Multiple Cloud Service Providers Research Articles

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.

Multi-replicas integrity checking scheme with supporting probability audit for cloud-based IoT.

Nowadays, more people are choosing to use cloud storage services to save space and reduce costs. To enhance the durability and persistence, users opt to store important data in the form of multiple copies on cloud servers. However, outsourcing data in the cloud means that it is not directly under the control of users, raising concerns about security and integrity. Recent research has found that most existing multicopy integrity verification schemes can correctly perform integrity verification even when multiple copies are stored on the same Cloud Service Provider (CSP), which clearly deviates from the initial intention of users wanting to store files on multiple CSPs. With these considerations in mind, this paper proposes a scheme for synchronizing the integrity verification of copies, specifically focusing on strongly privacy Internet of Things (IoT) electronic health record (EHR) data. First, the paper addresses the issues present in existing multicopy integrity verification schemes. The scheme incorporates the entity Cloud Service Manager (CSM) to assist in the model construction, and each replica file is accompanied with its corresponding homomorphic verification tag. To handle scenarios where replica files stored on multiple CSPs cannot provide audit proof on time due to objective reasons, the paper introduces a novel approach called probability audit. By incorporating a probability audit, the scheme ensures that replica files are indeed stored on different CSPs and guarantees the normal execution of the public auditing phase. The scheme utilizes identity-based encryption (IBE) for the detailed design, avoiding the additional overhead caused by dealing with complex certificate issues. The proposed scheme can withstand forgery attack, replace attack, and replay attack, demonstrating strong security. The performance analysis demonstrates the feasibility and effectiveness of the scheme.

Cost-Effective Resource Allocation and Optimization Strategies for Multi-Cloud Environments

Abstract: As organizations increasingly adopt multi-cloud strategies to leverage the benefits of multiple cloud service providers, the need for effective cost optimization becomes paramount. This research paper aims to explore and propose innovative approaches for optimizing costs in multi-cloud environments. The study will investigate resource allocation, workload placement, and performance optimization techniques, taking into account factors such as workload variability, service-level agreements (SLAs), and pricing models across different cloud providers. The objective is to develop cost-effective strategies that balance performance and cost across a heterogeneous cloud ecosystem, ultimately helping organizations make informed decisions in their multi-cloud deployments. This research topic addresses the growing interest in multi-cloud architectures and the associated challenges in managing costs while ensuring high-quality service delivery. It provides a valuable contribution to the field of cloud computing and cost optimization, which is increasingly crucial for businesses and enterprises seeking to maximize the benefits of cloud technology.

Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities.

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement.

A truthful dynamic combinatorial double auction model for cloud resource allocation

Dynamic auction-based resource allocation models require little global price information, are decentralized and suitable for the distributed systems like cloud computing. For the cloud computing market, we proposed a Truthful Dynamic Combinatorial Double Auction (TDCDA) model to improve the social welfare and resource utilization. In our model, multiple cloud service providers and cloud users bid for various resources in a dynamic environment. We adopted a payment scheme to ensure truthfulness for all participants, which motivates bidders to reveal their true preferences. Since the combinatorial auction allocation with goal of economic efficiency is NP-hard, we developed a greedy mechanism to achieve the approximately efficient solution. Considering both parties’ interests and the resource scarcity, this model also ensures fairness and balances resource allocation. The proposed model is proven to be approximately efficient, incentive compatible, individually rational and budget-balanced. Simulation results show that the model not only achieves economic efficiency, but also improves resource allocation and meets resource needs for more cloud users.

GDPR compliance verification through a user-centric blockchain approach in multi-cloud environment

With cloud-hosted web applications becoming ubiquitous, the security risks presented for user personal data that is migrated to the cloud are at an all-time high. When using a cloud-hosted web application, users only ever interact with web interfaces of the web applications and are usually completely unaware of how their data is distributed amongst the multiple cloud service providers that the web application uses, making it difficult to verify the lawful use and ownership of personal data. The General Data Protection Regulation (GDPR) seeks to empower users to gain better control over their personal data. Blockchain-based approaches have risen in popularity over the recent years to tackle the challenge of verifying GDPR compliance in multi-cloud environments. By deploying smart contracts on the blockchain, we can create transparent and immutable logs of data processes in the hopes of automating GDPR compliance verification. However, the existing works are still limited to provide a user-centric compliance verification. To this end, we propose a user-centric, blockchain-based framework for data management in a cloud environment where all GDPR-relevant data operations take place on the blockchain through well-defined smart contracts.

