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The emergence of CubeSats as a viable means for realizing satellite networks at low costs has given rise to ubiquitous cyber-physical systems spanning air, ground, and space, in what is being recognized as the Internet of Space Things (IoST). IoST is expected to serve a wide variety of applications ranging from monitoring and reconnaissance to in-space backhauling, i.e., the network architecture must serve a plethora of application scenarios with differing service-level agreement (SLA) requirements over the same physical infrastructure in an end-to-end manner. At the same time, since the different use cases might belong to a variety of different stakeholders, IoST must support multi-tenancy and functional isolation of services. Consequently, a network slicing framework is vital to the success of IoST. To this end, an automatic network slicing framework for space-ground integrated networks is presented in this paper. The proposed framework has been designed to address the dual objectives of route computation and resource allocation with minimal SLA violations. Different from the existing state-of-the-art, the framework presented herein is purpose-built for ultra-dense CubeSat networks, and is fully automated. In other words, the framework is purely SLA-based, and does not require prior information concerning the resource requirements associated with a slice. Other key innovations introduced through this framework include a robust SLA model for slice customization, a novel topology construction mechanism, and a unique segment routing-based online admission control solution. Furthermore, the flexibility and efficacy of the proposed framework have been evaluated through a comprehensive use case driven evaluation scenario. [ABSTRACT FROM AUTHOR]

Segment Routing over IPv6 (SRv6 in short) is a networking architecture suitable for both IP backbones and datacenters. The research, standardization and implementation of this architecture are actively progressing and SRv6 is already adopted in a number of large scale deployments. Effective solutions for Performance Monitoring (PM) of SRv6 networks are strongly needed and there is a lot of activity in this area. A full blown Performance Monitoring solution needs to include: i) Data Plane (as needed to measure metrics such as packet loss and delay); ii) Control Plane (to send commands to the monitoring entities in the nodes); iii) Management Plane (e.g., to collect the measured metrics). Moreover, Big-Data tools and solutions can be applied inside or above the traditional Management Plane boundaries to store and analyze the collected data. In this article we describe SRv6-PM, a solution for Performance Monitoring of SRv6 networks that deals with all the aspects discussed above. SRv6-PM features a cloud-native architecture that supports: i) the ingestion, processing, storage and visualization of PM data using Big-Data tools; ii) the SDN-based control of network routers to drive the performance monitoring operations. In particular, we focus on Loss Monitoring and consider a solution capable of tracking single packet loss events operating in near-real time (e.g., with a time granularity in the order of 10-20 seconds). SRv6-PM is released as open source. We offer a re-usable and extensible platform that can be automatically deployed in different environments, from a single host to multiple servers on private/public clouds and includes a set of Big-Data tools and the SDN control plane. We also provide a reproducible Data Plane environment for PM experiments in SRv6 networks based on the Mininet emulator. [ABSTRACT FROM AUTHOR]

Cloud architectures achieve scaling through two main functions: (i) load-balancers, which dispatch queries among replicated virtualized application instances, and (ii) autoscalers, which automatically adjust the number of replicated instances to accommodate variations in load patterns. These functions are often provided through centralized load monitoring, incurring operational complexity. This article introduces a unified and centralized-monitoring-free architecture achieving both autoscaling and load-balancing, reducing operational overhead while increasing response time performance. Application instances are virtually ordered in a chain, and new queries are forwarded along this chain until an instance, based on its local load, accepts the query. Autoscaling is triggered by the last application instance, which inspects its average load and infers if its chain is under- or over-provisioned. An analytical model of the system is derived, and proves that the proposed technique can achieve asymptotic zero-wait time with high (and controlable) probability. This result is confirmed by extensive simulations, which highlight close-to-ideal performance in terms of both response time and resource costs. [ABSTRACT FROM AUTHOR]

Segment routing is an emerging networking technology, where an arbitrary forwarding path can be identified using a list of segments. In this article, we study network monitoring in segment routing based on monitoring trails. Unlike the existing monitoring cycle, a trail is more flexible because it can be either open or closed (i.e., a cycle). Consider a network with a given set of ${k}$ monitors, which are devices responsible for network monitoring. A monitoring trail must start and end at a monitor. The ${k}$ -monitor cover problem is to cover every link in the network using trails such that each trail has no more than ${K}$ segments and the total length of all trails is minimized. In this article, we prove that the ${k}$ -monitor cover problem is NP-hard. To solve it, the first ILP is formulated and an efficient heuristic algorithm (${k}$ -MCA) is designed. [ABSTRACT FROM AUTHOR]

