Multiple Cloud Platforms Research Articles

Streamlining budgeting and forecasting across multi-cloud environments with dynamic financial models

This review paper explores the complexities of financial management in multi-cloud environments, emphasizing the need for streamlined budgeting and forecasting processes. It begins by addressing the challenges inherent in managing finances across multiple cloud platforms, such as integration difficulties, data silos, and cost management issues. The paper then provides an overview of dynamic financial models, detailing their components and benefits in overcoming these challenges. It highlights the role of automation, real-time data utilization, and predictive analytics in enhancing financial accuracy and efficiency. Furthermore, the paper discusses the importance of collaborative platforms in fostering stakeholder communication and coordination. Finally, strategic planning, best practices for multi-cloud financial management, and emerging trends and prospects are outlined. The review underscores the significance of aligning financial models with organizational goals and leveraging advanced technologies to achieve optimal financial outcomes in multi-cloud settings. Keywords: Multi-Cloud Financial Management, Budgeting and Forecasting, Dynamic Financial Models, Automation in Finance, Predictive Analytics.

Multi-Level Encryption System using AES and RSA Algorithms

Abstract: As the volume of sensitive data entrusted to the cloud explodes, from healthcare records to financial transactions, the need for robust security solutions intensifies. Traditional encryption techniques, while effective, remain vulnerable to key compromise, leaving data exposed to malicious factors. This study proposes a multi-level encryption strategy that leverages the power of AES for efficient data encryption, RSA for secure key protection with public-key cryptography, and role-based encryption for granular access control. This layered approach significantly increases the computational complexity for unauthorized access, making brute-force or dictionary attacks exponentially more difficult. Our research demonstrates successful integration of these algorithms, achieving a 50% reduction in key compromise risk and enhanced data integrity through MAC verification. Moving forward, we envision seamless integration with multiple cloud platforms and exploring advanced access control mechanisms based on user context and behavior. This multi-level encryption strategy holds immense potential to reshape the landscape of cloud data security, fostering a more secure and trustworthy digital future.

Data Transmission Security on Cloud Computing using Algorithm

Cloud computing enables businesses to efficiently store vast amounts of data in the cloud, which can be accessed seamlessly over the Internet without hardware compatibility constraints. However, the transmission of data over the Internet is susceptible to various security threats such as man-in-the-middle attacks, known plaintext attacks, chosen ciphertext attacks, related-key attacks, and pollution attacks. Uploading data to a single cloud service may heighten the risk of compromising confidential information. Prior research has explored several cryptography techniques like SA-EDS, RFDA, and EST to secure data storage across multiple cloud platforms. However, these existing methods exhibit vulnerabilities to various attacks. This article focuses on addressing data security challenges in multi-cloud environments and proposes an advanced approach called Proficient Security over Distributed Storage. PSDS has undergone rigorous testing against multiple attacks, demonstrating its resilience against related-key attacks, pollution attacks, chosen ciphertext attacks, and known plaintext attacks. Additionally, PSDS exhibits reduced computational time compared to existing encryption methods such as STTN and RFD.

Reconnoitering the significance of security using multiple cloud environments for conveyance applications with blowfish algorithm

In recent years the process of transportation needs a highly effective traffic system in order to monitor all consumer goods as many goods are left out at different locations. To handle such moving cases cloud platform is highly helpful as with respect to geographical location the goods are mapped in correct form. However incorporation of single cloud platform does not provide sufficient amount of storage about all goods thus a multiple cloud platform is introduced in proposed system. As multiple cloud platform is provided the security features of each data base system is also checked and enhanced using encryption keys. Moreover for proper operating conditions of multiple cloud platforms an analytical model is designed that synchronizes necessary data at end system. The defined analytical model focuses on solving multiple objectives that are related to critical energy problems where demand problems are reduced. Further the encryption process is carried out using Improved BlowFish Algorithm (IBFA) by allocating proper resources with decryption keys. To validate the effectiveness of proposed method five scenarios are considered where all scenario outcomes proves to be much higher than existing models by an average of 43%.

OFP-TM: an online VM failure prediction and tolerance model towards high availability of cloud computing environments.

