Beyond 5G(B5G) and 6 G networks must support diverse applications with widely varying Quality of Service (QoS) requirements, ranging from ultra-low latency to extremely high throughput. Effectively meeting these demands calls for efficient resource management across applications. However, static resource allocation often struggles to adapt to rapid traffic fluctuations and volatile radio channel conditions, resulting in inefficiencies and performance degradation. This requires adaptive, application-aware control that can respond in real time at fine granularity. In this work, we propose a runtime resource control mechanism that enables application-aware resource allocation based on specific QoS requirements. We focus on dynamic Physical Resource Block (PRB) allocation and evaluate its impact on application-level throughput and latency through experimental validation on a B5G testbed, built using open-source RAN (srsRAN) and core network (Open5GS) implementation. Our results show that dynamic PRB allocation has a significant impact on overall performance. We also evaluate the control loop latency to understand the responsiveness and effectiveness of real-time adaptive resource allocation. This study contributes to the open-source community by advancing autonomous resource management, with the implementation released as open-source to support reproducibility.

5G New Radio (NR) is designed to support diverse services, shifting from a fixed smartphone-centric infrastructure to flexible deployment options tailored for verticals like Ultra-Reliable Low-Latency Communications (URLLC) and Machine-Type Communications (MTC). To optimize the QoS of 5G NR to the service needs, understanding the impact of the introduced configuration parameters is critical. This paper investigates the configuration space of 5G NR using open-source 5G standalone deployments based on OpenAirInterface (OAI) and srsRAN (SRS). We conduct a detailed study on the impact of Next Generation NodeB (gNB) configurations on uplink and downlink throughput and latency, we compare the 5G NR implementations of OAI and SRS, and we investigate reproducibility across different testbeds at the University of Wuerzburg and NTNU. Our datasets are made publicly available.

While the concept of AI4Net has been widely discussed in the past decade and adopted in 5G, its counterpart, Net4AI, has not gained that much attention so far. This is mostly due to the absence of solutions for the network to support AI applications beyond providing the communication infrastructure. In-Network Computing (INC) is a promising paradigm, potentially being integrated into 6G, which opens new solutions for realizing Net4AI. This paper focuses on the specific case of INC-assisted Split-AI. A Neural Network (NN) is split vertically and the executions of some layers of the split NN are offloaded to the entities of an Intelligent 6G User Plane. With the example of INC-assisted Split-AI, we elaborate on the challenges of Net4AI and discuss key requirements for the 6G architecture in terms of novel capabilities and information exchange.

Driven by the development of programmable networking hardware, In-network Computing (INC) has gained a considerable amount of attention in recent years. However, INC has so far barely been studied in the context of mobile networks, despite the vast advantages shown for fixed networks, such as latency or traffic reduction. Motivated by an Augmented Reality (AR) use-case, our work envisions an INC-enabled Intelligent User Plane (IUP) for 6G networks, which allows offloading computational tasks to UP entities having enhanced computational capabilities. The 6G IUP thus helps to keep mobile end-devices lighter and supports meeting the stringent delay requirements of novel applications, such as AR. Besides elaborating on the involved prospects and challenges, we identify key enablers for realizing the INC-enabled IUP. We show that embedding INC into the 6G system entails major changes in the architecture, as compared to the current 5G design.

Scenarios related to 5G and beyond give rise to a high degree of heterogeneity in terms of applications, services, and user expectations as well as more demanding QoS requirements with an end-to-end scope that can cover multiple operator domains. In this work, we design an inter-domain component that addresses the control plane challenges of establishing such end-to-end connectivity in a scalable, efficient, and automated way. Furthermore, we provide insights into operational aspects by investigating to which extent different traffic aggregation mechanisms can be used to benefit from economies of scale while meeting QoS constraints.

Big data analytics platforms have played a critical role in the unprecedented success of data-driven applications. However, real-time and streaming data applications, and recent legislation, e.g., GDPR in Europe, have posed constraints on exchanging and analyzing data, especially personal data, across geographic regions. To address such constraints data has to be processed and analyzed in-situ and aggregated results have to be exchanged among the different sites for further processing. This introduces additional network delays due to the geographic distribution of the sites and potentially affecting the performance of analytics platforms that are designed to operate in datacenters with low network delays. In this paper, we show that the three most popular big data analytics systems (Apache Storm, Apache Spark, and Apache Flink) fail to tolerate round-trip times more than 30 milliseconds even when the input data rate is low. The execution time of distributed big data analytics tasks degrades substantially after this threshold, and some of the systems are more sensitive than others. A closer examination and understanding of the design of these systems show that there is no winner in all wide-area settings. However, we show that it is possible to improve the performance of all these popular big data analytics systems significantly amid even transcontinental delays (where inter-node delay is more than 30 milliseconds) and achieve performance comparable to this within a datacenter for the same load.

Network softwarization is one of the key enablers of the future Internet evolution, also supporting the road from the fifth generation (5G) to the next-generation communication systems, namely 6G, with their main objective of bringing hyper-connected experience to every corner of society.

