Multiple Public Clouds Research Articles

Cloud storage cost: a taxonomy and survey

Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain.

Footprint of cloud computing on banking services

Technological development plays a vital role in the advancement of modern banking. The fintech sector has been impacted by cloud computing due to its strong influence in meeting many of the needs of the financial sector. In terms of security, service, innovation, and scalability, the cloud has provided numerous advantages to the banking zone. Cloud computing is in full swing in the 21st century. 55% of organizations use multiple public clouds to move their infrastructure to the cloud. The bank of 2030 will be significantly distinct from the one we have now. In the face of converting customers’ necessities and developing tech and opportunity enterprise strategies, banks must start making plans now to ensure they are prepared.

LISA: a lean information service architecture for SLA management in multi-cloud environments

Cloud Computing emerged as a disruptive technology for managing IT services over the internet, evolving from Grid Computing, Utility Computing and Software-as-a-Service (SaaS). After an initial scepticism, international companies are widely migrating their IT workload on private and public Cloud to optimise geographical coverage and launch new digital services. Currently, hybrid and Multi-Cloud environments introduce additional complexity in managing the Quality of Services (QoS) therefore requiring more sophisticated Service Level Agreements (SLAs). To deal with these issues, our research developed a SLA-aware Lean Information Service Architecture (LISA) for managing Multi-Cloud environments that support users in the whole service lifecycle. LISA performance was tested in the Innovation Lab of a global telco operator, by deploying services on private Cloud and public Cloud providers. Experimental results prove not only LISA's effectiveness but also efficiency in various aspects, such as preventing SLA violations and service performance degradations, optimising the QoS, and controlling the service components deployed across multiple public Cloud providers.

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.

HCloud: A trusted JointCloud serverless platform for IoT systems with blockchain

Cloud computing has been exploited in managing large-scale IoT systems. IoT cloud servers usually handle a large number of requests from various IoT devices. Due to the fluctuant and heavy workload, the servers require the cloud to provide high scalability, stable performance, low price and necessary functionalities. However, traditional clouds usually offer computing service with the abstraction of virtual machine (VM), which can hardly meet these requirements. Meanwhile, different cloud vendors provide different performance stabilities and price models, which fluctuate according to the dynamic workload. A single cloud cannot satisfy all the requirements of the IoT scenario well. The JointCloud computing model empowers the cooperation among multiple public clouds. However, it is still difficult to dynamically schedule the workload on different clouds based on the VM abstraction. This paper introduces HCloud, a trusted JointCloud platform for IoT systems using serverless computing model. HCloud allows an IoT server to be implemented with multiple serverless functions and schedules these functions on different clouds based on a schedule policy. The policy is specified by the client and includes the required functionalities, execution resources, latency, price and so on. HCloud collects the status of each cloud and dispatches serverless functions to the most suitable cloud based on the schedule policy. By leveraging the blockchain technology, we further enforce that our system can neither fake the cloud status nor wrongly dispatch the target functions. We have implemented a prototype of HCloud and evaluated it by simulating multiple cloud providers. The evaluation results show that HCloud can greatly improve the performance of serverless workloads with negligible costs.

Petri net model for resource scheduling with auto scaling in elastic cloud

Cloud service vendors offer wide range of services in a pay per use paradigm to the customers. The aim of cloud resource management is to speed up the execution of tasks and efficient usage of computing resources. The main benefit of optimal scheduling policy is reduced makespan, energy as well as cost with minimum number of service level agreement (SLA) violations. In market oriented cloud the service vendors offer vivid variety of purchasing options and also dynamic prices to the customers. In order to incorporate these purchase-promotional offers and dynamic prices, this paper proposes a Petri net model for scheduling the workload across physical servers. Here, SLA requirements considered are CPU speed, memory, makespan and bandwidth with fewer virtual machine migrations. The experimental results indicate that the proposed system efficiently performs dynamic provisioning and elasticity in multiple public clouds with auto scaling that reduces makespan, number of SLA violations, penalty cost and maximises the profit.

Communication Pattern Based Data Authentication (CPDA) Designed for Big Data Processing in a Multiple Public Cloud Environment

With the development of cloud computing, there is a growing trend of multi-cloud Collaborative Big Data Computation (CBDC). In this environment, threats from authorized insiders are of particular concerns. Based on an extreme case of distributed computation where multiple collaborators jointly perform CBDC on shared datasets using an example distributed computing framework, MapReduce (MR), deployed in a Multiple Public Cloud (MPC) environment, this paper investigates how to protect the authenticity of data used during the computation in an efficient and scalable manner by proposing and evaluating a novel data authentication solution. The solution, called a Communication Pattern based Data Authentication (CPDA) framework, ensures data authenticity and non-repudiation of origin at the finest granularity without compromising efficiency and scalability. This is achieved by using an idea of communication pattern based authentication data aggregation. The framework has been comprehensively evaluated both theoretically and experimentally. The evaluation results show that the CPDA framework offers the strongest level of data authenticity protection (equivalent to that provided by digitally signing each data object individually) but introduces much lower overhead cost than the digital signature based solution. The results demonstrate that the idea of communication pattern based authentication data aggregation brings much benefit in terms of supporting efficient and scalable data authentication in a large-scale distributed system.

