Managing and scaling virtual network function(VNF) service chains require the collection and analysis ofnetwork statistics and states in real time. Existing networkfunction virtualization (NFV) monitoring frameworks either donot have the capabilities to express the range of telemetryitems needed to perform management or do not scale tolarge traffic volumes and rates. We present IntOpt, a scalableand expressive telemetry system designed for flexible VNFservice chain network monitoring using active probing. IntOptallows to specify monitoring requirements for individual servicechain, which are mapped to telemetry item collection jobsthat fetch the required telemetry items from P4 (programmingprotocol-independent packet processors) programmable dataplaneelements. In our approach, the SDN controller creates theminimal number of monitoring flows to monitor the deployedservice chains as per their telemetry demands in the network.We propose a simulated annealing based random greedy metaheuristic(SARG) to minimize the overhead due to activeprobing and collection of telemetry items. Using P4-FPGA, webenchmark the overhead for telemetry collection and compareour simulated annealing based approach with a na¨ıve approachwhile optimally deploying telemetry collection probes. Ournumerical evaluation shows that the proposed approach canreduce the monitoring overhead by 39% and the total delays by57%. Such optimization may as well enable existing expressivemonitoring frameworks to scale for larger real-time networks.

In 5G, network densification is a major concern for operators. When a massive amount of small cells are deployed, the backhaul capacity is crucial and researchers are exploring the use of high frequency bands such as 28, 60 or even 140 GHz because of the large portion of spectrum that is available. Unfortunately, such mmWave links frequently change their capacity due to blocking and weather phenomena which makes it challenging to design stable multihop backhaul networks using those frequency bands. In this paper, we investigate the use of Software Defined Networking (SDN) for the operation and control of wireless backhaul networks. We explore different ways how SDN resiliency mechanisms such as FastFailover Groups can be used to mitigate disruptive connectivity in the multihop operation due to mmWave links frequently failing. We also demonstrate a clear benefit for using low frequency assist mode, where the small cell has an additional stable LTE uplink to the eNB that is used should the mmWave backhaul links fail. Our experiments using a network emulator show that such SDN based local repair mechanisms can significantly reduce the packet loss rate inside the mmWave backhaul mesh, which can be further reduced with an LTE assisted Failover.

Providing network reliability as well as low and predictable latency is important especially for Industrial Automation and Control Networks. However, diagnosing link status from the control plane has high latency and overhead. In addition, the communication with the industrial controller may impose additional network latency. We present FastReact - a system enabling In-Network monitoring, control and caching for Industrial Automation and Control Networks. FastReact outsources simple monitoring and control actions to evolving programmable data planes using the P4 language. As instructed by the Industrial Controller through a Northbound API, the SDN controller composes control actions using Boolean Logic which are then installed in the data plane. The data plane parses and caches sensor values and performs simple calculations on them which are connected to fast control actions that are executed locally. For resiliency, FastReact monitors liveness and response of sensors/actuators and performs a fast local link repair in the data plane if a link failure is detected. Our testbed measurement show that FastReact can reduce the sensor/actuator delay while being resilient against several failure events.

Software Defined Networking (SDN) is currently a hot topic in the area of Datacenter Networking or Enterprise Networks as it has the promise to radically simplify network management and operation. However, it has not been considered so far as a promising candidate for Industrial Control Networks mainly because of the deterministic performance requirements and the dedicated design of those networks to fulfil strict performance guarantees. In this paper, we propose a resilient SDN based architecture for Industrial Control Networks and show that by combining several SDN based fast failover technologies using per-link Bidirectional Forwarding Detection (BFD), preconfigured primary and backup paths and flexible packet duplication orchestrated by an SDN controller, we can reduce significantly the control latency and provide more stringent performance guarantees even under lossy and failing links.

In-Band Network Telemetry (INT) is a novel framework for collecting telemetry items and switch internal state information from the data plane at line rate. With the suppor programmable data planes and programming language P4,switches parse telemetry instruction headers and determine which telemetry items to attach using custom metadata. At the network edge, telemetry information is removed and the original packets are forwarded while telemetry reports are sent to a distributed stream processor for further processing by a network monitoring platform. In order to avoid excessive load on the stream processor, telemetry items should not be sent for each individual packet but rather when certain events are triggered. In this paper, we develop a programmable INT event detection mechanism in P4 that allows customization of which events to report to the monitoring system, on a per-flow basis, from the control plane. At the stream processor, we implement a fast INT report collector using the kernel bypass technique AF XDP, which parses telemetry reports and streams them to a distributed Kafka cluster, which can apply machine learning, visualization and further monitoring tasks. In our evaluation, we use realworld traces from different data center workloads and show that our approach is highly scalable and significantly reduces the network overhead and stream processor load due to effective event pre-filtering inside the switch data plane. While the INT report collector can process around 3 Mpps telemetry reports per core, using event pre-filtering increases the capacity by 10-15x.