This paper studies the impact of tunable parametersin the NB-IoT stack on the energy consumption of a user equipment(UE), e.g., a wireless sensor. NB-IoT is designed to enablemassive machine-type communications for UE while providing abattery lifetime of up to 10 years. To save battery power, most oftime the UE is in dormant state and unreachable. Still, duringthe CONNECTED and IDLE state, correct tuning of criticalparameters, like Discontinuous reception (DRX), and extendedDiscontinuous reception (eDRX), respectively, are essential to savebattery power. Moreover, the DRX and eDRX actions relate tovarious parameters which are needed to be tuned in order toachieve a required UE battery lifetime. The objective of thispaper is to observe the influence of an appropriate tuning ofthese parameters to reduce the risk of an early battery drainage

We present the latency-aware multipath schedulerZQTRTT that takes advantage of the multipath opportunities ininformation-centric networking. The goal of the scheduler is touse the (single) lowest latency path for transaction-oriented flows,and use multiple paths for bulk data flows. A new estimatorcalled zero queue time ratio is used for scheduling over multiplepaths. The objective is to distribute the flow over the paths sothat the zero queue time ratio is equal on the paths, that is,so that each path is ‘pushed’ equally hard by the flow withoutcreating unwanted queueing. We make an initial evaluation usingsimulation that shows that the scheduler meets our objectives.

The demand for mobile communication is continuously increasing, and mobile devices are now the communication device of choice for many people. To guarantee connectivity and performance, mobile devices are typically equipped with multiple interfaces. To this end, exploiting multiple available interfaces is also a crucial aspect of the upcoming 5G standard for reducing costs, easing network management, and providing a good user experience. Multi-path protocols, such as multi-path TCP (MPTCP), can be used to provide performance optimization through load-balancing and resilience to coverage drops and link failures, however, they do not automatically guarantee better performance. For instance, low-latency communication has been proven hard to achieve when a device has network interfaces with asymmetric capacity and delay (e.g., LTE and WLAN). For multi-path communication, the data scheduler is vital to provide low latency, since it decides over which network interface to send individual data segments. In this paper, we focus on the MPTCP scheduler with the goal of providing a good user experience for latency-sensitive applications when interface quality is asymmetric. After an initial assessment of existing scheduling algorithms, we present two novel scheduling techniques: the block estimation (BLEST) scheduler and the shortest transmission time first (STTF) scheduler. BLEST and STTF are compared with existing schedulers in both emulated and real-world environments and are shown to reduce web object transmission times with up to 51% and provide 45% faster communication for interactive applications, compared with MPTCP's default scheduler.

Protection and automation in a smart grid environmentoften have stringent real-time communication requirementsbetween devices within a substation as well as between distantlylocated substations. The Generic Object Oriented SubstationEvent (GOOSE) messaging service has been proposed to achievethis goal as it allows to transfer time-critical information within afew milliseconds. However, the transmission of GOOSE messagesare often limited to a small Local Area Network (LAN).In this paper, we propose the use of the fifth generation ofmobile networks (5G) as a means to transport GOOSE messagesin a large scale smart grid environment. The end-to-end delay ismeasured between GOOSE devices over an 5G network with thefocus on the core network using the Open5GCore platform in alab environment. Although there is a lack of a real radio accessnetwork, the experimental results confirm that the delay withinthe rest of the 5G network is small enough for it to be feasiblefor inter-substation GOOSE transmissions.

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.

An increasing number of cellular congestion controlalgorithms (CCAs) are relying on measurements of the deliveryrate observed at the receiver. Accordingly, early detection ofchanges in the receiver’s rate would improve the performanceof such algorithms. Rate measurements over short time intervalscould allow fast detection of change in the rate observed bythe upper layers of a cellular receiver. However, upper layerrate measurements for cellular receivers over a short time scaleproduce unreliable results due to the effect of underlying lowerlayer mechanisms. In this paper, we introduce a rate estimationapproach that reduces the variability observed in short timescale receiver rate measurements and allows faster rate changedetection.

An increasing number of cellular congestion controlalgorithms (CCAs) are relying on measurements of the deliveryrate observed at the receiver. Accordingly, early detection ofchanges in the receiver’s rate would improve the performanceof such algorithms. Rate measurements over short time intervalscould allow fast detection of change in the rate observed bythe upper layers of a cellular receiver. However, for cellularreceivers, upper-layer rate measurements over short time scalesproduce unreliable results due to the effect of underlying lowerlayer mechanisms such as scheduling and retransmissions. In thispaper, we introduce a rate estimation approach that reduces thevariability observed in short time scale receiver rate measurementsand allows faster rate change detection. We also integratean adaptive mechanism to improve online measurements overdifferent time scales.

Internet services such as virtual reality, interactive cloud applications, and online gaming, have a strict quality of service requirements (e.g., low-latency). However, the current Internet is not able to satisfy the low-latency requirements of these applications. This as the standard TCP induces high queuing delays when used by capacity-seeking traffic, which in turn results in unpredictable latency. The Low Latency Low Loss Scalable throughput (L4S) architecture aims to address this problem by combining scalable congestion controls (e.g., DCTCP) with early congestion signaling from the network. For incremental deployment, the L4S architecture defines a Dual Queue Coupled AQM that enables the safe coexistence of scalable and classic (e.g., Reno, Cubic, etc.) flows on the global Internet. The DualPI2 AQM is a Linux kernel implementation of a Dual Queue Coupled AQM. In this paper, we benchmark the DualPI2 AQM to validate experimental result(s) reported in previous works that demonstrate the coexistence of scalable and classic congestion controls, and its low-latency service. Our results validate the coexistence of scalable and classic flows using DualPI2 single queue AQM while the result with dual queue shows neither rate nor window fairness between the flows.

The fast growth of Internet traffic, the growingimportance of cellular accesses and the escalating competitionbetween content providers and network operators result in agrowing interest in improving network performance and userexperience. In terms of network transport, different solutionsranging from tuning TCP to installing middleboxes are applied.It turns out, however, that the practical results sometimes aredisappointing and we believe that poor testing is one of thereasons for this. Indeed, many cases in the literature limittesting to the simple and rare use case of a single file download,while common and complex use cases like web browsing oftenare ignored or modelled only by considering smaller files. Tofacilitate better testing, we present a set of metrics by whichthe complexity around web pages can be characterised andthe potential for different optimisations can be estimated. Wealso derive numerical values of these metrics for a small set ofpopular web pages and study similarities and differences betweenpages with the same kind of content (newspapers, e-commerceand video) and between pages designed for the same platform(computer and smartphone).