Single Cloud Service Provider Research Articles

Towards a federated and hybrid cloud computing environment for sustainable and effective provisioning of cyber security virtual laboratories

Cloud Computing (CC) and virtualization concepts are two advanced technologies introduced to empower distance and blended learning. Besides, they play a crucial role in equipping learners with practical skills and fostering hands-on experience to defend against cyber-attacks. Many Higher Education Institutions (HEIs) in developed countries have already embraced the promise of CC to raise educational standards. However, the pace of its adoption in developing countries has stagnated. Moreover, existing solutions in the literature are not sustainable. They either rely on on-premise infrastructure or are bound by a single cloud service provider. Consequently, they are likely prone to failures and a sudden outage. To fill this gap, this paper is a first that comprehensively addresses the above issues and introduces a federated hybrid CC system based on an extension of Apache Virtual Computing Lab (VCL). The proposed system provides an independent open-source implementation, greater configuration flexibility, and methodological improvements as compared to existing studies in the literature. In addition, it promotes the sustainability of the CC services, extensible cloud architecture, and fault tolerance. VCL is primarily focused on provisioning Virtual Laboratories (VL) for remote cybersecurity and computer networks education, with potential expansion to other domains of engineering education. In addition, this paper introduces GPT-TerminalPro, a terminal-based tool driven by OpenAI’s Generative Pretrained Transformer (GPT-3.5) that provides intelligent assistance to users while performing lab tasks. To experimentally evaluate the VCL’s performance, the standard Linux tools as well as the Apache benchmark and httperf HTTP load generators are utilized. VCL has been tested with 30 users and 61 virtual user computing environments provisioning to validate the overall performance. The results are fascinating: the provisioning time including all VCL background tasks is always less than a minute and utilizes fewer computing resources while providing a better user experience. This paper will encourage the adoption of CC in low-income countries.

Performing Distributed Quantum Calculations in a Multi-cloud Architecture Secured by the Quantum Key Distribution Protocol

Quantum computing (QC) is an emerging area that yearly improves and develops more advances in the number of qubits and the available infrastructure for public users. Nowadays, the main cloud service providers (CSP) are implementing different mechanisms to support access to their quantum computers, which can be used to perform small experiments, test hybrid algorithms and prove quantum theories. Recent research work have discussed the low capacity of using quantum computers in a single CSP to perform quantum computation that are needed to solve different experiments for real world problems. Thus, there are needs for computing powers in the form of qubits from multi-cloud environment. Quantum computing in a multi-cloud environment requires security of the communicating channels. A well known algorithm in quantum cryptography for this purpose is the quantum key distribution (QKD) protocol. This enables the sender and receiver of a message to know when a third party eavesdropped any data from the insecure quantum channel. To address the low capacity issue, this research develops and tests the use of heterogeneous quantum computers located on different CSP to distribute quantum calculations between them by leveraging the channel security provided by the QKD protocol. The achieved results show over 88.1% of correct distributed quantum computation results without error correction methods, 96.8% of correct distributed quantum computation results using error correction methods and over 98.8% correct authorisation detection in multi-cloud environments. This demonstrates that quantum calculations can be distributed between different CSP while securing the channel with the QKD protocol at the same time.

Online dynamic replication and placement algorithms for cost optimization of online social networks in two-tier multi-cloud

