InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Federated learning for distributed intrusion detection systems in public networks

Abstract. The rapid integration of technologies such as IoT devices, cloud, and edge computing has led to a progressively interconnected network of intelligent environments, services, and public infrastructures. This evolution highlights the critical need for sophisticated and self-governing Intrusion Detection Systems (IDS) to enhance trust and ensure the security and integrity of these interconnected environments. Furthermore, the advancement of AI-based Intrusion Detection Systems hinges on the effective utilization of high-quality data for model training. A considerable number of datasets created in controlled lab environments have recently been released, which has significantly facilitated researchers in developing and evaluating resilient Machine Learning models. However, a substantial portion of the architectures and datasets available are now considered outdated. As a result, the principal aim of this thesis is to contribute to the enhancement of knowledge concerning the creation of contemporary testbed architectures specifically designed for defense systems. The main objective of this study is to propose an innovative testbed infrastructure design, capitalizing on the broad connectivity panOULU public network, to facilitate the analysis and evaluation of AI-based security applications within a public network setting. The testbed incorporates a variety of distributed computing paradigms including edge, fog, and cloud computing. It simplifies the adoption of technologies like Software-Defined Networking, Network Function Virtualization, and Service Orchestration by leveraging the capabilities of the VMware vSphere platform. In the learning phase, a custom-developed application uses information from the attackers to automatically classify incoming data as either normal or malicious. This labeled data is then used for training machine learning models within a federated learning framework (FED-ML). The trained models are validated using previously unseen network data (test data). The entire procedure, from collecting network traffic to labeling data, and from training models within the federated architecture, operates autonomously, removing the necessity for human involvement. The development and implementation of FED-ML models in this thesis may contribute towards laying the groundwork for future-forward, AI-oriented cybersecurity measures. The dataset and testbed configuration showcased in this research could improve our understanding of the challenges associated with safeguarding public networks, especially those with heterogeneous environments comprising various technologies

An artificial intelligence-based collaboration approach in industrial IoT manufacturing : key concepts, architectural extensions and potential applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented

Opportunities and Challenges of Joint Edge and Fog Orchestration

Pushing contents, applications, and network functions closer to end users is necessary to cope with the huge data volume and low latency required in future 5G networks. Edge and fog frameworks have emerged recently to address this challenge. Whilst the edge framework was more infrastructure focused and more mobile operator-oriented, the fog was more pervasive and included any node (stationary or mobile), including terminal devices. This article analyzes the opportunities and challenges to integrate, federate, and jointly orchestrate the edge and fog resources into a unified framework.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586