Model Driven Deployment of Auto-Scaling Services on Multiple Clouds

DevOps teams have to consider many technology and platform aspects when developing, deploying and operating cloud based applications: application deployments need to work everywhere on different cloud platforms, identities need to come from anywhere, and networks need to connect to anyone. The CYCLONE middleware is a holistic middleware stack that allows deploying and managing cloud based applications on multiple clouds and multiple cloud platforms. It includes a deployment manager, a practical identity federation, as well as a network manager that connects VMs independent of any specific infrastructure. This article explains the CYCLONE middleware stack, and what it can offer for application developers and operators. The paper describes in details the main bioinformatics use cases that evolve from a single VM installation for simple microbial research to multicloud infrastructure for advanced genomic resource. The paper also describes the CYCLONE federated identity management and access control infrastructure that significantly simplifies access for institutional users.

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.

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.

Most of the current cloud computing platforms offer Infrastructure as a Service (IaaS) model, which aims to provision basic virtualised computing resources as on-demand and dynamic services. Nevertheless, a single cloud does not have limitless resources to offer to its users, hence the notion of an Inter-Cloud enviroment where a cloud can use the infrastructure resources of other clouds. However, there is no common framework in existence that allows the service owners to seamlessly provision even some basic services across multiple cloud service providers, albeit not due to any inherent incompatibility or proprietary nature of the foundation technologies on which these cloud platforms are built. In this paper we present a novel solution which aims to cover a gap in a subsection of this problem domain. Our solution offers a security architecture that enables service owners to provision a dynamic and service-oriented secure virtual private network on top of multiple cloud IaaS providers. It does this by leveraging the scalability, robustness and flexibility of peer-to-peer overlay techniques to eliminate the manual configuration, key management and peer churn problems encountered in setting up the secure communication channels dynamically, between different components of a typical service that is deployed on multiple clouds. We present the implementation details of our solution as well as experimental results carried out on two commercial clouds.

Most of the current cloud computing platforms offer Infrastructure as a Service (IaaS) model, which aims to provision basic virtualized computing resources as on-demand and dynamic services. Nevertheless, a single cloud provider may not have limitless resources to offer to its users, hence the notion of an Inter-Cloud environment where a cloud can use the infrastructure resources of other clouds. However, there is no common framework in existence that allows the service owners to seamlessly provision even some basic services across multiple cloud service providers, albeit not due to any inherent incompatibility or proprietary nature of the foundation technologies on which these cloud platforms are built. In this paper we present a novel solution which aims to cover a gap in a subsection of this problem domain. Our solution offers a security architecture that enables service owners to provision a dynamic and service-oriented secure virtual private network on top of multiple cloud IaaS providers. It does this by leveraging the scalability, robustness and flexibility of peer-to-peer overlay techniques to eliminate the manual configuration, key management and peer churn problems encountered in setting up the secure communication channels dynamically, between different components of a typical service that is deployed on multiple clouds. We present the implementation details of our solution as well as experimental results detailing the overheads of our solution carried out on two commercial clouds.

Enterprise organizations increasingly depend on multi-cloud strategies to host mission-critical customer relationship management (CRM) and enterprise resource planning (ERP) systems. At the same time, artificial intelligence (AI) is becoming deeply embedded in business operations through automation, decision support, and predictive intelligence. Integrating AI-driven automation across distributed, heterogeneous cloud environments introduces architectural, operational, and governance complexities that exceed the capabilities of traditional integration approaches. This article presents a structured set of reusable design patterns for implementing AI-powered automation in multi-cloud CRM and ERP ecosystems. The patterns are derived from real-world enterprise modernization initiatives, including a large-scale healthcare transformation program spanning multiple major cloud platforms. Five core patterns are proposed: (1) Event-Driven Orchestration Layer for decoupled workflow coordination, (2) Intelligent API Gateway for adaptive service routing and resilience, (3) Composable Microservices with Embedded AI Agents for modular automation, (4) Unified Data Fabric with AI Data Pipelines for cross-cloud data consistency and governance, and (5) Adaptive Feedback Loop Automation for continuous learning and system optimization. Together, these patterns address recurring challenges related to orchestration complexity, data fragmentation, regulatory compliance, scalability, and operational resilience. Implementation considerations are examined across technical, organizational, and governance dimensions. A healthcare case context illustrates practical application, demonstrating measurable improvements in processing efficiency, operational effectiveness, and regulatory compliance. The paper contributes an applied architectural framework that enables enterprises to design resilient, governable, and scalable AI automation platforms aligned with long-term digital transformation objectives.