In social media, a huge number of worldwide data objects are posted every day. The contents of these data objects include text, links, images, audio, and videos which could be small, medium, or large and accessed across the world. Moving these data objects into a single cloud service provider (CSP) is risky and results in four-fold obstacles: vendor lock-in, service availability, cost-ineffective use, and increasing latency. Using multiple CSPs to replicate and distribute the data object solves such obstacles. However, replicating data objects among multiple CSPs increases the cost of creating and maintaining this replication. This study focuses on three issues of Online Social Network (OSN) which include: (1) determining the appropriate number of replicas of each data object based on its popularity on the OSN, (2) identifying the suitable datacenters that host the replicas according to latency time of different regions, and (3) deciding the suitable storage class for the data object at a specific time of its lifetime. Two algorithms are proposed to adapt the replication and placement of the data object according to its popularity in the OSN. The first algorithm is Dynamic Fixed Time (DFT) which uses fixed time periods to adapt replication and placement. The second algorithm is Dynamic Exponential Time (DET) which determines the data object replication and placement based on exponential time periods. A simulation using a synthesized workload generated based on a real Facebook statistic dataset shows that the proposed algorithms produce a monetary cost savings of more than 23% compared to the Static Replication and Local Placement (SRLP) algorithm.

Strategies for efficient resource management in federated cloud environments supporting Infrastructure as a Service (IaaS)

The increased market activity and visibility have resulted in a growing demand for additional computer resources to meet the needs of cloud clients. Delivering promised quality of service (QoS) while fulfilling diverse resource demands dynamically might be difficult for a single Cloud Service Provider (CSP). Federated cloud computing, also known as distributed cloud computing, is an established paradigm in which CSPs collaborate to aggregate unused resources, resulting in financial and QoS benefits. Notably, this strategy improves availability and reliability while overcoming individual CSP difficulties in preserving QoS amid fluctuations in resource demand. The federated cloud successfully leverages computing resources even during low-demand periods, demanding a comprehensive resource management strategy for Infrastructure as a Service (IaaS) inside participating CSPs. Such a strategy is crucial for preserving QoS, ensuring availability and dependability, and optimizing underutilized computing resources. This research presents the novel IaaS cloud design, revealing a unique methodology that reimagines traditional cloud systems. The proposed IaaS cloud framework investigates virtual machine migration and resource consolidation, building a strong foundation founded on IaaS principles and emphasizing the crucial role of virtualization. The technique introduces ground-breaking concepts such as Cloud User (CU) and Reputation Management, which are strengthened by specific algorithms that improve cloud service security and trust. Furthermore, the combination of Trust Manager (TM) and Broker Manager (BM) components strengthens SLA control and trust evaluation, aligning smoothly with IaaS standards to improve service quality and reliability. User Profiling, which is classified into private, social, and corporate profiles, provides a separate lens for successful cloud user management inside the IaaS landscape, allowing for customised service delivery. SMI and cutting-edge ranking algorithms, such as the Deep Q-based Algorithm, optimize cloud service provider selection and ranking—an important aspect of IaaS. The use of the Banker's algorithm and a comprehensive Service Level Agreement (SLA) Management plan provides efficient resource allocation, mirroring recognized IaaS standards. This research study not only throws light on these trailblazing techniques, but it also establishes a new standard for IaaS cloud design and resource management.

H2RUN: An efficient vendor lock‐in solution for multi‐cloud environment using horse herd Runge Kutta based data placement optimization

AbstractVendor lock‐in is a scenario where the cost of switching to another vendor is mainly high and for this reason, the consumer is mainly struck with a single cloud vendor. The vendor lock‐in mainly implies the situation of an organization where it is locked into a service or product of a single Cloud Service Providers (CSP) to prevent the disruption of the business operation or due to an insufficient workforce. Due to its high availability, portability, and other benefits, various researchers frequently adopt the multi‐cloud strategy to prevent vendor lock‐in. However, the multi‐cloud architecture also comes with major drawbacks such as cost, multiple CSP management, and the tradeoff between cost and availability. The organizations are mainly considerate about achieving the tradeoff between the cost and availability. To overcome this problem, we have proposed an H2RUN (Horse Herd Runge Kutta) optimization algorithm to achieve a tradeoff between the cost and availability using erasure coding. The proposed H2RUN optimization algorithm selects the optimal CSP and erasure coding parameters that enhance the availability of the CSP with minimal cost. The efficiency of the proposed methodology can be evaluated using the experiments conducted on different real‐time CSPs using various metrics. Based on the results achieved, the H2RUN optimizer minimizes the cost up to 48% and availability up to 58%.

