Multiple Cloud Providers Research Articles

Transforming business scalability and operational flexibility with advanced cloud computing technologies

In the rapidly evolving landscape of digital transformation, cloud computing has emerged as a pivotal technology enabling businesses to achieve unparalleled scalability and operational flexibility. This review explores the transformative impact of advanced cloud computing technologies on business operations, highlighting key innovations and their implications for organizational growth and efficiency. Cloud computing offers a dynamic and scalable environment where resources can be provisioned and managed on-demand, allowing businesses to respond swiftly to changing market conditions and customer demands. By leveraging cloud infrastructure, companies can scale their operations seamlessly without the need for significant upfront investments in physical hardware. This flexibility not only reduces capital expenditure but also enhances the ability to innovate and adapt in a competitive marketplace. Advanced cloud technologies, such as multi-cloud and hybrid cloud solutions, further augment operational flexibility by enabling organizations to optimize their IT environments. Multi-cloud strategies allow businesses to distribute workloads across multiple cloud providers, mitigating the risks associated with vendor lock-in and ensuring high availability and redundancy. Hybrid cloud solutions, which integrate on-premises infrastructure with public and private clouds, provide a balanced approach to managing sensitive data and workloads while benefiting from the scalability of the cloud. Moreover, cloud-native technologies like containerization and serverless computing have revolutionized application development and deployment. Containers encapsulate applications and their dependencies, ensuring consistency across different computing environments and facilitating rapid deployment. Serverless computing reviews the underlying infrastructure, allowing developers to focus solely on code, thus accelerating the development cycle and reducing operational overhead. The integration of advanced analytics and artificial intelligence (AI) with cloud computing further enhances business capabilities. Cloud platforms offer robust analytics tools and AI services that can process vast amounts of data in real-time, providing actionable insights and enabling predictive decision-making. This integration empowers businesses to optimize operations, improve customer experiences, and drive strategic initiatives with data-driven precision. In conclusion, advanced cloud computing technologies are instrumental in transforming business scalability and operational flexibility. By harnessing the power of the cloud, organizations can achieve greater agility, cost efficiency, and innovation, positioning themselves for sustained growth and competitive advantage in the digital age. As cloud technologies continue to evolve, their potential to redefine business operations and drive economic value will only expand, making cloud adoption a critical imperative for modern enterprises. Keywords: Operational Flexibility, Transforming, Business Scalability, Advanced, Cloud Computing Technologies.

QFaaS: A Serverless Function-as-a-Service framework for Quantum computing

Quantum computing is rapidly reaching a point in which its application design and engineering aspects must be seriously considered. However, quantum software engineering is still in its infancy, with numerous challenges, especially in dealing with the diversity of quantum programming languages and noisy intermediate-scale quantum (NISQ) systems. To alleviate these challenges, we propose QFaaS, a holistic Quantum Function-as-a-Service framework, which leverages the advantages of the serverless model, DevOps lifecycle, and the state-of-the-art software techniques to advance practical quantum computing for next-generation application development in the NISQ era. Our framework provides essential elements of a serverless quantum system to streamline service-oriented quantum application development in cloud environments, such as combining hybrid quantum–classical computation, automating the backend selection, cold start mitigation, and adapting DevOps techniques. QFaaS offers a full-stack and unified quantum serverless platform by integrating multiple well-known quantum software development kits (Qiskit, Q#, Cirq, and Braket), quantum simulators, and cloud providers (IBM Quantum and Amazon Braket). This paper proposes the concept of quantum function-as-a-service, system design, operation workflows, implementation of QFaaS, and lessons learned on the benefits and limitations of quantum serverless computing. We also present practical use cases with various quantum applications on today’s quantum computers and simulators to demonstrate our framework capability to facilitate the ongoing quantum software transition.