The indispensable collaboration of cloud computing in every digital service has raised its resource usage exponentially. The ever-growing demand of cloud resources evades service availability leading to critical challenges such as cloud outages, SLA violation, and excessive power consumption. Previous approaches have addressed this problem by utilizing multiple cloud platforms or running multiple replicas of a Virtual Machine (VM) resulting into high operational cost. This paper has addressed this alarming problem from a different perspective by proposing a novel mathbb {O}nline virtual machine mathbb {F}ailure mathbb {P}rediction and mathbb {T}olerance mathbb {M}odel (OFP-TM) with high availability awareness embedded in physical machines as well as virtual machines. The failure-prone VMs are estimated in real-time based on their future resource usage by developing an ensemble approach-based resource predictor. These VMs are assigned to a failure tolerance unit comprising of a resource provision matrix and Selection Box (S-Box) mechanism which triggers the migration of failure-prone VMs and handle any outage beforehand while maintaining the desired level of availability for cloud users. The proposed model is evaluated and compared against existing related approaches by simulating cloud environment and executing several experiments using a real-world workload Google Cluster dataset. Consequently, it has been concluded that OFP-TM improves availability and scales down the number of live VM migrations up to 33.5% and 83.3%, respectively, over without OFP-TM.

Swarm: A federated cloud framework for large-scale variant analysis.

Genomic data analysis across multiple cloud platforms is an ongoing challenge, especially when large amounts of data are involved. Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms. We demonstrate its utility via common inquiries of genomic variants across BigQuery in the Google Cloud Platform (GCP), Athena in the Amazon Web Services (AWS), Apache Presto and MySQL. Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.

Hummingbird: efficient performance prediction for executing genomic applications in the cloud.

A major drawback of executing genomic applications on cloud computing facilities is the lack of tools to predict which instance type is the most appropriate, often resulting in an over- or under- matching of resources. Determining the right configuration before actually running the applications will save money and time. Here, we introduce Hummingbird, a tool for predicting performance of computing instances with varying memory and CPU on multiple cloud platforms. Our experiments on three major genomic data pipelines, including GATK HaplotypeCaller, GATK Mutect2 and ENCODE ATAC-seq, showed that Hummingbird was able to address applications in command line specified in JSON format or workflow description language (WDL) format, and accurately predicted the fastest, the cheapest and the most cost-efficient compute instances in an economic manner. Hummingbird is available as an open source tool at: https://github.com/StanfordBioinformatics/Hummingbird. Supplementary data are available at Bioinformatics online.

Deployment of Elastic Virtual Hybrid Clusters Across Cloud Sites

Virtual clusters are widely used computing platforms than can be deployed in multiple cloud platforms. The ability to dynamically grow and shrink the number of nodes has paved the way for customised elastic computing both for High Performance Computing and High Throughput Computing workloads. However, elasticity is typically restricted to a single cloud site, thus hindering the ability to provision computational resources from multiple geographically distributed cloud sites. To this aim, this paper introduces an architecture of open-source components that coherently deploy a virtual elastic cluster across multiple cloud sites to perform large-scale computing. These hybrid virtual elastic clusters are automatically deployed and configured using an Infrastructure as Code (IaC) approach on a distributed hybrid testbed that spans different organizations, including on-premises and public clouds, supporting automated tunneling of communications across the cluster nodes with advanced VPN topologies. The results indicate that cluster-based computing of embarrassingly parallel jobs can benefit from hybrid virtual clusters that aggregate computing resources from multiple cloud back-ends and bring them together into a dedicated, albeit virtual network. The work presented in this article has been partially funded by the European Union's (EU) Horizon 2020 research project DEEP Hybrid-DataCloud (grant agreement No 777435).

Artificial Intelligence on Information Services

Artificial Intelligence as a Service (AIaaS) is the outsourcing of an external artificial intelligence (AI) company. AI as a service allows people and companies to try AI for a variety of purposes without much initial investment and little risk. The test could allow for the sampling of multiple public cloud platforms to explore different ways of machine learning. Different AI provider platforms offer a wide range of machine learning and AI styles. This diversity can best fit the organizational needs of AI because organizations need to evaluate features and price to see what works for you. Cloud AI service providers can provide the special hardware needed for other AI tasks, such as GPU-based processing of deep workloads. Buying the hardware and software needed to launch an AI cloud is expensive. Combined with labor and repair costs, as well as hardware changes for different operations, it makes AIaaS costs deter many organizations. AI cloud computing including Amazon Machine Learning, Microsoft Cognitive Services and Google Cloud Machine Learning can help potential organizations with their data. Having the opportunity to try out algorithms and the services of various providers can allow businesses to find out what works and allow measurement before committing. When something measuring is found on the scale, the resources of these major providers are available to back up your computer-assisted scale.

NanoSPC: a scalable, portable, cloud compatible viral nanopore metagenomic data processing pipeline.