An ensemble classification model for fake feedback detection using proposed labeled CloudArmor dataset

With an increase in the use of Cloud Services, Cloud Customers (CCs) have started browsing reviews about various Cloud Service Providers (CSPs) online provided by numerous CCs to gather their sentiments. This pattern has proved to be fruitful, but it can become ineffective if fake reviews are posted. This article aims at introducing an ensemble model to classify reviews as fake or genuine on the proposed labeled CloudArmour dataset. Labeling has been achieved through these aspects: Number of Reviews provided by a CC to single CSP; Number of CSPs reviewed by a CC; & Frequency of feedbacks published. Additionally, we have compared the performance of the proposed ensemble model with these supervised machine learning classification techniques: KNN; Logistic_Regression; and SVM_rbf on the basis of the following parameters: accuracy; precision, recall; and F1-Score. Ensemble model has yielded highest values as: 97.51%; 98.19%; 93.65%; & 95.86% respectively. Thus, we conclude that the ensemble model has outperformed other models.

Searchable encryption scheme for multiple cloud storage using double‐layer blockchain

SummaryWith the emergence of cloud storage security, how to ensure the security of cloud storage has become a research focus. The typical searchable encryption model consists of a single cloud service provider and has some unsolvable drawbacks. In this article, a new searchable encryption scheme in multi‐cloud was proposed based on blockchain. We first define a system model in multiple clouds and combine multiple cloud service providers to store data through a consortium chain. Then we store the encrypted documents and indexes in The Interplanetary File System (IPFS) and store the hash value and IPFS address of the documents in the blockchain. Our scheme can provide outsourced encrypted data retrieval based on multiple keywords, as well as verification schemes for the integrity of retrieved files. The theoretical analysis and experiments on real‐world data show the security and high performance of our scheme.

Blockchain for cloud exchange: A survey

Compared with single cloud service providers, cloud exchange provides users with lower price and flexible options. However, conventional cloud exchange markets are suffering from a number of challenges such as central architecture being vulnerable to malicious attacks and cheating behaviours of third-party auctioneers. The recent advances in blockchain technologies bring the opportunities to overcome the limitations of cloud exchange. However, the integration of blockchain with cloud exchange is still in infancy and extensive research efforts are needed to tackle a number of research challenges. To bridge this gap, this paper presents an overview on using blockchain for cloud exchange. In particular, we first give an overview on cloud exchange. We then briefly survey blockchain technology and discuss the issues on using blockchain for cloud exchange in aspects of security, privacy, reputation systems and transaction management. Finally, we present the open research issues in this promising area.

A Strategy for Live Migration of Virtual Machines in a Cloud Federation

Migration of virtual machines (VMs) from one data center to another is essential to deal with scenarios such as load balancing, maintenance, power management, VM failures, etc. Application of VM migration is not only limited to a single cloud service provider, but also to inter-cloud architectures such as cloud federation. Migration in a federation is a complex operation as it encompasses many autonomous service providers with different standards, rules, and protocols. Migration can be carried out in two ways, first, live and, second, non-live. In case of live migration, a VM continues to execute at the source in contrast to non-live where its execution is suspended. In this paper, a framework for secure live migration of VMs in a cloud federation is proposed. Costs associated with it is analyzed for serial, parallel, and improved serial strategies. The proposed framework is simulated using a CloudSim simulator. Metrics considered for evaluation are communication overhead, migration time, and downtime. Further, power consumption for all strategies is calculated and compared. It is observed that downtime is least for parallel, whereas migration time is least for improved serial. The power consumption is highest for parallel and least for improved serial.

