that brings computation and information storage nearer to the end-users to improve response times and spare transmission capacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones. However, CC and edge computing have security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Machine learning (ML) is the investigation of computer algorithms that improve naturally through experience. In this review paper, we present an analysis of CC security threats, issues, and solutions that utilized one or several ML algorithms. We review different ML algorithms that are used to overcome the cloud security issues including supervised, unsupervised, semi-supervised, and reinforcement learning. Then, we compare the performance of each technique based on their features, advantages, and disadvantages. Moreover, we enlist future research directions to secure CC models.

Cloud Computing: An Innovative Tool for Library Services

The Cloud Computing (CC) is an Internet-based technology which offers a shared pool of highly available, virtualized, dynamically scalable, and configurable computing resources (e.g., networks, servers, computing power, storage, applications, data, services, etc.). In the CC, the authentication as well as trust and reputation of the cloud users and service provider is a critical issue for the security of the cloud. Lots of articles previously present different trust models, but unfortunately, these trust models are not suitable for all cases as the trust is a non-measurable and subjective parameter that differs person to person. The trust involves many multi-dimensional factors such as evidence, reputation, expectations, feedback, history, risk, motivation, session behaviours, and so on. To addresses the above-mentioned issues, the paper present a novel multi-dimensional trust and reputation calculation model for the cloud computing, in which we integrate multiple trust factors that increase the novelty of the model. Additionally, we dynamically assign a weight for each trust and reputation factor using the Weighted Moving Average and Ordered Weighted Averaging (WMA-OWA) combination algorithm. The proposed model overcome the issues of the previous existing models. The detail presentation and analysis of the model shows that the model is appropriate and accurate for trust calculation in the cloud.

In cloud computing, a cloud user pays proportionally to the amount of the consumed resources (bandwidth, memory, and CPU cycles etc.). We posit that such a cloud computing system is vulnerable to DDoS (Distributed Denial-of-Service) attacks against quota. Attackers can force a cloud user to pay more and more money by exhausting its quota without crippling its execution system or congesting links. In this paper, we address this issue and claim that cloud should enable users to pay only for their admitted traffic. We design and prototype such a charging model in a CoreLab testbed infrastructure and show an example application.

Cloud computing, undoubtedly, has become the buzzword in the IT industry today. Looking at the potential impact it has on numerous business applications as well as in our everyday life, it can certainly be said that this disruptive technology is here to stay. Many of the features that make cloud computing attractive, have not just challenged the existing security system, but have also revealed new security issues. This paper provides an insightful analysis of the existing status on cloud computing security issues based on a detailed survey carried by the author. It also makes an attempt to describe the security challenges in Software as a Service (SaaS) model of cloud computing and also endeavors to provide future security research directions.

In recent years, the number of e-commerce netizen users in China has grown dramatically, and e-commerce users who shop online have also grown rapidly. In this competitive environment, the rapid development of e-commerce has also risen rapidly. Therefore, it is very necessary to actively carry out research on e-commerce models for cloud computing and internet of things technology. The purpose of this article is mainly to explore the research on e-commerce model for cloud computing and internet of things technology and establish a model for cloud computing and internet of things. It has data mining and distribution functions to test multiple indicators such as cloud computing facilities and enterprise performance. The research results show that the number of complaints on the e-commerce model based on cloud computing and internet of things technology is 2,368; the number of complaints on the B2B e-commerce model is 19,955; the number of complaints on the B2C e-commerce model is 5,016; the number of complaints in C2C e-commerce was 51,854.

A Cloud Computing model enables convenient, on demand network access, otherwise known as cloud computing (CC) or "the Cloud". Cloud computing is delivered via internet, using cloud." Everything from mobile applications to data centers on a pay-per-use basis. It is difficult to adopt CC and edge computing for several reasons, including security concerns, client vulnerabilities, and partnership acknowledgment. An algorithm that automatically improves with experience is known as a machine learning algorithm. This paper aims to examine some of the ML algorithms applied to CC security threats. Among the algorithms, we reviewed unsupervised, supervised, semi-supervised, and reinforcement learning for addressing cloud security challenges. CC models can be secured by pursuing future research directions.

Due to economic benefits of cloud computing, consumers have rushed to adopt Cloud Computing. Apart from rushing into cloud, security concerns are also raised. These security concerns cause trust issue in adopting cloud computing. Enterprises adopting cloud, will have no more control over data, application and other computing resources that are outsourced from cloud computing provider. In this paper we propose a novel technique that will not leave consumer alone in cloud environment. Firstly we present theoretical analysis of selected state of the art technique and identified issues in IaaS cloud computing. Secondly we propose Distributed Trust Protocol for IaaS Cloud Computing in order to mitigate trust issue between cloud consumer and provider. Our protocol is distributed in nature that lets the consumer to check the integrity of cloud computing platform that is in the premises of provider's environment. We follow the rule of security duty separation between the premises of consumer and provider and let the consumer be the actual owner of the platform. In our protocol, user VM hosted at IaaS Cloud Computing uses Trusted Boot process by following specification of Trusted Computing Group (TCG) and by utilizing Trusted Platform Module (TPM) Chip of the consumer. The protocol is for the Infrastructure as a Service IaaS i.e. lowest service delivery model of cloud computing.

The paper proposed a network scheduling in cloud computing based on intelligence Particle Swarm Optimization algorithm aimed at the disadvantages of cloud computing network scheduling.Firstly, on the basis of cloud model, used intelligence Particle Swarm Optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned the better solution into the initial pheromone of improved Particle Swarm Optimization algorithm, and found out the cloud computing network scheduling and the algorithm's global optimal solution through improved Particle Swarm Optimization information communications and feedbacks.Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence Particle Swarm Optimization algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution's time of objective function value is shorten which meet the user's needs more.