Artificial Intelligence on Information Services

The proliferation of multi-cloud environments has introduced complexities in resource management, necessitating intelligent solutions for optimal resource allocation. Traditional resource allocation techniques often struggle to adapt to the dynamic nature of multi-cloud ecosystems, leading to suboptimal performance, increased costs, and inefficient resource utilization. This paper explores the application of Deep Reinforcement Learning (DRL) as an advanced AI-driven approach to optimize resource distribution across multiple cloud platforms. By leveraging reinforcement learning techniques, DRL enables autonomous decision-making, learning from past experiences to refine resource allocation strategies in real-time. The proposed framework is designed to handle diverse workloads, minimize latency, and maximize cost-efficiency. Additionally, the study evaluates key performance metrics, including throughput, response time, and adaptability, comparing DRL-based approaches with traditional methods. Experimental results indicate that DRL significantly improves efficiency, scalability, and adaptability in dynamic cloud environments, paving the way for intelligent and automated cloud resource management.

ABSTRACTCloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences. However, only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers: 1) selecting an appropriate cloud platform for a specific application could be challenging, as various cloud services are available and 2) existing general cloud platforms are not designed to support geoscience applications, algorithms and models. To tackle such barriers, this research aims to design a hybrid cloud computing (HCC) platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform. This platform can manage different types of underlying cloud infrastructure (e.g., private or public clouds), and enables geoscientists to test and leverage the cloud capabilities through a web interface. Additionally, the platform also provides different geospatial cloud services, such as workflow as a service, on the top of common cloud services (e.g., infrastructure as a service) provided by general cloud platforms. Therefore, geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly. A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.

The security issue has been a challenging concern for cloud computing because of the multitenant usage model. In cloud, each application normally runs on a dynamic coalition that is composed by multiple virtual machines (VMs) running on different virtualised service nodes, which the authors called logic virtual domain (LVD). Moreover, the owners of cloud applications, who are also the tenants of cloud, would specify some security policies to control the access to those resources that they have paid for. Therefore the owners of cloud infrastructures have to provide the tenants with the mechanism to correctly configure and enforce the access control policies on resources that are from multiple service nodes, to meet the security requirements from cloud applications. To address the above challenge, this study presents the design and implementation about a multilayer access control architecture for LVD, named CloudAC, aiming to provide isolation control, information flow control and resource-sharing control among multiple VMs on Xen virtualisation platforms in cloud computing environment. The theory and technology this research formed will provide reliable security guarantee for resource configuration and application deployment on LVDs.

The growth in the number and types of cloud-based services offered to IT customers is supported by the constant entry of new actors in the market and the consolidation of disruptive technologies such as AI, Big Data and Micro-services. From the customer’s perspective, in a market landscape where the cloud offer is highly diversified due to the presence of multiple competing service providers, picking the service that best accommodate their specific needs is a critical challenge. Once the choice is made, so called “cloud orchestration tools” (orchestrators) are required to take care of the customer application’s life-cycle. While big players offer their customers proprietary orchestrators, in the literature quite a number of open-source initiatives have launched multi-cloud orchestrators capable of transparently managing applications on top of the most representative cloud platforms. In this paper, we propose TORCH, a TOSCA-based framework for the deployment and orchestration of cloud applications, both classical and containerised, on multiple cloud providers. The framework assists the cloud customer in defining application requirements by using standard specification models. Unlike other multi-cloud orchestrators, adopts a strategy that separates the provisioning workflow from the actual invocation of proprietary cloud services API. The main benefit is the possibility to add support to any cloud platforms at a very low implementation cost. In the paper, we present a prototypal implementation of TORCH and showcase its interaction with two different container-based cluster platforms. Preliminary performance tests conducted on a small-scale test-bed confirm the potential of TORCH.