Migration cost and profit oriented cloud federation formation: hedonic coalition game based approach

Cloud federation has paved the way for cloud service providers (CSP) to collaborate with other CSPs to serve users’ resource requests, which are prohibitively high for any single CSP during peak time. Moreover, to entice different CSPs to participate in federation, it is necessary to maximize the profit of all CSPs involved in the federation. Further, federation enables overloaded CSPs to distribute their load among other underloaded member CSPs of federation by migrating the virtual machines (VM). Migration of VM among member CSPs of federation, also enables to increase the reliability and availability of cloud services on occurrence of faults in the datacenters of CSPs. Thus it becomes important for CSPs to form a federation with other CSPs, in such a way that the migration cost of VMs between CSPs of the same federation is minimized and simultaneously profit of CSPs in federation is maximized. In this paper, we model the problem of forming federation among CSPs as a hedonic coalition game, with a utility function depending on profit and migration cost, with the objective of maximizing the former and minimizing the latter. We propose an algorithm to solve this hedonic game and compare its performance with other existing game-theory based cloud federation formation mechanisms.

Parallel checking of content integrity in multi‐cloud storage of heterogeneous sensor systems

SummaryHeterogeneous sensor systems collect a large amount of sensor data. Because of limited processing capability of sensor nodes, such data are typically stored and processed in a centralized environment in the cloud. However, when data are managed and processed by multiple cloud servers, users do not physically possess these data. The risk of unauthorized access to the cloud data increases dramatically. Therefore, enforcing the integrity of the outsourced data remotely becomes a challenge in the heterogeneous sensor system. Remote data possession auditing is a solution to guarantee the availability and integrity of data outsourced to the cloud. Most existing solutions only target at a single cloud service provider (CSP) environment without considering the multi‐cloud scenario. How to audit the data possession among the distributed CSP servers effectively becomes a major concern. In this paper, we propose a parallel cloud data possession checking scheme for the multi‐cloud environment. Our approach utilizes the homomorphic verification tag created by the Paillier cryptosystem to support unlimited query challenges and introduces the error‐correction encoding method to ensure error localization and data correction. We prototype our solution and analyze the scheme comprehensively. Our evaluation results demonstrate the efficiency and security of our method. It also supports simultaneous verification of multiple CSPs. Copyright © 2016 John Wiley & Sons, Ltd.

Federated Architecture for Ranking the Services in Cloud Computing

Objectives: To improve Quality of Services (QoS) and ranking cloud service providers in federated cloud environment. It is also aimed to resolve various issues faced by the user and providers in cloud. Methods: In order to avoid the workload of existing cloud model and to compete with the well-known cloud service providers, customized federated cloud provider architecture was suggested. It is suggested to manage and to maintain QoS for the submitted tasks. The proposed architecture consists of external world, middle world and internal world. Each world plays vital role for customizing the request in federated environment to enhance QoS. To avoid starvation in the architecture to suggest the differentiated module and resolves the various key issues using the Stochastic Markov process model and also evaluating the cloud providers based on the quality of service requirements. Findings: Service Level Agreement (SLA) in single and federated cloud plays a vital role in enhancing Quality of Services. Single cloud service provider representation does not provide the skilled QoS, when workload becomes high. Customized federated cloud architecture reduces the drawbacks of single cloud service provider. The proposed architecture was implemented in CloudSim using Java. The simulation result of the proposed architecture proves enhanced QoS to the user and cloud service providers. The proposed federated cloud model enhances the Quality of Services by more than 18% of existing single and federated cloud model. The parameters considered for the simulation for numbers of users, providers, load factor, turnaround time and average load deviation of tasks. Applications: This architecture can be used to rank different cloud service providers and it can be trusted for any distributed cloud services with extended QoS.