Smart City IoT Data Management with Proactive Middleware

With the increased emergence of cloud-based services, users are frequently perplexed as to which cloud service to use and whether it will be beneficial to them. The user must compare various services, which can be a time-consuming task if the user is unsure of what they might need for their application. This paper proposes a middleware solution for storing Internet of Things (IoT) data produced by various sensors, such as traffic, air quality, temperature, and so on, on multiple cloud service providers depending on the type of data. Standard cloud computing technologies become insufficient to handle the data as the volume of data generated by smart city devices grows. The middleware was created after a comparative study of various existing middleware. The middleware uses the concept of the federal cloud for the purpose of storing data. The middleware solution described in this paper makes it easier to distribute and classify IoT data to various cloud environments based on its type. The middleware was evaluated using a series of tests, which revealed its ability to properly manage smart city data across multiple cloud environments. Overall, this research contributes to the development of middleware solutions that can improve the management of IoT data in settings such as smart cities.

Making your Own NAS

This project aims to deploy the personal NAS system for remote and centralized storage solutions for all types of handsets and systems. Modern network-attached storage architectures suffer from poor performance, resource management, and inflexibility. We examine the problems of current architectures and propose another format for doing network storage at the user level. Nowadays, we have multiple cloud service providers, so there will always be an issue with data protection and backups. So, to solve all these issues, we came up with the new pre-existing idea of NAS, to leverage the data storage over the network using a GUI or dedicated devices. NAS will act as a public or private cloud storage service, which can be accessed from anywhere on the globe through internet facilities.

Cloud Service Provider Selection Using Fuzzy Data Envelopment Analysis Based on SMI Attributes

A variety of business firms are moving towards the cloud after realizing the benefits and success of using cloud technology. With multiple cloud service providers having different features, selecting the best or ranking them becomes a tedious task. There are several multi-criteria decision-making models (MCDM) trying to handle the selection of the best cloud service provider problem. A significant amount of research focused on MCDM models using quality of service (QoS) characteristics, but not much research has been conducted to address the fuzziness inherent in data. Therefore, a suitable model for choosing suitable cloud service providers is proposed in this work to address this data fuzziness. The SMI attributes recommended by CSMIC, approved by ISO, are considered as the evaluation criteria for the evaluation of cloud service providers. In this research work, fuzzy DEA is used to compute the efficiency values of cloud service providers and modified Chen and Klein method is used to arrive at the final ranking of cloud service providers(CSPs).

A Decentralized Public Auditing Scheme for Secure Cloud Storage Based on Blockchain

The cloud storage service has brought great convenience to the customer, which can save massive storage and computation resources via outsourcing the data to cloud service provider (CSP). However, the security issues are the biggest challenge such as data integrity. The user can verify the integrity of outsourced data through a remote data auditing solution without retrieving original data from cloud, however, the auditing procedure has heavy computational overhead, which employs third party auditor (TPA) to conduct auditing task on behalf of users. In this paper, we propose a decentralized public auditing scheme for cloud storage based on blockchain, which removes TPA and increases the number of CSP, the auditing task was assigned to multiple CSPs, and the blockchain technology was used to record the audit process. Meanwhile, the structure of e-voting system is utilized to realize the audit result statistics of multiple CSPs via smart contract, which enhanced the credibility and stability of final auditing result. The theoretical analysis and experimental results demonstrate that proposed scheme is secure and efficient.

Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment

Multi-cloud is becoming a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud service providers to achieve high-quality services with lower operation cost and higher application resilience. In multi-cloud, cloud services are widely distributed at different locations with differentiated prices. Therefore, Web application providers face the challenge to select proper cloud services for application replication and deployment with the aim of minimizing the deployment cost. Meanwhile, the deployed application replicas must satisfy the constraint on request response time to maintain the quality of user experience. To meet the two major requirements, this article studies a new problem of Web application replication and deployment in multi-cloud (WARDMC) that jointly considers both the cost minimization and constraints on average response time, including particularly request processing time and network latency. To address the problem, we develop a new approach named MCApp. MCApp combines iterative mixed integer linear programming with domain-tailored large neighborhood search to optimize both application replicas deployment and user requests dispatching. Extensive experiments using the real-world datasets demonstrate that MCApp significantly outperforms several recently proposed approaches.

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%.