Metagenomic sequencing combined with Oxford Nanopore Technology has the potential to become a point-of-care test for infectious disease in public health and clinical settings, providing rapid diagnosis of infection, guiding individual patient management and treatment strategies, and informing infection prevention and control practices. However, publicly available, streamlined, and reproducible pipelines for analyzing Nanopore metagenomic sequencing data are still lacking. Here we introduce NanoSPC, a scalable, portable and cloud compatible pipeline for analyzing Nanopore sequencing data. NanoSPC can identify potentially pathogenic viruses and bacteria simultaneously to provide comprehensive characterization of individual samples. The pipeline can also detect single nucleotide variants and assemble high quality complete consensus genome sequences, permitting high-resolution inference of transmission. We implement NanoSPC using Nextflow manager within Docker images to allow reproducibility and portability of the analysis. Moreover, we deploy NanoSPC to our scalable pathogen pipeline platform, enabling elastic computing for high throughput Nanopore data on HPC cluster as well as multiple cloud platforms, such as Google Cloud, Amazon Elastic Computing Cloud, Microsoft Azure and OpenStack. Users could either access our web interface (https://nanospc.mmmoxford.uk) to run cloud-based analysis, monitor process, and visualize results, as well as download Docker images and run command line to analyse data locally.

On automated cloud bursting and hybrid cloud setups using Apache Mesos

SummaryHybrid cloud technology is becoming increasingly popular as it merges private and public clouds to bring the best of two worlds together. However, due to the heterogeneous cloud installation, facilitating a hybrid cloud setup is not simple. Despite some commercial solutions being available to build a hybrid cloud, an open‐source implementation is still unavailable. In this paper, we try to bridge the gap by providing an open‐source implementation using the power of Apache Mesos. We build a hybrid cloud on top of multiple cloud platforms, private and public. Through comprehensive experimental results, we show that our solution is able to ensure resource bursting by leveraging the power of the public cloud.

Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

In the age of big data, service recommendation has provided an effective manner to filter valuable information from massive data. Generally, by observing the past service invocation records (Boolean values) distributed across different cloud platforms, a recommender system can infer personalized preferences of a user and recommend him/her new services to gain more profits. However, the historical service invocation records are a kind of private information for users. Therefore, how to protect sensitive user data distributed across multiple cloud platforms is becoming a necessity for successful service recommendations. Additionally, the historical service invocation records often update with time, which call for an efficient and scalable service recommendation method. In view of these challenges, we introduce the multi-probe Simhash technique in information retrieval domain into the recommendation process and further put forward a privacy-preserving recommendation method based on historical service invocation records. At last, we design several experiments on the real-world service quality data in set WS-DREAM. Experimental results show the feasibility of the proposal in terms of producing accurate recommended results while protecting users’ private information contained in historical service invocation records.

Adaptive management of applications across multiple clouds: The SeaClouds Approach


 
 
 How to deploy and manage, in an efficient and adaptive way, complex applications across multiple heterogeneous cloud platforms is one of the problems that have emerged with the cloud revolution. In this paper we present context, motivations and objectives of the EU research project SeaClouds, which aims at enabling a seamless adaptive multi-cloud management of complex applications by supporting the distribution, monitoring and migration of application modules over multiple heterogeneous cloud platforms. After positioning SeaClouds with respect to related cloud initiatives, we present the SeaClouds architecture and discuss some of its aspect, such as the use of the OASIS standard TOSCA and the compatibility with the OASIS CAMP initiative.
 
 

CloudMF

While the number of cloud solutions is continuously increasing, the development and operation of large-scale and distributed cloud applications are still challenging. A major challenge is the lack of interoperability between the existing cloud solutions, which increases the complexity of maintaining and evolving complex applications potentially deployed across multiple cloud infrastructures and platforms. In this article, we show how the Cloud Modelling Framework leverages model-driven engineering and supports the DevOps ideas to tame this complexity by providing: (i) a domain-specific language for specifying the provisioning and deployment of multi-cloud applications, and (ii) a models@run-time environment for their continuous provisioning, deployment, and adaptation.

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

To maximize the economic benefits, a cloud service provider needs to recommend its services to as many users as possible based on the historical user-service quality data. However, when a cloud platform (e.g., Amazon) intends to make a service recommendation decision, considering only its own user-service quality data is insufficient, because a cloud user may invoke services from multiple distributed cloud platforms (e.g., Amazon and IBM). In this situation, it is promising for Amazon to collaborate with other cloud platforms (e.g., IBM) to utilize the integrated data for the service recommendation to improve the recommendation accuracy. However, two challenges are present in the above-mentioned collaboration process, where we attempt to use multi-source data for the service recommendation. First, protecting users’ privacy is challenging when IBM releases its own data to Amazon. Second, the recommendation efficiency and scalability are often low when the user-service quality data of Amazon and IBM update frequently. Considering these challenges, a privacy-preserving and scalable service recommendation approach based on distributed locality-sensitive hashing, i.e., $\textit {SerRec}_{\textit {distri-LSH}}$ , is proposed in this paper to handle the service recommendation in a distributed cloud environment. Extensive experiments on the WS-DREAM data set validate the feasibility of our approach in terms of service recommendation accuracy, scalability, and privacy preservation.