Cloud security is an evolving sub-domain of computer and network security. Cloud platform utilizes third-party data centers model. An example of cloud platform as a service (PaaS) is Heroku. It supports several programming languages that are used for web application deployment model. Heroku is based on a managed container system, with integrated data services and a powerful ecosystem, for deploying and running modern apps. One essential issue in cloud computing is data security, which is handled using cryptography methods. A possible method to encrypt data is Advanced Encryption Standard (AES). In this paper, we implement Heroku as a cloud platform, then we implement AES for data security in Heroku. The performance evaluation shows that AES cryptography can be used for data security. Moreover, delay calculation of data encryption shows that larger size of data increases the data delay time for encrypting data.

Cloud computing has attracted a great deal of attention in the education sector as a way of delivering more economical, securable, and reliable education services. This paper proposes and introduces a cloud-based smart education system for e-learning content services with a view to delivering and sharing various enhanced forms of educational content, including text, pictures, images, videos, 3-dimensional (3D) objects, and scenes of virtual reality (VR) and augmented reality (AR). The proposed system consists of six main features that are required for deploying cloud-based educational content services: 1) a cloud platform that provides an infrastructure for the realization of a cloud-based educational media service environment, 2) a compatible file format that enables it to provide media content through various types of devices, 3) an authoring tool that enables teachers to create various types of media content, 4) a content viewer that displays different types of media on multiple platforms, 5) an inference engine that provides students with individualized learning content, and 6) a security system that manages privileged user access and data encryption in the cloud for dependable educational content services. We believe that the proposed system should provide a new and innovative solution for cloud-based educational media services by supporting a cloud-based service environment with a totally integrated system.

Management of networked Internet of Things (IoT) infrastructure with in-network processing capabilities is becoming increasingly difficult due to the volatility of the system with low-cost resource-constraint devices. Traditional software-defined networking (SDN) based management systems are not suitable to handle the plug and play nature of such systems. Therefore, in this paper, we propose Aloe, an elastically auto-scalable SDN orchestration framework. Instead of using service grade SDN controller applications, Aloe uses multiple lightweight controller instances to exploit the capabilities of in-network processing infrastructure. The proposed framework ensures the availability and significant reduction in flow-setup delay by deploying instances near the resource constraint IoT devices dynamically. Aloe supports fault-tolerance and can recover from network partitioning by employing self-stabilizing placement of migration capable controller instances. The performance of the proposed system is measured by using an in-house testbed along with a large scale deployment in Amazon web services (AWS) cloud platform. The experimental results from these two testbed show significant improvement in response time for standard IoT based services. This improvement of performance is due to the reduction in flow-setup time. We found that Aloe can improve flow-setup time by around 10%-30% in comparison to one of the state of the art orchestration framework.

With the continuous increase of equipment and traffic, network development is facing a series of problems: complex management and operation and high cost, slow deployment and deployment of new services, lack of flexibility in traffic scheduling, and so on. Open Stack provides solutions for infrastructure as a service (Iaa S), a cloud operating system that can manage a large number of resource pools in the entire data center, including computing, storage, and network resources. This article combines SDN and Open Stack to achieve unified management of massive network devices in cloud data centers. Network administrators can optimize and maintain the network based on a global view of the network, reducing costs for creating cloud data centers. Through the research of this paper, the hybrid networking cloud platform suitable for the operator’s architecture proposed in this paper can better assist operators’ first-line implementation and operation and maintenance personnel to easily cope with the hybrid networking requirements of the cloud platform. Compared with the traditional cloud management platform, the hybrid network has strong operability.