RAaaS: Resource Allocation as a Service in multiple cloud providers

Cloud users require a set of specific computing needs for their applications, while cloud providers offer a variety of computing products and services on the Internet. These two cloud players make deals through the use of service level agreements (SLAs) where, for instance, prices and levels of quality of service (QoS) are defined. From the cloud user’s point of view, building a robust set of SLAs becomes a challenging problem when multiple cloud providers are present in the market. The allocation of resources in the cloud to run complex applications with guaranteed reliable, secure and acceptable response times is not an easy task, and this paper aims to tackle this problem. This work describes a resource allocation service that aims to optimize the user’s request of cloud resources (virtual machines — VMs) onto multiple Infrastructure-as-a-Service (IaaS) cloud providers. The Resource-Allocation-as-a-Service (RAaaS) proposed in this paper works as a standalone service between cloud users and cloud providers, and it relies on three different requirements: reliability, processing, and mutual trust. The proposed resource allocation service is carried out using the three very common types of VM billing models: on-demand, reserved and spot, where the spot cost model is employed to furnish low-cost resources for the application allocation to improve its reliability. The contributions of this paper are threefold: (i) a three-dimension SLA encompassing reliability, processing, and trust; (ii) an integer linear program (ILP) to schedule cloud-based VMs to applications considering the three-dimension SLA model, and (iii) a heuristic algorithm to mitigate possible QoS violations. Experimental results show that the proposed RAaaS procedure is capable of optimizing resource allocation considering multiple criteria in the SLA while mitigating the extra costs introduced by mutual trust between customers using redundant spot instances allocation.

An efficient and secure identity-based integrity auditing scheme for sensitive data with anti-replacement attack on multi-cloud storage

The rapid growth of cloud storage brings forth significant difficulties in ensuring the integrity of data owners' sensitive data. Multi-cloud storage systems, which motive to enhance trustworthiness, are vulnerable to rigorous replacement attacks. These insidious attacks involve malicious actors substituting legitimate data with counterfeit versions, leading to compromised integrity and fraudulent audit outcomes. We propose a novel identity-based data integrity auditing protocol tailored for multi-cloud storage environments to tackle this challenge. Our protocol leverages a Third-Party Auditor (TPA) to delegate data authenticity and auditing responsibilities, employing identity-based blind signatures with a robust data recovery scheme. By eliminating the need for cumbersome public key certificate management, our solution effectively protects data owners' sensitive data while ensuring confidentiality across multiple cloud providers, thereby mitigating the risk of replacement attacks. A thorough security examination shows the proposed system resists forging, replacement attacks, and data confidentiality violations. A rigorous performance experiment also reveals outstanding computational and communication efficiency that outperforms current methods in tag production, proof generation, and verification. Our protocol offers an extraordinary balance of security and efficiency, making it particularly useful for auditing patent-sensitive data in multi-cloud storage. Our proposal provides a timely and effective way to protect sensitive data and prevent attackers in multi-cloud storage settings at a time when replacement attacks pose a serious concern.

An online service provisioning strategy for container-based cloud brokers

Container-based cloud brokers sit between multiple cloud providers and users, renting virtual machines (VMs) from multiple cloud providers, provisioning services to users, and executing jobs in the form of containers. Current cloud providers offer two billing models for Infrastructure-as-a-Service (IaaS), including on-demand and reserved. Given the huge uncertainty of user requests, cloud brokers can combine two billing options to rent VMs to reduce costs while gaining the flexibility to handle short-term fluctuations in requests. Optimal scheduling and auto-scaling strategies depend on information about user requests over a long period of time in the future, which is often difficult to predict accurately. To solve this problem, this work proposes an online service provisioning strategy to optimize the cost of cloud brokers to satisfy user requests without requiring any future information. Our proposed strategy consists of an online container scheduling algorithm and an online reserved VM pool auto-scaling algorithm. It is shown theoretically that the two proposed online algorithms can achieve guaranteed competitive ratios of n+1n−1TT−3(μ+1) and 2−1T respectively. Eventually, the effectiveness of our proposed algorithms is validated with both simulated and multiple real user request datasets. Our proposed strategy can significantly reduce the cost of cloud brokers compared to several benchmarks.