An empirical study for evaluating the performance of multi-cloud APIs

The massive use of cloud APIs for workload orchestration and the increased adoption of multiple cloud platforms prompted the rise of multi-cloud APIs. Multi-cloud APIs abstract cloud differences and provide a single interface regardless of the target cloud platform. Identifying whether the performance of multi-cloud APIs differs significantly from platform-specific APIs is central for driving technological decisions on cloud applications that require maximum performance when using multiple clouds. This study aims to evaluate the performance of multi-cloud APIs when compared to platform-specific APIs. We carried out three rigorous quasi-experiments to measure the performance (dependent variable) of cloud APIs (independent variable) regarding CPU time, memory consumption and response time. jclouds and Libcloud were the two multi-cloud APIs used (experimental treatment). Their performance were compared to platform-specific APIs (control treatment) provided by Amazon Web Services and Microsoft Azure. These APIs were used for uploading and downloading (tasks) 39 722 files in five different sizes to/from storage services during five days (trials). Whereas jclouds performed significantly worse than platform-specific APIs for all performance indicators on both cloud platforms and operations for all five file sizes, Libcloud outperformed platform-specific APIs in most tests (p-value not exceeding 0.00125, A-statistic greater than 0.64). Once confirmed by independent replications, our results suggest that jclouds developers should review the API design to ensure minimal overhead whereas jclouds users should evaluate the extent to which this trade-off affect the performance of their applications. Multi-cloud users should carefully evaluate what quality attribute is more important when selecting a cloud API.

A Digital Forensic Framework for Cloud Based on VMI

Traditional digital forensic methods become exceedingly feeble in cloud due to the fact that the basic infrastructure turns to virtualized environment. Leveraging properties of virtualization, virtual machine introspection has showed potential for cloud forensic. In this paper, we propose a novel framework, which contains a trustworthy independent agency, to provide memory digital forensic analysis (DFA) for virtual machines (VMs) in cloud. With the assistance of cloud platform, memory dump files can be obtained and transferred to the agency for further DFA, where information of VMs can be extracted using the proper tools. We described the constructional design of the framework. Test results showed the versatility of DFA and ability of malware detection. The framework indicates DFA can be purchased as a service only when there is a need in order to reduce the expenditures on maintaining exclusive facility and furnish standard procedure to multiple cloud platforms.

Cloud computing and big data: Technologies and applications

Cloud computing and big data: Technologies and applications

Intercrossed Access Controls for Secure Financial Services on Multimedia Big Data in Cloud Systems

The dramatically growing demand of Cyber Physical and Social Computing (CPSC) has enabled a variety of novel channels to reach services in the financial industry. Combining cloud systems with multimedia big data is a novel approach for Financial Service Institutions (FSIs) to diversify service offerings in an efficient manner. However, the security issue is still a great issue in which the service availability often conflicts with the security constraints when the service media channels are varied. This paper focuses on this problem and proposes a novel approach using the Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services on multimedia big data in cloud computing. The proposed approach is entitled IntercroSsed Secure Big Multimedia Model (2SBM), which is designed to secure accesses between various media through the multiple cloud platforms. The main algorithms supporting the proposed model include the Ontology-Based Access Recognition (OBAR) Algorithm and the Semantic Information Matching (SIM) Algorithm . We implement an experimental evaluation to prove the correctness and adoptability of our proposed scheme.

A service-oriented framework for developing cross cloud migratable software

Whilst cloud computing has burst into the current scene as a technology that allows companies to access high computing rates at limited costs, cloud vendors have rushed to provide tools that allow developers to build software for their cloud platforms. The software developed with these tools is often tightly coupled to their services and restrictions. Consequently vendor lock in becomes a common problem which multiple cloud users have to tackle in order to exploit the full potential of cloud computing. A scenario where component-based applications are developed for being deployed across several clouds, and each component can independently be deployed in one cloud or another, remains fictitious due to the complexity and the cost of their development. This paper presents a cloud development framework for developing cloud agnostic applications that may be deployed indifferently across multiple cloud platforms. Information about cloud deployment and cloud integration is separated from the source code and managed by the framework. Interoperability between interdependent components deployed in different clouds is achieved by automatically generating services and service clients. This allows software developers to segment their applications into different modules that can easily be deployed and redistributed across heterogeneous cloud platforms.