Cloud Computing Services Research Articles

Secure Deduplication with User-Defined Access Control in Cloud Storage

Abstract: Cloud storage is one of the most important services of cloud computing, offering significant benefits to users by allowing them to outsource their data for storage and sharing with authorized parties. Secure deduplication has been extensively researched in cloud storage, aiming to eliminate redundancy in encrypted data and reduce storage space. Our proposed solution focuses on uploading files to the cloud storage system. When a file is uploaded, it is checked to see if an identical file already exists. If a duplicate is found, the system displays an entry indicating that the file already exists. This secure and efficient data deduplication scheme ensures that users can store their data securely in the cloud, with access limited to authorized parties. By eliminating redundancy, more space is available for uploading different files. To ensure data security, we utilize the MD5 algorithm for hashing purposes and AES for encryption. Our system offers a practical solution for organizations seeking to securely store their data in the cloud, while also preserving user privacy and data ownership

Load Balancing of Cloud Computing Service Model Empowered with Fuzzy Logic

Nowadays, cloud computing is a growing field in research and the marketplace, including virtualization, internet service, computer software, and web services. A cloud comprises multiple elements like a consumer's data center and servers. It provides high availability, efficiency, scalability, mobility, fault tolerance, decreased overhead of users, reduced expenses of ownership, on-demand services, etc. A Variety Of users need various Quality of Service (QoS) demands. Therefore, the cloud supplier needs to arrange the tasks to get maximum advantages regarding their services, and the consumer's quality of service demands are satisfied. Currently, the need for cloud is increasing day by day, people moved towards cloud simultaneously, so its scale is up that's why the service providers are required to deal with enormous requests. The biggest challenge is the availability of services and maintaining the performance equivalent or more effective whenever workload occurs. Multiple requests are processed simultaneously; that's why the workload increased. The load balancer uses to resolve that issue. Our research shows how to balance the load in a cloud environment using fuzzy logic. The processor speed, storage capacity, and assigned load of Virtual Machine (VM) utilize to balance the load in cloud computing through fuzzy logic to achieve better processing time and storage utilization.

A Review of Cloud Computing Service Models

Cloud computing provides an infrastructure to process all types of data resources and also used for computing the data resources. It provides a good environment to work with data of large amounts. Despite all the technologies it has become the most important technology of the 21st century. In IT departments it focuses on strategic projects rather than to keep the data in running form. By understanding the solitary attributes and deliberation linked to each model. Any business or company can use it to take selective decision when they will worry about to select most profitable cloud computing for the maximum and specified requirement and ambition or intention. In this research paper we will present a comprehensive review on the different types of cloud computing services model. These models are used to provide intuition for consortium and to make decision- creator to purchase cloud technology. We are also discussing about the benefits, characteristics and comparison among IaaS, PaaS and SaaS.

A Bio inspired Approach for Load Balancing in Container as a Service Cloud Computing Model

A Bio inspired Approach for Load Balancing in Container as a Service Cloud Computing Model

A Profit Maximization Scheme with Guaranteed Quality of Service in Cloud Computing

A Profit Maximization Scheme with Guaranteed Quality of Service in Cloud Computing

Diabetes Prediction Using Colab Notebook Based Machine Learning Methods

Diabetes is getting more and more common around the world. People suffer from diabetes or live at risk associated with this disease. It is necessary to prevent health problems caused by diabetes, to reduce the risk of diabetes and to reduce a load of diabetes on the health system. Therefore, it is important to diagnose and treat diabetic patients early. In this study, Pima Indian Diabetes (PID) database was used to predict diabetes. Random Forest Classifier, Extra Tree Classifier and Gaussian Process Classifier machine learning methods have been used to predict whether individuals have diabetes or not. In this study, the method with the highest prediction accuracy was determined as the Random Forest Classifier. The accuracy of the recommended method was 81.71%. The proposed method was developed to assist clinicians in predicting diabetic patients using diagnostic measurements. The machine learning methods developed in this study were applied using Colab Notebook a Google Cloud Computing service.

