Multi-cloud environments enable a cost-efficient scaling of cloud-native applications across geographically distributed virtual nodes with different pricing models. In this context, the resource fragmentation caused by frequent changes in the resource demands of deployed microservices, along with the allocation or termination of new and existing microservices, increases the deployment cost. Therefore, re-orchestrating deployed microservices on a cheaper configuration of multi-cloud nodes offers a practical solution to restore the cost efficiency of deployment. However, the rescheduling procedure causes frequent service interruptions due to the continuous termination and rebooting of the containerized microservices. Moreover, it may potentially interfere with and delay other deployment operations, compromising the stability of the running applications. To address this issue, we formulate a multi-objective integer linear programming (ILP) problem that computes a microservice rescheduling solution capable of providing minimum deployment cost without significantly affecting the service continuity. At the same time, the proposed formulation also preserves the quality of service (QoS) requirements, including latency, expressed through microservice co-location constraints. Additionally, we present a heuristic algorithm to approximate the optimal solution, striking a balance between cost reduction and service disruption mitigation. We integrate the proposed approach as a custom plugin of the Kubernetes (K8s) scheduler. Results reveal that our approach significantly reduces multi-cloud deployment costs and service disruptions compared to the benchmark schemes, while ensuring QoS requirements are consistently met.

Maintaining up-to-date attack profiles is a critical challenge for Network Intrusion Detection Systems (NIDSs). State-of-the-art solutions based on Machine Learning (ML) algorithms often rely on public datasets, which can be outdated or anonymised, hindering their effectiveness in real-world scenarios. Collaborative learning tackles data limitations by enabling multiple parties to jointly train and update their NIDSs through sharing recent attack information. However, directly sharing network traffic data can compromise the participants’ privacy. Federated Learning (FL) addresses this concern: it allows participants to collaboratively improve their NIDS models by sharing only the trained model parameters, not the raw data itself. Nevertheless, recent studies have proven that the Federated Averaging (FedAvg) algorithm at the core of FL can be inefficient with heterogeneous and unbalanced datasets. A recent solution called FLAD addresses the limitations of FedAvg, resulting in higher accuracy of the final ML model on out-of-distribution data. This work focuses on the resource usage of the FL process, demonstrating the superiority of FLAD over FedAvg in computational efficiency and convergence time, showcasing its potential to enhance NIDS effectiveness.

In network security, Network Function Virtualization can be exploited to implement flexible security services tailored to specific user needs. However, in practice this is hard to achieve due to the limitations of reference software platforms, such as Kubernetes, which are designed to orchestrate cloud-native services. In this work, we complement Kubernetes with a state-of-the-art algorithm for application-aware provisioning of security services. We demonstrate that the proposed solution improves basic provisioning mechanisms, such as the default Kubernetes scheduler, in terms of Quality of Service and security guarantees for the users.

Fog computing promises to extend cloud computing to match emerging demands for low latency, location-awareness and dynamic computation. It thus brings data processing close to the edge of the network by leveraging on devices with different computational characteristics. However, the heterogeneity, the geographical distribution, and the data-intensive profiles of IoT deployments render the placement of fog applications a fundamental problem to guarantee target performance figures. This is a core challenge for fog computing providers to offer fog infrastructure as a service, while satisfying the requirements of this new class of microservices-based applications. In this article we root our analysis on the throughput requirements of the applications while exploiting offloading towards different regions. The resulting resource allocation problem is developed for a fog-native application architecture based on containerised microservice modules. An algorithmic solution is designed to optimise the placement of applications modules either in cloud or in fog. Finally, the overall solution consists of two cascaded algorithms. The first one performs a throughput-oriented partitioning of fog application modules. The second one rules the orchestration of applications over a region-based infrastructure. Extensive numerical experiments validate the performance of the overall scheme and confirm that it outperforms state-of-the-art solutions adapted to our context.

In this paper we present Foggy, an architectural framework and software platform based on Open Source technologies. Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed, heterogeneous and decentralized Cloud Computing systems. Foggy is tailored for emerging domains such as 5G Networks and IoT, which demand resources and services to be distributed and located close to data sources and users following the Fog Computing paradigm. Foggy provides a platform for infrastructure owners and tenants (i.e., application providers) offering functionality of negotiation, scheduling and workload placement taking into account traditional requirements (e.g. based on RAM, CPU, disk) and non-traditional ones (e.g. based on networking) as well as diversified constraints on location and access rights. Economics and pricing of resources can also be considered by the Foggy model in a near future. The ability of Foggy to find a trade-off between infrastructure owners' and tenants' needs, in terms of efficient and optimized use of the infrastructure while satisfying the application requirements, is demonstrated through three use cases in the video surveillance and vehicle tracking contexts.

We introduce Cloud4IoT, a platform offering automatic deployment, orchestration and dynamic configuration of IoT support software components and data-intensive applications for data processing and analytics, thus enabling plug-and-play integration of new sensor objects and dynamic workload scalability. Cloud4IoT enables the concept of Infrastructure as Code in the IoT context: it empowers IoT operations with the flexibility and elasticity of Cloud services. Furthermore it shifts traditionally centralized Cloud architectures towards a more distributed and decentralized computation paradigm, as required by IoT technologies, bridging the gap between Cloud Computing and IoT ecosystems. Thus, Cloud4IoT is playing a role similar to the one covered by solutions like Fog Computing, Cloudlets or Mobile Edge Cloud. The hierarchical architecture of Cloud4IoThosts a central Cloud platform and multiple remote edge Cloud modules supporting dedicated devices, namely the IoT Gateways, through which new sensor objects are made accessible to the platform. Overall, the platform is designed in order to support systems where IoT-based and data intensive applications may pose specific requirements for low latency, restricted available bandwidth, or data locality. Cloud4IoT is built on several Open Source technologies for containerisation and implementations of standards, protocols and services for the IoT. We present the implementation of the platform and demonstrate it in two different use cases.