In graph theory and its practical networking applications, e.g., telecommunications and transportation, the problem of finding paths has particular importance. Selecting paths requires giving scores to the alternative solutions to drive a choice. While previous studies have provided comprehensive evaluation of single-path solutions, the same level of detail is lacking when considering sets of paths. This paper emphasizes that the path characterization strongly depends on the properties under consideration. While property-based characterization is also valid for single paths, it becomes crucial to analyse multiple path sets. From the above consideration, this paper proposes a mathematical approach, defining a functional model that lends itself well to characterizing the path set in its general formulation. The paper shows how the functional model contextualizes specific attributes.

Processing capacity distribution has become widespread in the fog computing era. End-user services have multiplied, from consumer products to Industry 5.0. In this scenario, the services must have a very high-reliability level. But in a system with such displacement of hardware, the reliability of the service necessarily passes through the hardware design. Devices shall have a high quality, but they shall also efficiently support fault management. Hardware design must take into account all fault management functions and participate in creating a fault management policy to ensure that the ultimate goal of fault management is fulfilled, namely to increase a system's reliability. Efficiently and sustainably, both in the system's performance and the product's cost. This paper analyzes the hardware design techniques that efficiently contribute to the realization of fault management and, consequently, guarantee a high level of reliability and availability for the services offered to the end customer. We describe hardware requirements and how they affect the choice of devices in the hardware design of networking systems.

In this article, the problem of temporal isolation among containerized software components running in shared cloud infrastructures is tackled, proposing an approach based on hierarchical real-time CPU scheduling. This allows for reserving a precise share of the available computing power for each container deployed in a multi-core server, so to provide it with a stable performance, independently from the load of other co-located containers. The proposed technique enables the use of reliable modeling techniques for end-to-end service chains that are effective in controlling the application-level performance. An implementation of the technique within the well-known OpenStack cloud orchestration software is presented, focusing on a use-case framed in the context of network function virtualization. The modified OpenStack is capable of leveraging the special real-time scheduling features made available in the underlying Linux operating system through a patch to the in-kernel process scheduler. The effectiveness of the technique is validated by gathering performance data from two applications running in a real test-bed with the mentioned modifications to OpenStack and the Linux kernel. A performance model is developed that tightly models the application behavior under a variety of conditions. Extensive experimentation shows that the proposed mechanism is successful in guaranteeing isolation of individual containerized activities on the platform.

Fault management is an important function that impacts the design of any digital system, from the simple kiosk in a shop to a complex 6G network. It is common to classify fault conditions into different taxonomies using terms like fault or error. Fault taxonomies are often suitable for managing fault detection, fault reporting, and fault localization but often neglect to support all different functions required by a fault management process. A correctly implemented fault management process must be able to distinguish between defects and faults, decide upon ap-propriate actions to recover the system to an ideal state, and avoid an error condition. Fault management is a multi-disciplinary process where recovery actions are deployed promptly by com-bined hardware, firmware, and software orchestration. The importance of fault management processes significantly increases with modern nanometer technologies, which suffer the risk of so-called soft errors, a corruption of a bit cells that can happen due to spurious disturbance, like cosmic radiation. Modern fault management implementations must support recovery actions for soft errors to ensure a steady system. This paper describes an extended fault classification model that emphasizes fault management and recovery actions. We aim to show how the reliability-based fault taxonomy definition is more suitable for the overall fault management process.

This work presents a framework for evaluating the performance of various virtual switching solutions, each widely adopted on Linux to provide virtual network connectivity to containers in high-performance scenarios, like in Network Function Virtualization (NFV). We present results from the use of this framework for the quantitative comparison of the performance of software-based and hardware-accelerated virtual switches on a real platform with respect to a number of key metrics, namely network throughput, latency and scalability.

This paper presents preliminary results from our on-going research for ensuring stable performance of co-located distributed cloud services in a resource-efficient way. It is based on using a real-time CPU scheduling policy to achieve a fine-grain control of the temporal interferences among real-time services running in co-located containers. We present results obtained applying the method to a synthetic application running within LXC containers on Linux, where a modified kernel has been used that includes our real-time scheduling policy.

This paper highlights inefficiencies in modern cloud infrastructures due to a distance between the research on high-level cloud management / orchestration and the research on low-level kernel and hypervisor mechanisms. Our position about this issue is that more research is needed to make these two worlds talk to each other, providing richer abstractions to describe the low-level mechanisms and automatically map higher-level descriptions and abstractions to configuration and performance tuning options available within operating systems and kernels (both host and guest), as well as hypervisors.