Investigating the Use of Cloud Computing Services among Postgraduate Students at the University of Ibadan, Nigeria

Cloud computing services (CCS) offer many benefits which have improved their acceptance in the educational sector, especially, tertiary education. While it is true that the benefits of CCS include less worries about data loss, ease of accessibility and the ability to interact with data in real-time, there is no known study on how awareness of cloud computing services, perceived ease of use, computer self-efficacy, and intention to use CCS influence its actual use among postgraduate students in Nigeria. This study provides answers to four research questions and tests two research hypotheses. It adopts a descriptive survey design using a multistage sampling technique for data collection. Convenience sampling technique was used to select a sample size of 360 postgraduate students from the University of Ibadan, Nigeria. Descriptive statistics, Friedman mean rank, Spearman rank correlation and Multiple linear regression were used for analyses. The findings revealed that awareness of cloud computing services, perceived ease of use, and computer self-efficacy had significant relationships with the intention to use cloud computing services, and the intention to use cloud computing services had a significant influence on the actual use of cloud computing services among postgraduate students. The study recommends that university authorities should encourage courses that train students on the use of CCS, as well as course curricula to improve students’ computer self-efficacy and their use of cloud computing services. Keywords: Awareness, Cloud Computing, Self-Efficacy, Intention to Use, Perceived Ease of Use, Postgraduate students, Cloud Computing Services, University of Ibadan, Nigeria

Data Integrity Verification in Cloud Computing

High security is one of the major obstacles for opening up the new era of the long dreamed vision of Cloud computing as a utility. In today’s trend of cloud computing, all the sensitive applications and data are moved towards cloud infrastructure and data center that run on virtual computing resources in the form of virtual machine. These attributes poses many security challenges such as accessibility vulnerabilities, virtualization vulnerabilities and web application vulnerabilities. We primarily aim to achieve better data integrity verification technique for Data Storage as a Service (Daas) in cloud computing and help users to recognize the threats associated with their uses.

Financial and Marketing Strategy Analysis of Amazon

Amazon (Amazon.com) is the biggest online retailer in the world and a well-known cloud service provider. Amazon began as an online bookstore but has now evolved into a web-based company primarily specializing in e-commerce, cloud computing, digital streaming, and artificial intelligence (AI) services. The company, which uses an Amazon-to-buyer sales model, has a massive product selection and inventory that allows customers to purchase almost everything, including clothing, household products, furniture, toys, jewelry, books, movies, electronics, pet supplies, and gourmet food. This paper selects the annual report data of Amazon from 2017 to 2021, analyzes the indicators of its profitability, solvency and operating capacity, and evaluates its profit model from a financial perspective in combination with DuPont analysis and cash flow analysis.

Attribute-Based Expressive and Ranked Keyword Search Over Encrypted Documents in Cloud Computing

Attribute-based keyword search (ABKS) has been proposed to realize fine-grained access control and provide search service in cloud computing. However, most ABKS schemes focus on single or conjunctive keyword search, while the recent Boolean keyword search schemes only support monotonic query formula mainly involving AND, OR and threshold operators. How to support more expressive Boolean query formulas and return the corresponding accurate search results to users have become challenges for practical ABKS over ciphertexts. In this paper, we introduce an attribute-based expressive and ranked keyword search scheme over encrypted documents named ABERKS, which allows authorized users to submit expressive Boolean query formulas involving AND, OR, NOT and threshold operators. ABERKS utilizes a non-monotonic access tree structure to construct the query formula, and further leverages extended Boolean model to rank the search results. Specifically, the users are able to define the weights in the query formula, and get the relevance score of each matched ciphertext if the attributes and keywords are both satisfied. We prove the security of ABERKS against chosen keyword attack under selective ciphertext policy model and against keyword guessing attack, and also conduct extensive experiments to show the efficiency and practicality of ABERKS.

The Impact Cloud Computing Services in It Industry of Malaysia

This particular study is based on impact of cloud computing in IT industry of Malaysia. Globalisation and industrialisation have enhanced the importance of technological advancements in all business sectors. These can foster business financial growth. On the other hand, pandemic situation has brought different adversities in social conditions of local people. This has increased the importance to implement this technology that has ability to resolve the financial and social conditions as well. This is the reason, developing countries such as Malaysia has adapted this approach. This study has been accomplished with “positivism philosophy, deductive approach and secondary data collection method”. Proper justification with the research objectives has been provided in the last section of this study.