CloudPilot: Flow acceleration in the cloud

TCP-split proxies have been previously studied as an efficient mechanism to improve the rate of connections with large round trip times. These works focused on improving a single flow. In this paper, we investigate how strategically deploying TCP-split proxies in the cloud can improve the performance of geo-distributed applications entailing multiple flows interconnecting globally-distributed sources and destinations using different communication patterns, and being subject to budget limitations.We present CloudPilot, a Kubernetes-based system that measures communication parameters across different cloud regions, and uses these measurements to deploy cloud proxies in optimized locations on multiple cloud providers. To this end, we model cloud proxy acceleration and define a novel cloud-proxy placement problem. Since this problem is NP-Hard, we suggest a few efficient heuristics to solve it. Finally, we find that our cloud-proxy optimization can improve flow completion time by an average of 3.6× in four different use cases.

A multi-objective cloud energy optimizer algorithm for federated environments

Energy is a considerable portion of the cost of a cloud data center. In addition to energy, carbon emission tax imposes another cost on data centers, which have different policies in different cities around the world. Most of the cloud data centers are geographically distributed, which can have a huge advantage in reducing such costs. In addition, in recent years, cloud federation, in which multiple cloud providers share their IT infrastructures voluntarily, could play a crucial role in minimizing the energy consumption of cloud data centers. On the other hand, improper VM placement results frequent VM migration, constant turning off/on physical machines, service quality degradation and energy consumption increase. To tackle these weaknesses, we propose a multi-objective algorithm, Called OUR-ACS, to minimize data centers' energy consumption, carbon emission, and the total expenses (energy cost + carbon tax) in a federated environment considering both initial VM placement and VM consolidation approaches. We evaluate the efficiency of our proposed algorithm by using the ClousSim Plus simulator toolkit using real-world datasets. Compared to the competing algorithms, our simulation results indicate that our proposed algorithm could reduce the total energy consumption, carbon emission, and the total costs of multiple cloud data centers by an average of 37.6%, 41%, and 25%, respectively, in a federated environment.

Towards cost-aware VM migration to maximize the profit in federated clouds

Federated cloud has emerged as a cost-effective and scalable platform for multiple cloud providers to collaborate in serving the growing demand for cloud resources. In a federation, the major concern for each cloud provider is to maximize its own profit while meeting the quality of service requirements of end-users. Virtual machine (VM) migration allows the cloud provider to rent resources across the other cloud providers in a federation to minimize the cost. However, VM migration comes with an additional penalty in terms of time and cost, and it might degrade the performance of the system during migration.We address the problem of cost-aware VM migration in federated clouds to maximize the profit of the cloud provider by minimizing the cost. We analyze the impact of VM migration across the data centers in a federation by modeling migration cost, migration time, and downtime. We formulate an optimization problem to minimize the total cost of operation (TCO) of the cloud provider as well as the migration time. We model the TCO to include the cost of hosted VMs and their migration cost. We propose efficient heuristics to select the source and destination data centers for migration, as well as the VM to be migrated. Based on these heuristics, we design a polynomial-time VM migration algorithm to minimize the TCO and migration time. The proposed algorithm considers the characteristics of the VMs, the bandwidth and the data transfer price across data centers to give the highest benefit in migration. We evaluate the proposed algorithm using CloudSim across different scenarios. Our results demonstrate the advantages of VM migration in a federated cloud for both the cloud provider and the end-user, to lower the TCO and the migration downtime. The proposed algorithm is shown to outperform other baseline approaches, with up to 48% reduction in the TCO along with reducing the migration time, downtime, and number of migrations. We also evaluate the impact of network bandwidth and the page dirty rate of VMs on migration performance.