Petri net model for resource scheduling with auto scaling in elastic cloud

Cloud service vendors offer wide range of services in a pay per use paradigm to the customers. The aim of cloud resource management is to speed up the execution of tasks and efficient usage of computing resources. The main benefit of optimal scheduling policy is reduced makespan, energy as well as cost with minimum number of service level agreement (SLA) violations. In market oriented cloud the service vendors offer vivid variety of purchasing options and also dynamic prices to the customers. In order to incorporate these purchase-promotional offers and dynamic prices, this paper proposes a Petri net model for scheduling the workload across physical servers. Here, SLA requirements considered are CPU speed, memory, makespan and bandwidth with fewer virtual machine migrations. The experimental results indicate that the proposed system efficiently performs dynamic provisioning and elasticity in multiple public clouds with auto scaling that reduces makespan, number of SLA violations, penalty cost and maximises the profit.

Cyber-physical application monitoring across multiple clouds

Cyber-physical systems (CPS) integrate cyber-infrastructure comprising computers and networks with physical processes. The cyber components monitor, control, and coordinate the physical processes typically via actuators. As CPS are characterized by reliability, availability, and performance, they are expected to have a tremendous impact not only on industrial systems but also in our daily lives. We have started to witness the emergence of cloud-based CPS. However, cloud systems are prone to stochastic conditions that may lead to quality of service degradation. In this paper, we propose M2CPA - a novel framework for multi-virtualization, and multi-cloud monitoring in cloud-based cyber-physical systems. M2CPA monitors the performance of application components running inside multiple virtualization platforms deployed on multiple clouds. M2CPA is validated through extensive experimental analysis using a real testbed comprising multiple public clouds and multi-virtualization technologies.

Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart Factory

The smart factory of Industry 4.0 has been regarded as a solution for handling the increasing production complexity caused by growing global economy and demand for customized products. Besides, it will make the interactions between humans, machines, and products become a highly competitive area for market capitalization in the near feature. Nowadays, cloud computing with the high performance of computing and self-service access plays an important role in realizing smart factor. To minimize the overall cost of company in a heterogeneous cloud environment, including multiple public clouds, while ensuring a proper level of quality-of-service, task placement across multiple public clouds is a critical problem, where task deadlines and long-haul data transmission costs between smart factory and different clouds must be considered. We formulate this task placement problem as an integer linear program (ILP) to minimize company cost under the task deadline constraint. With extensive simulations, we evaluate the performance of our proposed ILP model in heterogeneous public clouds with finite and infinite resources.

Hybris

Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources. Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data, which are dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f +1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3 f +1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds. We implemented Hybris in Java and evaluated it using a series of micro and macro-benchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.

Novel efficient particle swarm optimization algorithms for solving QoS‐demanded bag‐of‐tasks scheduling problems with profit maximization on hybrid clouds

SummaryUsers are willing to execute bag‐of‐task applications consisting of multiple tasks on clouds, since cloud resources are delivered in a pay‐as‐you‐go manner. Given multiple bag‐of‐task applications to be executed with user‐specified quality‐of‐service demands, a cloud provider has to outsource some tasks to public clouds when its private cloud has insufficient resources to afford all applications' tasks. The key issue is how to schedule tasks on hybrid clouds (environments consisting of a private cloud and multiple public clouds) for maximizing the cloud provider's profit while meeting the quality‐of‐service demands. To solve this problem, we propose an efficient particle swarm optimization algorithm (EPSO) and three hybrid ones (HEPSO1‐HEPSO3), in which task sequences are considered as solutions. A mapping operator (BBMO) is developed to map particles to solutions and a quick task dispatching method containing an acceleration method is designed to calculate solutions' objectives. Experimental results show that EPSO not only outperforms an existing PSO (the best algorithm for solving a problem that is a special case of ours) significantly but also achieves a 11.48x speedup. The HEPSO1 to HEPSO3 outperform EPSO. The BBMO outperforms the well‐known ranked‐order value rule and achieves a 5.47x speedup. The acceleration method in quick task dispatching brings a 2.69x speedup.

Middleware for Multicloud

There are many different options available to implement information technology (IT) today: enterprise data center legacy approaches or private cloud, public cloud, hybrid cloud, fog, device, mobile edge, and multicloud. Although definitions vary, for a variety of reasons, many companies are using multicloud approaches in a variety of patterns, at various layers such as infrastructure as a service (IaSS) and software as a service (SaaS).1 In some cases, multiple SaaS or infrastructure public or private clouds are used to support an end-to-end process, sometimes called workflow enactment.2 For example, a purchase order might be sent via a (cloud-based SaaS) email service such as Gmail, processed in a private cloud application, and then billed via a (cloud-based SaaS) billing service such as Zuora. Other use cases involve multiple public infrastructure clouds. For example, Netflix famously uses Amazon Web Services (AWS) for digital movie transcoding and its entertainment recommendation engine, but it also backs up data to Google’s cloud in the event of an AWS outage.