Online Throughput Maximization on Unrelated Machines: Commitment is No Burden

We consider a fundamental online scheduling problem in which jobs with processing times and deadlines arrive online over time at their release dates. The task is to determine a feasible preemptive schedule on a single or multiple possibly unrelated machines that maximizes the number of jobs that complete before their deadline. Due to strong impossibility results for competitive analysis on a single machine, we require that jobs contain some slack ɛ > 0, which means that the feasible time window for scheduling a job is at least 1+ɛ times its processing time on each eligible machine. Our contribution is two-fold: (i) We give the first non-trivial online algorithms for throughput maximization on unrelated machines, and (ii), this is the main focus of our paper, we answer the question on how to handle commitment requirements which enforce that a scheduler has to guarantee at a certain point in time the completion of admitted jobs. This is very relevant, e.g., in providing cloud-computing services, and disallows last-minute rejections of critical tasks. We present an algorithm for unrelated machines that is \(\Theta (\frac{1}{\varepsilon })\) -competitive when the scheduler must commit upon starting a job. Somewhat surprisingly, this is the same optimal performance bound (up to constants) as for scheduling without commitment on a single machine. If commitment decisions must be made before a job’s slack becomes less than a δ-fraction of its size, we prove a competitive ratio of \(\mathcal {O}(\frac{1}{\varepsilon - \delta })\) for 0 < δ < ɛ. This result nicely interpolates between commitment upon starting a job and commitment upon arrival. For the latter commitment model, it is known that no (randomized) online algorithm admits any bounded competitive ratio. While we mainly focus on scheduling without migration, our results also hold when comparing against a migratory optimal solution in case of identical machines.

Service-Oriented Network Virtualization toward Convergence of Networking and Cloud Computing

A holistic approach that makes it possible to control, manage, and optimize both computing resources and networking in a Cloud environment is required because of the crucial role that networking plays in Cloud computing. This results in a convergence of networking and Cloud computing. As a crucial feature for the next generation of networking, network virtualization is being implemented in the Internet and telecommunications sectors. It is anticipated that virtualization will bridge the gap between these two fields as a potential enabler of profound changes in the communications and computing domains. When applied to network virtualization, Service-Oriented Architecture (SOA) creates a Network-as-a-Service (NaaS) paradigm that may significantly facilitate the convergence of networking and Cloud computing. The use of SOA in network virtualization has recently received a lot of attention from both academia and industry. Although numerous pertinent research papers have been published, they are currently dispersed across a variety of subject areas in the literature, such as cloud computing, telecommunications, computer networking, and Web services. Specifically, we first introduce the SOA principle and review recent research progress on applying SOA to support network virtualization in both telecommunications and the Internet. In this article, we present a comprehensive survey of the most recent developments in service-oriented network virtualization for supporting Cloud computing, particularly from the perspective of network and Cloud convergence through NaaS. Next, we discuss the most recent advancements in network service description, discovery, and composition, as well as a framework for network-to-cloud convergence based on service-oriented network virtualization. We also talk about the problems these technologies face because of network-cloud convergence and the research opportunities in these areas. Our goal is to get researchers interested in this new interdisciplinary field.

A Blockchain-Based Authentication and Authorization Scheme for Distributed Mobile Cloud Computing Services.

Authentication and authorization constitute the essential security component, access control, for preventing unauthorized access to cloud services in mobile cloud computing (MCC) environments. Traditional centralized access control models relying on third party trust face a critical challenge due to a high trust cost and single point of failure. Blockchain can achieve the distributed trust for access control designs in a mutual untrustworthy scenario, but it also leads to expensive storage overhead. Considering the above issues, this work constructed an authentication and authorization scheme based on blockchain that can provide a dynamic update of access permissions by utilizing the smart contract. Compared with the conventional authentication scheme, the proposed scheme integrates an extra authorization function without additional computation and communication costs in the authentication phase. To improve the storage efficiency and system scalability, only one transaction is required to be stored in blockchain to record a user's access privileges on different service providers (SPs). In addition, mobile users in the proposed scheme are able to register with an arbitrary SP once and then utilize the same credential to access different SPs with different access levels. The security analysis indicates that the proposed scheme is secure under the random oracle model. The performance analysis clearly shows that the proposed scheme possesses superior computation and communication efficiencies and requires a low blockchain storage capacity for accomplishing user registration and updates.

Smart Root Search (SRS) in Solving Service Time–Cost Optimization in Cloud Computing Service Composition (STCOCCSC) Problems

In this paper, the novel heuristic search algorithm called Smart Root Search (SRS) was examined for solving a set of different-sized service time–cost optimization in cloud computing service composition (STCOCCSC) problems, and its performance was compared with those of the ICACRO-C, ICACRO-I, ICA, and Niching PSO algorithms. STCOCCSC is an np-hard problem due to the large number of unique services available as well as the many service providers who provide services with different quality levels. Finding closer-to-optimal solutions supports cloud clients by providing them with higher quality-lower price services. The SRS obtained results proved that the SRS provided 6.74, 11.2, 47.95, and 87.29 percent performance improvement on average to the comparative algorithms, respectively, for all considered five problems. Furthermore, employing symmetry concepts in dividing the problem search space helps the algorithm to avoid premature convergence and any efficiency reduction while facing higher-dimensional search spaces. Due to these achievements, the SRS is a multi-purpose, flexible, and scalable heuristic search algorithm capable of being utilized in various optimization applications.