A Many-to-Many Demand and Response Hybrid Game Method for Cloud Environments

In this article, we design a service mechanism for profits optimization between multiple cloud providers and multiple cloud customers ( <i>many-to-many</i> ). We explore this problem from the perspective of game theory but take a different approach compared with existing cloud resource pricing game methods. First, we regard the relationships among multiple cloud customers as an evolutionary game, and formulate the competitions among the multiple cloud providers as a noncooperative game. Eventually, we form a hybrid game model in which the strategy of each customer and each cloud provider is affected not only by the other side but also by customers or cloud providers other than themselves. Second, based on the hybrid game model, we simulate the bargaining process between cloud providers and customers by controlling supply and demand allocation, and try to ultimately achieve a balanced supply and demand state, i.e., a win-win situation. For each cloud customer and provider, we design a utility function. A customer’s utility involves net profits and the cloud providers’ bidding strategies, and a cloud provider’s utility involves net profits and the cloud customers’ demand strategies. Both sides attempt to maximize their own profits under the influences of each other. We prove that our proposed strategies enable each of the two games to converge to their own equilibrium. Finally, the strategies of cloud customers and providers can be implemented through an iterative proximal algorithm ( <inline-formula><tex-math notation="LaTeX">$\mathcal {IPA}$</tex-math></inline-formula> ) and a distributed iterative algorithm ( <inline-formula><tex-math notation="LaTeX">$\mathcal {DIA}$</tex-math></inline-formula> ). The experimental results validate our methods and show that the proposed method can benefit both multiple cloud providers and customers.

Raaas: Resource Allocation as a Service in Multiple Cloud Providers

Raaas: Resource Allocation as a Service in Multiple Cloud Providers

A General Performance and QoS Model for Distributed Software-Defined Environments

The landscape for cloud services and cyberinfrastructure offerings has increased drastically over the past few years. Initially, users moved their applications to the cloud to take advantage of a pay-per-usage model and on-demand access. However, as more cloud providers joined the market, users shifted their goals for using cloud computing from cost reduction to resilience, agility, and optimization. These goals can be achieved by dynamically combining services from multiple providers, for example, to avoid data center or cloud zone outages or to take advantage of extensive offerings with different price points. However, to efficiently support application deployment in this dynamic environment, new models and tools that can measure the application performance and the Quality of Service (QoS) of different configurations are required. The goal of this work is to evaluate the application performance and the QoS of a distributed Software-Defined Environment as well as calculate the QoS of alternative configurations from the underlying pool of services. In particular, we present a mathematical model and a tool for evaluating the performance and QoS of batch application workflows in a distributed environment. We experimentally evaluate the proposed model using a bioinformatics workflow running on infrastructure services from multiple cloud providers.

Minimizing the Cost for Resource Allocation from Multiple Cloud Providers

Abstract: Cloud computing provides streamlined tools for exceptional business efficiency. Cloud service providers typically offer two types of plans: reserved and on-demand. Restricted policies provide low-cost long-term contracting, while order contracts are expensive and ready for short periods. Cloud resources must be delivered wisely to meet current customer demands. Many current works rely on low-cost resource-reserved strategies, which may be under- or over-provisioning. Resource allocation has become a difficult issue due to unfairness causing high availability costs and cloud demand variability. That article suggests a hybrid approach to allocating cloud services to complex customer orders. The strategy was built in two stages: accommodation stages and a flexible structure. By treating each step as an optimization problem, we can reduce the overall implementation cost while maintaining service quality. Due to the uncertain nature of cloud requests, we set up a stochastic Optimization-based approach. Our technique is used to assign individual cloud resources and the results show its effectiveness. Keywords: Cloud computing, Resource allocation, Demand

Data correlation and fuzzy inference system-based data replication in federated cloud systems

Federated cloud is a promising solution for cloud providers that enables collaboration and leasing resources among multiple cloud providers. However, resource management in such system is very challenging, as each cloud provider must maintain its own economic profit while meeting the requirement of service level agreement (SLA). In this respect, we propose a dynamic and periodic data replication strategy in federated cloud systems. It aims to guarantee the monetary profit of a cloud provider while satisfying its users’ requirements in terms of response time and minimum availability. To identify replicas, we perform a periodical analysis of the users’ tasks using the spectral clustering technique to extract the existing correlations between remote data related to SLA violations. The adaptation of such correlations can significantly reduce data transfer amount and the time required to transfer it, thereby reducing tasks’ response time and the number of future SLA violations. Then, we rely on a fuzzy inference system to place the groups of replicas considering four main parameters to choose replicas placements among his owned or leased resources from other providers. Furthermore, a replicas number adjustment is performed when SLA is satisfied over time. To demonstrate the efficiency of our strategy, performance of the proposed strategy are compared alongside existing single-clouds-based and interconnected-clouds-based data replication strategies. The obtained results indicate that our strategy decreases the amount of SLA violations while preserving the monetary profit of providers.

