To provide the predictability required by emerging applications, operators typically rely on policing and/or shaping at the edge to ensure that tenants do not use excess bandwidth that was not accounted for. One of the promises of 6G is to deploy applications with strict predictability requirements across subnets and even over the Internet, where policing cannot be implemented in the end hosts. This paper presents an empirical study of the ability of modern programmable network devices to implement predictable traffic policing in the network. We find out that none of the five investigated hardware switches can provide accurate traffic policing, a key requirement for providing predictable service to applications. We observe that the switches let applications send more than what they should be allowed to, reaching up to 60% and 100% relative error for the rate and burst parameters. We further uncover the fact that switches cannot police arbitrarily low bursts, e.g., not less than 13 kilobit for one of our switches. We investigate how such limitations impact the performance of state-of-the-art solutions for predictable latency such as Chameleon. We observe that, for ensuring its predictable guarantees, Chameleon rejects around 50% of the tenants it could accommodate if switches were perfect, hence decreasing by the same ratio the revenue for the operator. Based on these observations, we discuss solutions toward more accurate and predictable policing in wide-area networks.

Providing predictable performance to tenants is mission critical for network hypervisors. As a hypervisor acts as an intermediary between tenants controllers and the physical infrastructure, its resources (e.g., CPU, RAM) should be provisioned and allocated carefully. Initially, we demonstrate that state-of-the-art CPU prediction approaches are not suitable for provisioning network hypervisor CPU resources, since they predict only the mean CPU utilization. However, provisioning the resources with a mean value can significantly degrade the forwarding performance of a network hypervisor. In this article, we present a novel approach which provisions network hypervisor CPU resources efficiently, while avoiding performance degradation. We take three steps to achieve our goal:(i)conducting a profound measurement campaign to determine what is the minimum amount of CPU resources that needs to be allocated to a network hypervisor in order to have no performance degradation;(ii)revealing the key properties of virtual networks that affect the CPU utilization;(iii)designing a precise CPU prediction model. Using randomly generated virtual networks and arbitrary physical topologies, we show that our prediction model exhibits an average relative error of around 4%. Further, our evaluations indicate that provisioning the CPU resources of a network hypervisor based on the proposed prediction model does not degrade the hypervisor forwarding performance. Utilizing our approach, network operators can minimize their resources consumption while still providing predictable and undegraded forwarding performance to tenants.

Service function chains (SFCs) are an ordered set of virtual network functions (VNFs) which can realize a specific network service. Enabled by virtualization technologies, these VNFs are hosted on physical machines (PMs), and interconnected by network switches. In today networks, these resources are usually under-utilized and/or over-provisioned, resulting in power-inefficient deployments. To improve power-efficiency, SFCs should be deployed utilizing the minimum number of PMs and network equipment, which are not concomitant. Considering the existing PM and switch power consumption models and their resource constraints, we formulate the power-aware and delay-constrained joint VNF placement and routing (PD-VPR) problem as an Integer Linear Program (ILP). Due to the NP-completeness of the problem, we proposeHolu, a fast heuristic framework that efficiently solves the PD-VPR problem in an online manner. Specifically,Holudecomposes the PD-VPR into two sub-problems and solve them sequentially:i)aVNF placementproblem that consists of mapping the VNFs to PMs using a centrality-based ranking method, andii)aroutingproblem that efficiently splits the delay budget between consecutive VNFs of the SFC, and finds a Delay-Constrained Least-Cost (DCLC) shortest-path through the selected PMs (hosting VNFs) using the Lagrange Relaxation based Aggregated Cost (LARAC) algorithm. Our simulation results indicate thatHoluoutperforms the state-of-the-art algorithms in terms of total power consumption and acceptance rate by 24.7% and 31%, respectively.

The recent trend of network softwarization suggests a radical shift in the implementation of traditional network intelligence. In Software Defined Networking (SDN), for instance, the control plane functions of forwarding devices are outsourced to the controller. Softwarized network components are expected to provide uninterrupted service during long periods of time, which makes them prone to the effects ofsoftware aging, a phenomena that has been observed in operational software systems where the failure rate increases or the performance of the software degrades with the elapsed time since the last restart. The effects of software aging in operational networks are typically mitigated bysoftware rejuvenation, i.e., planned restarts cleaning the internal system state in order to prevent or postpone aging-related failures. This article presentsARES, a three-step methodological framework for the management of the effects of software aging in softwarized networks, applied to the case study of open source SDN orchestration platforms. Using ARES, we demonstrate that software aging is a systematic problem that cannot be neglected in network orchestration systems. It stems not only from aging-related bugs and natural aging due to fragmentation, but also from design choices, e.g., when implementing distributed systems. Measurements for Open Network Operating System (ONOS) and OpenDaylight (ODL) demonstrate how “simple” and common networking tasks let network performance degrade rapidly and even lead to crashes: for instance, adding and removing 300 intents per second in ONOS significantly increases the response time by 50% per day and depletes the memory at the rate of 18GB per day. Moreover, we demonstrate a first rejuvenation approach that can mitigate the effects of aging in ONOS.

This paper presents Chameleon, a cloud network providing both predictable latency and high utilization, typically two conflicting goals, especially in multi-tenant datacenters. Chameleon exploits routing flexibilities available in modern communication networks to dynamically adapt toward the demand, and uses network calculus principles along individual paths. More specifically, Chameleon employs source routing on the "queue-level topology", a network abstraction that accounts for the current states of the network queues and, hence, the different delays of different paths. Chameleon is based on a simple greedy algorithm and can be deployed at the edge; it does not require any modifications of network devices. We implement and evaluate Chameleon in simulations and a real testbed. Compared to state-of-the-art, we find that Chameleon can admit and embed significantly, i.e., up to 15 times more flows, improving network utilization while meeting strict latency guarantees.