Share Your Data Carefree: An Efficient, Scalable and Privacy-Preserving Data Sharing Service in Cloud Computing

Benefiting from the powerful computing and storage capabilities of cloud services, data sharing in the cloud has been permeated across various applications including social networks, e-health and crowdsourcing transportation system. Intuitively, outsourcing data to untrusted cloud commonly raises concerns about data privacy breaches. To combat this, one approach is exploiting Broadcast Based Searchable Encryption (BBSE) for secure data sharing. Nevertheless, the latest proposed BBSE is still defective in either security or efficiency. In this paper, we propose ESPD, an Efficient, Scalable and Privacy-preserving Data sharing framework over encrypted cloud dataset. Different from previous works, ESPD supports sharing target data to multiple users with distinct secret keys, and keeps a constant ciphertext length with the changes of the amount of system users. This feature significantly improves search efficiency and makes ESPD scalable in real-world scenarios. We show a formal analysis to prove the security of ESPD in terms of file privacy, keyword privacy and trapdoor privacy. Also, extensive experiments on real-world dataset are conducted to indicate the desirable performance of ESPD compared to other similar schemes.

EFPB: Efficient Fair Payment Based on Blockchain for Outsourcing Services in Cloud Computing

Despite the maturity of cloud services (e.g., outsourcing of computational tasks), a number of operational challenges remain. For example, how do we ensure trust between outsourcers and workers in a zero-trust environment? While a number of blockchain-based solutions that eliminate the reliance on trusted third parties have been presented, many of these existing approaches do not achieve robust fairness and/or support compatibility with other systems. In this paper, we propose an efficient fair payment system using blockchain (EFPB), designed to achieve robust fairness and compatibility. Specifically, EFPB comprises a number of cryptographic building blocks, mainly: one-way accumulator (RSA-based construction), stealth address and symmetric encryption. We then evaluate the performance of EFPB to demonstrate that it is more efficient and low-cost than other competing schemes, as well as presenting a comparative summary of functionalities.

Checklist to verify that a Cloud Computing Service Level Agreement (SLA) signed by an PYME in Ecuador guarantees data security and protection

Data security and protection in the contracting of cloud services carried out by Ecuadorian (SMEs), requires knowing a series of necessary parameters that allow the user to identify a suitable provider in order to minimize the risks involved in storing information outside their own infrastructure, so as a result of this study, Ecuadorian SMEs are provided with a Checklist to verify the Levels of Service Agreements in such a way that the provider guarantees the security and protection of their data without implying explicit technical knowledge. To prepare the checklist, aspects such as: Types of cloud services, analysis of the main risks to which Ecuadorian companies have been exposed when contracting cloud services, and mapping of current Ecuadorian regulations have been analyzed vs. the main security risks identified (availability, access control, architecture, compliance with the standard) and finally, different international standards related to information security have been analyzed, focusing on the recommended controls in general information security as well as the information security of the cloud computing service.

A Review of the Challenges and Opportunities in Cloud Computing Services

A Review of the Challenges and Opportunities in Cloud Computing Services

Evaluation and Selection of Cloud Service: A neutrosophic model

The variety of cloud-based services that are now accessible is expanding at a fast pace, and as a result, it has become much more difficult for regular customers to choose the appropriate cloud services. When selecting an appropriate service to use in a setting where there is a high degree of unpredictability, it is in the user's best interest to be able to deal with ambiguous information. This is because the Cloud service environment contains a great number of unknowns, which may prevent the user from making wise choices. In this paper, the authors propose a framework that they call the Optimal Service Choice and Priority of Cloud Computing (CC) Service. This methodology gives cloud customers the ability to evaluate the various service options available to them relying on QoS (Quality of Requirements) standards. The model employs a mixed approach to decision making that is based on many factors. The PROMETHEE is used for the purpose of ranking and prioritizing the QoS criteria, and to get the final rank of cloud services. The suggested methodology is a Multi-criteria decision making approach due to this problem contain many conflicting criteria. The PROMETHEE is integrated with neutrosophic environment to overcome uncertainty information. The application of methodology is provided.