Fitness-Aware Containerization Service Leveraging Machine Learning

Containerized deployment of microservices has gained immense traction across industries. To meet demand, traditional cloud providers offer container-as-a-service, where selection of the container and containerization of workloads remain developer’s responsibility. This task is arduous for a developer since the choice of containers across different cloud providers is many. Furthermore, there does not exist any mechanism using which one can compare and contrast the capabilities of containers across different providers. In this scenario, we envisage the need for a smart cloud broker that can automatically deploy a chosen IT service into the best-fit container environment mapped to performance requirements, from among the set of available underpinning brokered container hosting systems spread across multiple cloud providers. We propose a novel fitness-aware containerization-as-a-service to achieve this. We show why a best-fit container selection process is operationally complex and time consuming, and how we heuristically prune the associated decision tree in two phases so that it becomes viable to implement this as an on-demand service. We propose a new metric called fitness quotient ( <inline-formula><tex-math notation="LaTeX">$FQ$</tex-math></inline-formula> ) to evaluate containers obtained from heterogeneous providers. We leverage machine learning techniques to inject automation into these two phases: unsupervised K-Means clustering in the first-level build-time phase to accurately classify IaaS cost and performance data, and polynomial regression during the second-level provisioning-time phase to discover relationships between SaaS performance and container strength. We also show that the utility of the framework that we propose is not limited to the container fitness use case that we analyze in this paper; rather it can be generalized to address a class of problems where overall time and cost complexity for provisioning-time decision making needs to be controlled under a given set of constraints.

Sky Computing: The Advancement of Cloud Computing

When we think of the future of cloud computing and ask how the technology of cloud can evolve then there is a more coherent service, we call sky computing which comes into picture. Sometimes, a single cloud isn’t enough. Sometimes, you need the whole sky. That’s why a number of researchers are developing tools to federate clouds, an architectural concept dubbed “sky computing”. The Sky Computing model allows the creation of large-scale infrastructure using resources from multiple cloud providers. These infrastructures are able to run embarrassingly parallel computation with high performance. The big challenge here is managing the combined resources of available Clouds, public or private and interoperating between them without an excessive efficiency loss. There is the need for an effective model that can deliver a proficient deployment, possible with Sky Computing. In this paper I have talked about sky computing and differences between grid, cloud and sky computing and various mechanics that might lead to sky computing over multiple clouds, and I have also covered the architecture of sky computing and the characteristic of sky computing.

Charon: A Secure Cloud-of-Clouds System for Storing and Sharing Big Data

We present <small>Charon</small>, a cloud-backed storage system capable of storing and sharing big data in a secure, reliable, and efficient way using multiple cloud providers and storage repositories to comply with the legal requirements of sensitive personal data. <small>Charon</small> implements three distinguishing features: (1) it does not require trust on any single entity, (2) it does not require any client-managed server, and (3) it efficiently deals with large files over a set of geo-dispersed storage services. Besides that, we developed a novel Byzantine-resilient data-centric leasing protocol to avoid write-write conflicts between clients accessing shared repositories. We evaluate <small>Charon</small> using micro and application-based benchmarks simulating representative workflows from bioinformatics, a prominent big data domain. The results show that our unique design is not only feasible but also presents an end-to-end performance of up to <inline-formula><tex-math notation="LaTeX">$2.5\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:mn>5</mml:mn><mml:mo>&#x00D7;</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="cogo-ieq1-2916856.gif"/></alternatives></inline-formula> better than other cloud-backed solutions.