AUTO-SCALING AND ADJUSTMENT PLATFORM FOR CLOUD-BASED SYSTEMS

For customers of cloud-computing platforms it is important to minimize the infrastructure footprint and associated costs while providing required levels of Quality of Service (QoS) and Quality of Experience (QoE) dictated by the Service Level Agreement (SLA). To assist with that cloud service providers are offering: (1) horizontal resource scaling through provisioning and destruction of virtual machines and containers, (2) vertical scaling through changing the capacity of individual cloud nodes. Existing scaling solutions mostly concentrate on low-level metrics like CPU load and memory consumption which doesn’t always correlate with the level of SLA conformity. Such technical measures should be preprocessed and viewed from a higher level of abstraction. Application level metrics should also be considered when deciding upon scaling the cloud-based solution. Existing scaling platforms are mostly proprietary technologies owned by cloud service providers themselves or by third parties and offered as Software as a Service. Enterprise applications could span infrastructures of multiple public and private clouds, dictating that the auto-scaling solution should not be isolated inside a single cloud infrastructure.
 The goal of this paper is to address the challenges above by presenting the architecture of Auto-scaling and Adjustment Platform for Cloud-based Systems (ASAPCS). It is based on open-source technologies and supports integration of various low and high level performance metrics, providing higher levels of abstraction for design of scaling algorithms. ASAPCS can be used with any cloud service provider and guarantees that move from one cloud platform to another will not result in complete redesign of the scaling algorithm. ASAPCS itself is horizontally scalable and can process large amounts of real-time data which is particularly important for applications developed following the microservices architectural style. ASAPCS approaches the scaling problem in a nonstandard way by considering real-time adjustments of the application logic to be part of the scalability strategy if it can result in performance improvements.

Cloud computing and big data: Technologies and applications

Cloud computing and big data: Technologies and applications

Cost-Effective Service Provisioning for Hybrid Cloud Applications

The workloads of social computing are tremendous, bursty and unpredictable. A hybrid cloud, which combines a private cloud and a public cloud, is a promising solution to processing the workloads. For most corporations, they leverage one public cloud. However, with fierce competition among public cloud providers, public cloud services change frequently, which may lead to service unavailability and a less cost-effective hybrid cloud solution. As a result, leveraging multiple public clouds in the hybrid cloud is a potential solution. In this paper, we identify such a problem in current hybrid cloud and analyze the necessity of load balancing for hybrid cloud applications. Focusing on cost minimization and performance guarantee, we propose a Least Cost per Connection (LCC) algorithm so as to choose the most cost-effective clouds along with adapting changes among multiple public clouds. The simulation results show that our solution can significantly decrease the outsourcing cost as well as guarantee Quality of Service (QoS) of applications.

Analysis of the Join Performance in Vertically Distributed Cloud Databases

Cloud Computing becomes interesting for enterprises across all branches. Renting computing capabilities from external providers avoids initial investments, as only those resources have to be paid that were used eventually. Especially in the context of “Big Data” this pay-as-you-go accounting model is particularly important. The dynamically scalable resources from the Cloud enable enterprises to store or analyze these huge amounts of unstructured data without using their own hardware infrastructure. However, Cloud Computing is currently facing severe data security and protection issues. These challenges require new ways to store and analyze data, especially when huge data volumes with sensitive data are stored at external locations. The presented approach separates data on database table level into independent chunks and distributes them across several clouds. Hence, this work is a contribution to a more secure and resilient cloud architecture as multiple public and private cloud providers can be used independently to store data without losing data security and privacy constraints.

Evacuation and Emergency Management Using a Federated Cloud

Contemporary disaster relief techniques fall significantly short in terms of efficiency and timeliness. In a postdisaster scenario, the generation and transmission of voluminous data at a high velocity impacts the communication framework. This work exploits the benefits of opportunistic communication and efficient big data management policies, focusing on the collaboration of multiple private and/or public clouds of diverse nature to perform damage assessment and determine the spatial distribution of the live victims and their physical and mental status. Based on the analytics, real-time decision making of the rescue operation is achieved.

A service brokering and recommendation mechanism for better selecting cloud services.

Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI).

Optimal application allocation on multiple public clouds

Cloud computing customers currently host all of their application components at a single cloud provider. Single-provider hosting eases maintenance tasks, but reduces resilience to failures. Recent research (Li et al., 2010) also shows that providers’ offers differ greatly in performance and price, and no single provider is the best in all service categories. In this paper we investigate the benefits of allocating components of a distributed application on multiple public clouds (multi-cloud). We propose a resource allocation algorithm that minimizes the overall cloud operation cost, while satisfying required service-level agreements (SLAs). In spite of the additional delays for inter-cloud communication and the additional costs for inter-cloud data transfer, our simulation study, using real cloud performance and cost data, demonstrates that multi-cloud allocation outperforms single-cloud allocations in a variety of realistic scenarios.