Security Analysis of “SMAKA: Secure Many-to-Many Authentication and Key Agreement Scheme for Vehicular Networks”

In the above article, Zhang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2021) proposed a many-to-many authentication and key agreement scheme named SMAKA for secure authentication and key agreement between multiple vehicles and cloud service providers (CSPs) in a vehicular network. However, we identify a security flaw in the design of Zhang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s scheme, which allows an attacker to hijack an authentication session between a vehicle and a CSP and impersonate the vehicle to establish a shared key with the CSP with a high chance.

Performance based Risk driven Trust (PRTrust): On modeling of secured service sharing in peer-to-peer federated cloud

Peer-to-peer (P2P) federated cloud system has emerged as a promising service delivery model wherein multiple cloud service providers (CSPs) collaborate and share resources and services among themselves to fulfill the spikes in demand from their cloud service consumers (CSCs). It facilitates CSPs to accomplish the committed service level agreements (SLAs) to their CSCs. However, the lack of preexisting trust relationships and unawareness about the cloud infrastructure and service delivery performance among CSPs in a distributed environment poses a risk to the quality of service (QoS) being delivered. We address this challenge by proposing a trust model, PRTrust, for a peer-to-peer federated cloud system that capitalizes the triangular relationship of performance, risk, and trust for the participating CSPs. The main contributions of this work are: (1) providing a logical design and architecture for trust and performance management in a peer-to-peer federated cloud system; (2) providing a two-tier weighted performance evaluation mechanism for CSPs; (3) providing a risk evaluation mechanism for CSPs based on their current performance level; (4) providing an improvised mechanism to evaluate and manage personalized reputation-based trust for the participating CSPs; (5) providing a CSP selection mechanism from a trusted list of CSPs, using the evaluated performance-based risk, for sharing the resources and services in a peer-to-peer federated cloud system. The proposed PRTrust model has shown better threat resilient behavior in dealing with malicious peer CSPs when compared with the reference EigenTrust model.

Formal Modelling and Automated Trade-off Analysis of Enforcement Architectures for Cryptographic Access Control in the Cloud

To facilitate the adoption of cloud by organizations, Cryptographic Access Control (CAC) is the obvious solution to control data sharing among users while preventing partially trusted Cloud Service Providers (CSP) from accessing sensitive data. Indeed, several CAC schemes have been proposed in the literature. Despite their differences, available solutions are based on a common set of entities—e.g., a data storage service or a proxy mediating the access of users to encrypted data—that operate in different (security) domains—e.g., on-premise or the CSP. However, the majority of these CAC schemes assumes a fixed assignment of entities to domains; this has security and usability implications that are not made explicit and can make inappropriate the use of a CAC scheme in certain scenarios with specific trust assumptions and requirements. For instance, assuming that the proxy runs at the premises of the organization avoids the vendor lock-in effect but may give rise to other security concerns (e.g., malicious insiders attackers). To the best of our knowledge, no previous work considers how to select the best possible architecture (i.e., the assignment of entities to domains) to deploy a CAC scheme for the trust assumptions and requirements of a given scenario. In this article, we propose a methodology to assist administrators in exploring different architectures for the enforcement of CAC schemes in a given scenario. We do this by identifying the possible architectures underlying the CAC schemes available in the literature and formalizing them in simple set theory. This allows us to reduce the problem of selecting the most suitable architectures satisfying a heterogeneous set of trust assumptions and requirements arising from the considered scenario to a decidable Multi-objective Combinatorial Optimization Problem (MOCOP) for which state-of-the-art solvers can be invoked. Finally, we show how we use the capability of solving the MOCOP to build a prototype tool assisting administrators to preliminarily perform a “What-if” analysis to explore the trade-offs among the various architectures and then use available standards and tools (such as TOSCA and Cloudify) for automated deployment in multiple CSPs.

BGSA: Broker Guided Service Allocation in Federated Cloud

Cloud brokering has emerged a new service paradigm because of the proliferation of a large number of cloud service providers. Cloud brokering attracts users because it aggregates and extends computing services from multiple cloud service providers. Service providers offer services with varying capabilities, technologies and pricing models which creates a heterogeneous service environment. Cloud users are also increasing day by day with varied requirements. Users prefer services from multiple service providers in order to fulfill their money and application requirements. Therefore, service allocation becomes one of most challenging task to fulfill all the requirements of users within resource constraints. Service allocation problem in multiple cloud environment has been studied as combinatorial optimization problem. Broker guided service allocation increases application resilience, provides unified billing and simplifies service management. In this paper we have proposed a broker guided service allocation (BGSA) model for Federated Cloud. BGSA employs a genetic algorithm based technique for service allocation. BGSA optimizes service allocation and derives well distributed set of non-dominated solutions. Simulation based evaluation of BGSA has been done and obtained results are compared with other two multi-objective algorithms. Observed results improve both execution time and total utilization.