Self-driving networks represent the next step of network management techniques in the close future. A fundamental point for such an evolution is the use of Machine Learning based solutions to extract information from data coming from network devices during their activity. In this work we focus on a new type of data, available thanks to the definition of the novel SRv6 paradigm, referred to as SRv6 Traffic Counters (SRTCs). SRTCs provide aggregated measurements related to forwarding operations performed by SRv6 routers. In this work a detailed description of different SRTCs types (SR.INT, PISD, PSID.TM and POL) is provided and their relationships is formalized. The theoretical framework deployed is used to identify, on the basis of network configuration parameters of both SRv6 and IGP protocols, the minimum set of independent SRTCs to characterize the Network Status: we show that about the 80% of counters can be neglected with no information loss. We also apply our framework to two use cases: i) Traffic Matrix (TM) Assessment and ii) Traffic Anomaly Detection. For the TM assessment, we show that in a partially deployed SRv6 scenario a specific type of SRTCs, i.e., PSID, is more reliable than other ones; on the contrary, in a fully deployed scenario POL and PSID.TM counters provide the full TM knowledge. For the Traffic Anomaly Detection case, we show that known solutions based on link load measurements can be improved when integrating SRTCs information. [ABSTRACT FROM AUTHOR]

In segment routing, a packet is forwarded along a path identified by a segment list. A segment list consists of segment identifiers (SIDs). A node-SID identifies a shortest-path segment, and an adjacency-SID identifies a link segment. A ${K}$ -segment path is a path with no more than ${K}$ segments. In this paper, we study the problem of finding a set of K-segment paths to carry all the flows in a given traffic matrix such that the maximum link utilization in the network is minimized. We first show that the solutions found by ${K}$ -LP, an existing linear programming (LP) approach, are not optimal because ${K}$ -LP does not support adjacency-SIDs. Focusing on 2-segment paths, a new LP formulation, denoted by e2-LP, is designed to support adjacency-SIDs in part. To fully support adjacency-SIDs, a mixed integer linear programming (MILP), denoted by ${K}$ -MILP, is designed. Since solving ${K}$ -MILP is time-consuming, a simplified version (${K}$ -sMILP) is also proposed. Finally, ${K}$ -sMILP is extended to prevent excessive flow splitting or using paths that are too long. [ABSTRACT FROM AUTHOR]

Segment Routing (SR) is a new fertile ground for Traffic Engineering (TE). By decomposing forwarding paths into segments, which specify a list of intermediate delivery points that a packet must visit on its way to the final destination, SR improves TE tasks and enables new solutions for the optimization of network resource utilization. This work proposes an Evolutionary Computation approach that enables Path Computation Element (PCE), or Software-defined Network (SDN) controllers, to optimize SR configurations for improved traffic distribution. Furthermore, we present a robust semi-oblivious method to address the variability of traffic requirements as well as alternative approaches to ensure a good network performance after link failures. In all cases, the optimization of network resource utilization is achieved using at the most three segments to configure each SR path. Moreover, all proposed optimization methods are made publicly available in a optimization framework developed by the authors. [ABSTRACT FROM AUTHOR]

Traffic matrix (TM) assessment is a key issue for optimizing network management costs and quality of service. This paper presents a method to measure the intensity of ingress–egress traffic flows on an Internet service providers network that overcomes the limits of the classical measurement-based approaches. The proposed algorithm, called segment routing perturbation traffic (SERPENT), uses a routing perturbation approach enabled by the segment routing paradigm: The paths of a subset of flows are changed so that their intensities can be determined measuring the variation of the load of the network links. The TM is measured in successive steps, called snapshots, in which sets of flows are progressively re-routed and measured, under a maximum link utilization constraint. We state an integer linear programming (ILP) optimization problem to determine the flows to be rerouted in one snapshot. SERPENT is an heuristic offering an efficient solution to the stated ILP. Results show that SERPENT assesses the intensity of more than 80% of flows even when the network is highly stressed, while reducing the configuration cost with respect to classical approaches. Moreover, when used in conjunction with an estimation algorithm, SERPENT allows a reduction of the estimation error by more than 50% with fewer than 5 snapshots. [ABSTRACT FROM AUTHOR]