Current Opportunities and Challenges of Next Generation Sequencing (NGS) of DNA; Determining Health and Diseases

Many publications have demonstrated the huge potential of NGS methods in terms of new species discovery, environment monitoring, ecological studies, etc. [24,35,92,97,103]. Undoubtedly, NGS will become one the major tools for species identification and for routine diagnostic use. While read lengths are still quite short for most existing systems ranging between 50 bp and 800 bp, they are likely to improve soon. This will enable easier, faster, and more reliable contig assembly and subsequent matching against reference databases. When data generation is no longer a bottleneck, the storage, speed of analysis, and interpretation of DNA sequence data are becoming the major challenges. Also, the integration or the use of data originating from diverse datasets and a variety of data providers are serious issues that need to be addressed. Poor sequence record annotations and species name assignments are known problems that should be instantly addressed and would allow the creation of reference databases used for routine diagnostics based on NGS. Samples with huge amounts of short DNA fragments need to be analyzed and compared against reference databases in an efficient and fast way. Although a number of solutions have been proposed by Industry; offering commercial software, there still remain hurdles to take. One of the challenges that we need to address is data upload from client’s computers to central or distributed data storage and analysis services. Another one is the efficient parallelization of analyses using cloud or grid solutions. The reliability and up-time of storage and analyses facilities is another important problem that need to be addressed if one wants to use it for routine diagnostics. Finally, the management, reporting and visualization of the analyses results are among the last issues, but not the least challenging ones. Considering the constant growth of computational power and storage capacity needed by different bioinformatics applications, working with single or a limited number of servers is no longer realistic. Using a cloud environment and grid computing is becoming a must. Even single cloud service provider can be restrictive for bioinformatics applications and working with more than one cloud can make the workflow more robust in the face of failures and always growing capacity needs. In this white paper we review the current state of the art in this field. We discuss the main limitations and challenges that we need to address such as; data upload from client’s computers to central or distributed data storage and analysis services; efficient parallelization of analyses using grid solutions; reliability and up-time of storage and analyses facilities for routine diagnostics; management, retrieving and visualization of the analyses results.

Ensuring Data Integrity Using Cooperative Provable Data Possession for Multi Cloud Environment

Cloud computing is a new and fast growing technology that offers an innovative, efficient and scalable business model for organizations. Cloud computing is type of computing where resources are shared rather than having local servers or personal devices to handle various applications. Cloud computing is a type of internet based computing where different services like server, storage, application, and network are delivered to the organizations. Although almost every business organization is going to adopt this technology due to its various advantages. When cloud computing become more perfect organizations and user will store enormous amount of data on remote cloud storage to achieve remote access, reduced cost, data collection and sharing of other services. To access services from cloud user can have account associated with single and multiple cloud service providers (SPs). Important aspect for cloud computing environment is to maintain integrity of stored data. Implementation of encryption of the information is done in such a way that it will be impossible for the attackers to read the resources sent on the web. Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) are the methods used for the encryption. Result will be text (cipher) which is decrypted on the receiver’s side. AES and ECC algorithm implemented together to provide sound security.

MCACM: A Cloud Storage Access Control Model for Multi-Clouds Environment Based on XACML

Cloud storage as a popular application in cloud services, are developing from single cloud service provider towards multiple cloud service providers. As a consequence, users need to manage their files from different sources and share in multi-clouds. While the conventional access control models are no longer suitable in multi-clouds environment. Therefore, a multi-clouds access control model based on XACML, named MCACM, is proposed here to implement access control in multi-clouds environment. In this model, cross-clouds access control is enabled through extending standard XACML framework. At last, we implemented a prototype of MCACM, and performance evaluation results show that our scheme is efficient.

Automation of Discovery and Aggregation of Cloud Services

Cloud computing is a technology where IT-related resources are dynamically provided as a service to the customers through Internet. The customer can demand for the services dynamically from Cloud Service Provider (CSP)s, take them on lease based on Service Level Agreement (SLA), release the resources after completion of task and pay for what is used. The required services may not be available from a single CSP. There are many CSPs providing multiple services with different Quality of Service (QoS). The customer has to discover the available services with the expected QoS which is one of the major challenges to be solved in cloud computing today. In this paper we dynamically create a repository of the cloud services and aggregate them whenever there is a demand for service and then derive that the services obtained from the repository are time efficient as compared to direct service discovery.