A Study on Federated Cloud Computing Environment

Federated cloud computing is the advancement in the area of the general cloud computing paradigm. In a federated cloud environment, multiple cloud service providers share their computing assets, servers, and various facilities to fulfill customer demands. Federated cloud computing terminology consists of the aggregation of services considered by interoperability characteristics and creates the integration of several cloud service providers regardless of any geographical location. It improves the performance, utilization of facilities, minimizes response time and pricing model by partial subcontracting various computing resources and facilities from the nearby cost-efficient province. Customers also get profited from service level agreements signed between the cloud service providers through intermediator cloud brokers. This work aims to survey the federated cloud environment, its various architectural types, advantages associated with the federation, challenges associated with a federated cloud environment, and future research directions in the federated cloud computing research area.

User-centric framework to facilitate trust worthy cloud service provider selection based on fuzzy inference system

The widespread adoption of cloud computing by several companies across diverse verticals of different sizes has led to an exponential growth of Cloud Service Providers (CSP). Multiple CSPs offer homogeneous services with a vast array of options and different pricing policies, making the suitable service selection process complex. Our proposed model simplifies the IaaS selection process that can be used by all users including clients from the non-IT background. In the first phase, requirements are gathered using a simple questionnaire and are mapped with the compute services among different alternatives.In the second phase, we have implemented the Sugeno Fuzzy inference system to rank the service providers based on the QoS attributes to ascertain the appropriate selection. In the third phase, we have applied the cost model to identify the optimal CSP. This framework is validated by applying it for a gaming application use case and it has outperformed the online tools thus making it an exemplary model.

Slicing, Tokenization, and Encryption Based Combinational Approach to Protect Data-at-Rest in Cloud Using TF-Sec Model

Data in the Cloud, which applies to data as a cloud service provider (CSP), transmits stores, or manages it. The company will enforce the same definition of data usage while the data is resident within the enterprise and thus extend the required cryptographic security criteria to data collected, exchanged, or handled by CSP. The CSP Service Level Agreements cannot override the cryptographic access measures. When the data is transferred securely to CSP, it can be securely collected, distributed, and interpreted. Data at the rest position applies to data as it is processed internally in organized and in the unstructured ways like databases and file cabinets. The Data at the Rest example includes the use of cryptography for preserving the integrity of valuable data when processed. For cloud services, computing takes multiple forms from recording units, repositories, and many unstructured items. This paper presents a secure model for Data at rest. The TF-Sec model suggested is planned for use with Slicing, Tokenization, and Encryption. The model encrypts the given cloud data using AES 256 encryption, and then the encrypted block is sliced into the chunks of data fragments using HD-Slicer. Then it applies tokenization algorithm TKNZ to each chunk of data, applies erasure coding technique to tokens, applies the data dispersion technique to scramble encrypted data fragments, and allocates to storage nodes of the multiple CSP. In taking the above steps, this study aims to resolve the cloud security problems found and to guarantee the confidentiality of their data to cloud users due to encryption of data fragments would be of little benefit to a CSP.

A delegation model for SDN-driven federated cloud

The federated cloud paradigm is getting popular for its dynamic allocation of resources, processing power, improved performance, and scalability. A federated cloud lets multiple cloud service providers collaborate and delegate services. However, this delegation requires pre-approval from domain administrators which can create an administrative bottleneck and cause serious delays in times of need. Despite the recent advancements in the field, the federated cloud environment still lacks an ad-hoc delegation method that allows its users to delegate their rights at runtime. Software Defined Network (SDN) provides an opportunity to enhance the performance of federated cloud in terms of delegating network services among different domains. This paper explores various characteristics of the SDN-driven federated cloud and proposes a delegation framework enhancing different important dimensions. Further, a fine-grained delegation model is developed that supports delegation within different cloud environments constituting the federated cloud. A CPN based simulation of the model is provided to rectify any ambiguities in its practical use. The model is also compared with existing models to emphasize its significance. The results reflect that various dynamics of the SDN-driven federated cloud can be efficiently addressed by the proposed model.