Mitigating Cyber-Attacks in Cloud Environments

Involvement of multiple cloud providers enhances the security complexity in cloud computing. Despite engaging best in class human and hardware resources, cyber-attacks in cloud paradigm continue to rise. This work aims to explore the cloud vulnerabilities that arise due to the multiple entry points. Underlying security threats are categorized into resources at providers' end, hardware security, transmission security, process security, and endpoint security. To mitigate the cyber-attacks in cloud, this work proposed a comprehensive multi-point-based framework that leverages the underlying hardware to strengthen the security at the user's end, internet service provider's end, and at the cloud data center. Security is further fortified by including the process level interaction at terminals. Framework is advanced enough to accommodate the vulnerable points of a system and a network. With the implementation of the proposed system, potential attacks can be detected during early state of penetration.

Model-Driven Elasticity Management with OCCI

Elasticity is considered as a fundamental feature of cloud computing where the system capacity can adjust to the current application workloads by provisioning or de-provisioning computing resources automatically and timely. Many studies have been already conducted to elasticity management systems, however, almost all lack to offer a complete modular solution. In this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDEMO</small> , a new elasticity management system powering both vertical and horizontal elasticities, both VM and Container virtualization technologies, multiple cloud providers simultaneously, and various elasticity policies. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDEMO</small> is characterized by the following features: it represents ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) the first system that manages elasticity using Open Cloud Computing Interface (OCCI) model with respect to the OCCI standard specifications, ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) the first unified system which combines the functionalities of the worldwide cloud providers: Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i> ) allows a dynamic configuration at runtime during the execution of the application. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDEMO</small> permits to timely adapt resource capacity according to the workload intensity and increase application performance without introducing a significant overhead.

A QoS-Based Splitting Strategy for a Resource Embedding Across Multiple Cloud Providers

In cloud computing, a fundamental management problem with the Infrastructure as a Service model lies in the efficient embedding of computing and networking resources onto distributed virtualized infrastructures. This issue, usually referred to as the Virtual Network Embedding (VNE) problem, has been well studied for a single Cloud Provider (CP). However, wide-area services delivery may require to embed heterogeneous resources over multiple CPs. This adds more complexity and scalability issues, since the Virtual Network Requests (VNRs) embedding process requires two phases of operation: the multicloud VNRs splitting, followed by the intracloud VNR segments mapping. This paper addresses the problem of VNE across multiple CPs by proposing a VNRs splitting strategy which aims at improving the performance and QoS of resulting VNR segments. An Integer Linear Program (ILP) is used to formalize the splitting phase as a maximization problem with constraints. Subsequently, in order to minimize the overall delay, a multi-objective intracloud resource mapping approach formalized as a Mixed-Integer Linear Program (MILP) is adopted. Simulations with the exact method show the efficiency of the proposed strategy based on several performance criteria. In particular, the acceptance rate and the delay are respectively improved by 15.1 and 18.5 percent, while preventing QoS violations.

From SLA to vendor‐neutral metrics: An intelligent knowledge‐based approach for multi‐cloud SLA‐based broker

Cloud computing has been consolidated as a support for the vast majority of current and emerging technologies. However, there are some barriers that prevent the exploitation of the full potential of this technology. First, the major cloud providers currently put the onus of implementing the mechanisms that ensure compliance with the desired service levels on cloud consumers. However, consumers do not have the required expertise. Since each cloud provider exports a different set of low-level metrics, the strategies defined to ensure compliance with the established service-level agreement (SLA) are bound to a particular cloud provider. This fosters provider lock-in and prevents consumers from benefiting from the advantages of multi-cloud environments. This paper presents a solution to the problem of automatically translating SLAs into objectives expressed as metrics that can be measured across multiple cloud providers. First, we propose an intelligent knowledge-based system capable of automatically translating high-level SLAs defined by cloud consumers into a set of conditions expressed as vendor-neutral metrics, providing feedback to cloud consumers (intelligent tutoring system). Secondly, we present the set of vendor-neutral metrics and explain how they can be measured for the different cloud providers. Finally, we report a validation based on two use cases (IaaS and PaaS) in a multi-cloud environment formed by leading cloud providers. This evaluation has demonstrated that, thanks to the complementarity of the two solutions, cloud consumers can automatically and transparently exploit the multi-cloud in many application domains, as endorsed by the cloud experts consulted in the course of this study.