To meet their increasingly stringent dependability requirements, communication networks need to be predictable, both in terms of correctness and performance. In principle, Software-Defined Networks (SDN) enable such more predictable networks, however, these networks still depend the underlying switches. This paper presents an empirical study of the predictability of SDN switches. Our extensive benchmarking of seven hardware OpenFlow switches from four different manufacturers raises several concerns regarding the dependability of these switches. We uncover several incorrect and unpredictable behaviors and performance issues. In particular, we identify unpredictable behaviors related to the management of flows and buffers, and observe that existing quality-of-service mechanisms, such as priority queuing, introduce unexpected overheads. The latter, in turn, can lead to violations of latency guarantees. Based on our insights, we discuss first solutions toward more predictable architectures.

A predictable network performance is mission critical for many applications and yet hard to provide due to difficulties in modeling the behavior of the increasingly complex network equipment. This paper studies the problem of providing deterministic latency guarantees in small networks based on low-capacity hardware (e.g., in-cabin and industrial networks): such networks are of increasing importance, need to meet stringent performance requirements, but have hardly been explored so far. Our main contribution is the design, implementation, and evaluation of Loko, a system which provides predictable latency guarantees in programmable networks using low-cost hardware. Loko relies on a novel measurement-based methodology and uses deterministic network calculus to derive a reliable performance model of a given switch. To this end, we also show that state-of-the-art models in the literature like QJump and Silo fall short to model the behavior of such switches, due to incorrect architectural and performance assumptions. As a case study, we implement Loko for the Zodiac FX switch. Our experiments are encouraging: we find that the derived models are indeed accurate, allowing Loko to provide deterministic end-to-end guarantees with low-cost programmable devices.

The routing algorithms used by current operators aim at coping with the demanded QoS requirements while optimizing the use of their network resources. These algorithms rely on the optimal substructure property (OSP), which states that an optimal path contains other optimal paths within it. However, we show that QoS metrics such as queuing delay and buffer consumption do not satisfy this property, which implies that the used algorithms lose their optimality and/or completeness. This negatively impacts the operator economy by causing a waste of network resources and/or violating Service Level Agreements (SLAs). In this paper, we propose a new so-called Mn taxonomy defining new metric classes. An Mn metric corresponds to a metric which requires the knowledge of the n previously traversed edges to compute its value at a given edge. Based on this taxonomy, we present three solutions for solving routing problems with the newly defined classes of metrics. We show that state-of- the-art algorithms based on the OSP indeed lose their original optimality and/or completeness properties while our proposed solutions do not, at the price of an increased computation time.

Industrial control systems are foreseen to operate over hybrid wired/wireless networks. While the controller will be deployed in the wired network, sensors and actuators will be deployed in a wireless sensor network (WSN). To support QoS for control systems, an end-to-end delay bound and a target reliability must be provided in both wireless and wired domains. However, for industrial WSNs, guaranteeing reliability is a challenging task because of low-power communications and the harsh wireless environment. In this work, we present the first QoS framework for arbitrary hybrid wired/wireless networks, which guarantees that the delay bound and the target reliability of each application are provided. As part of this framework, we propose the first reliability-based scheduler for WSN able to achieve a target reliability in the presence of dynamic interference. Simulations of the proposed scheduler prove its suitability in different interference scenarios and motivate further work.

Massive advancements in computing and communication technologies have enabled the ubiquitous presence of interconnected computing devices in all aspects of modern life, forming what is typically referred to as the “Internet of Things”. These major changes could not leave the industrial environment unaffected, with “smart” industrial deployments gradually becoming a reality; a trend that is often referred to as the 4th industrial revolution or Industry 4.0. Nevertheless, the direct interaction of the smart devices with the physical world and their resource constraints, along with the strict performance, security, and reliability requirements of industrial infrastructures, necessitate the adoption of lightweight as well as secure communication mechanisms. Motivated by the above, this paper highlights the Message Queue Telemetry Transport (MQTT) as a lightweight protocol suitable for the industrial domain, presenting a comprehensive evaluation of different security mechanisms that could be used to protect the MQTT-enabled interactions on a real testbed of wireless sensor motes. Moreover, the applicability of the proposed solutions is assessed in the context of a real industrial application, analyzing the network characteristics and requirements of an actual, operating wind park, as a representative use case of industrial networks.

Industrial networks require real-time guarantees for the flows they carry. That is, flows have hard end-to-end delay requirements that have to be deterministically guaranteed. While proprietary extensions of Ethernet have provided solutions, these often require expensive forwarding devices. The rise of software-defined networking (SDN) opens the door to the design of centralized traffic engineering frameworks for providing such real-time guarantees. As part of such a framework, a network model is needed for the computation of worst-case delays and for access control. In this paper, we propose two network models based on network calculus theory for providing deterministic services (DetServ). While our first model, the multi-hop model (MHM), assigns a rate and a buffer budget to each queue in the network, our second model, the threshold-based model (TBM), simply fixes a maximum delay for each queue. Via a packet-level simulation, we confirm that the delay bounds guaranteed by both models are never exceeded and that no packet loss occurs. We further show that the TBM provides more flexibility with respect to the characteristics of the flows to be embedded and that it has the potential of accepting more flows in a given network. Finally, we show that the runtime cost for this increase in flexibility stays reasonable for online request processing in industrial scenarios.