Monitoring and providing customers with a satisfying Quality of Experience (QoE) is a crucial business incentive for mobile network operators (MNOs). While the MNO is capable of monitoring a vast amount of network-related key performance indicators (KPIs), it typically does not have access to application-specific performance metrics. Among others, this is due to practical obstacles, such as missing standardized interfaces between the network and the application. Existing QoE models allow to map collected KPIs to the user-perceived quality. However, they are not dynamic, cumbersome to obtain, and often rely on application-level information, such as the stalling duration in the case of video streaming. The 5G networking architecture provides new features which can potentially overcome current limitations of in-network QoE monitoring. More specifically, the Application Function (AF) provides a standardized interface for communicating between 5G systems and third parties, such as application providers. The Network Data Analytics Function (NWDAF) is capable of collecting a vast number of network statistics from other 5G network functions and is dedicated to training and deploying Machine Learning (ML) models. This opens new possibilities, unimaginable for earlier mobile network generations, to dynamically learn the relationship between network KPIs and QoE by utilizing ML. Besides elaborating on how the new capabilities introduced with 5G can support an ML-based QoE estimation, we perform a simulation-based feasibility study which evaluates the estimation accuracy of different state-of-the-art regression techniques. In addition, we discuss them with respect to various qualitative aspects from an MNO’s point of view.

Network operators generally aim at providing a good level of satisfaction to their customers. Diverse application demands require the usage of beyond best-effort resource allocation mechanisms, particularly in resource-constrained environments. Such mechanisms introduce additional complexity in the control plane and need to be configured appropriately. Within 5G mobile networks, two new mechanisms for QoS-aware resource allocation are introduced. While QoS Flows enable specifying various QoS profiles on a per flow granularity, slices are dedicated virtual networks, strongly isolated against each other, with aggregated QoS guarantees. It is, however, unclear how QoS Flows and network slicing can optimally be exploited to ensure a high customer QoE while efficiently utilizing the available network resources. We address this research question and evaluate the outlined interplay using the OMNeT++ simulation environment in a multi-application scenario. We show that resource isolation induced by slicing may negatively affect application quality or system utilization, and that this impact can be overcome by finetuning the system parameters.

The Softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and data-center providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on the management of softwarized networks.

Since their first release, 5G systems have been enhanced with Network Data Analytics Functionalities (NWDAF) as well as with the ability to interact with 3rd parties' Application Functions (AFs). Such capabilities enable a variety of potentials, unimaginable for earlier generation networks, notable examples being 5G built-in Machine Learning (ML) mechanisms for QoE estimation, subject of this paper. In this work, an ML-based mechanism for video streaming QoE estimation in 5G networks is presented and evaluated. The mechanism relies on an ML algorithm embedded in NWDAF, the collection of 5G network KPIs, and the collection of QoE information from video streaming service provider, i.e., the 3rd party AF. The mechanism has been evaluated in terms of QoE estimation accuracy against the cost in terms of required input sources and data for the estimation, and its performance has been compared to alternative methodologies not making use of ML. The evaluation, via simulation activity, clearly highlights the benefits of the proposed mechanism. Based on the derived results, the required input sources are ranked with respect to their importance.

As end-user devices often have multiple access networks available, choosing the most suitable network can help to improve application performance and user experience. However, selecting the best access network for HTTP Adaptive Streaming (HAS) is non-trivial, e.g., due to complex interactions between network conditions and the Adaptive Bit-Rate algorithm (ABR), which adapts to network conditions by selecting which video representation to load. In this paper, we propose to use an application-informed approach, Informed Access Network Selection (IANS), to select the most suitable access network for each video segment. We evaluate the impact of IANS on HAS performance in a testbed under a variety of network conditions and using different workloads. We find that IANS improves HAS performance substantially, in particular in cases where the available downstream capacity is low. In the Capacity Decrease scenario, where capacity decreases drastically during the video load, IANS can improve the estimated Mean Opinion Score (MOS) compared to using a single network from 2.1 to 2.8. We compare IANS to MPTCP using the Lowest-RTT-first scheduler, which continues to use a low downstream capacity network, resulting in lower performance. This confirms that IANS can improve video streaming performance.

HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. It enables dynamic adaptation of video quality by splitting a video into small segments and providing multiple quality levels per segment. So far, HAS services typically utilize a fixed segment duration. This reduces the encoding and streaming variability and thus allows a faster encoding of the video content and a reduced prediction complexity for adaptive bit rate algorithms. Due to the content-agnostic placement of I-frames at the beginning of each segment, additional encoding overhead is introduced. In order to mitigate this overhead, variable segment durations, which take encoder placed I-frames into account, have been proposed recently. Hence, a lower number of I-frames is needed, thus achieving a lower video bitrate without quality degradation. While several proposals exploiting variable segment durations exist, no comparative study highlighting the impact of this technique on coding efficiency and adaptive streaming performance has been conducted yet. This paper conducts such a holistic comparison within the adaptive video streaming eco-system. Firstly, it provides a broad investigation of video encoding efficiency for variable segment durations. Secondly, a measurement study evaluates the impact of segment duration variability on the performance of HAS using three adaptation heuristics and the dash.js reference implementation. Our results show that variable segment durations increased the Quality of Experience for 54% of the evaluated streaming sessions, while reducing the overall bitrate by 7% on average.

HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. Splitting the video clip into small segments and providing multiple quality levels per segment allows the client to dynamically adapt the quality to current network conditions. The performance of HAS, and as a consequence the user Quality of Experience (QoE), is influenced by a multitude of parameters. This includes adjustable settings like quality switching thresholds, the initial buffer level, or the maximum buffer, as well as video characteristics like segment duration or the variation of segment sizes along the video. Recently, a couple of analytical models for video streaming have been proposed, allowing to compare these input parameters and derive their impact on QoE-relevant metrics for HAS-based video delivery. The outcome of these models are typically asymptotic probabilities, distribution functions, or centralized and standardized moments. For instance, these models do not yield any temporal information in terms of stalling events or requested video quality. This contradicts to QoE prediction models like P.1203, which compute the QoE based on the chronological sequence of a specific video playback. So far, it is unclear how and to which extent the generalized results of analytical models can be utilized to derive sequence-based QoE values or the QoE distribution for a set of sequences for similar input parameters with stochastic variations. To address this problem, we compare testbed measurements with the output of a GI/GI/1 model with pq-policy and buffer-based quality switching capability to conclude to which extent the results still allow to approximate the video QoE.

The prevailing wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Unfortunately, bugs, misconfigurations, faults or attacks can introduce inconsistencies between the network control and the data plane that can undermine the correct operation at runtime. Through our experiments, we realize that P4 SDNs are no exception, and are prone to similar problems. With the aim to verify the control-data plane inconsistency, we present the design and implementation of P4Consist, a system to detect the inconsistency between control and data plane in P4 SDNs. P4Consist generates active probe-based traffic continuously or periodically as an input to the P4 SDNs to check whether the actual behavior on the data plane corresponds to the expected control plane behavior. In P4Consist, the control plane and the data plane generate independent reports which are later, compared to verify the control-data plane consistency. The previous works in the field of monitoring and verification mostly aim to test the P4 programs through static analysis and thus, are insufficient to verify the network consistency at runtime. Experiments with our prototype implementation of P4Consist are promising and show that P4Consist can verify the control-data plane consistency in the complex datacenter 4-ary fat-tree (20 switches) and multipath grid (4, 9 and 16 switches) topologies with 60k rules per switch within a minimum time of 4 minutes. At the same time, P4Consist scales to multiple source-destination pairs to detect control-data plane inconsistency.

Software Defined Networking aims to separate network control and data plane by moving the control logic from network elements into a logically-centralized controller. Using a well-defined, unified control-channel protocol, such as OpenFlow, the controller is able to configure the forwarding behavior of data plane devices. Here, the OpenFlow protocol is translated to vendor- and device-specific instructions that, for instance, manipulate the flow table entries of a switch. In practice, SDN-enabled switches often feature different hardware capabilities and configurations with respect to the number of flow tables, their implementation, and which kind of data plane features they support. This leads to device heterogeneity within the SDN landscape, thereby obstructing the increased scalability and flexibility promised by the SDN paradigm. To overcome this challenge we propose TableVisor, a transparent proxy-layer for the SDN control channel that enables the flexible abstraction of heterogeneous data plane devices into a single emulated data plane switch. In this paper, we extend our previous work by introducing features to integrate modern P4 devices into an existing SDN environment and perform a detailed performance evaluation to quantify the overhead induced by our approach.

The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Unfortunately, bugs, misconfigurations, faults or attacks can introduce inconsistencies that undermine correct operation. Previous work in this area, however, lacks a holistic methodology to tackle this problem and thus, addresses only certain parts of the problem. Yet, the consistency of the overall system is only as good as its least consistent part. Motivated by an analogy of network consistency checking with program testing, we propose to add active probe-based network state fuzzing to our consistency check repertoire. Hereby, our system, Pazz, combines production traffic with active probes to periodically test if the actual forwarding path and decision elements (on the data plane) correspond to the expected ones (on the control plane). Our insight is that active traffic covers the inconsistency cases beyond the ones identified by passive traffic. Pazz prototype was built and evaluated on topologies of varying scale and complexity. Our results show that Pazz requires minimal network resources to detect persistent data plane faults through fuzzing and localize them quickly while outperforming baseline approaches.

The majority of Internet video traffic today is delivered via HTTP Adaptive Streaming (HAS). Recent studies concluded that pure client-driven HAS adaptation is likely to be sub-optimal, given clients adjust quality based on local feedback. In [1], we introduced a network-assisted streaming architecture (BBGDASH) that provides bounded bitrate guidance for a video client while preserving quality control and adaptation at the client. Although BBGDASH is an efficient approach for video delivery, deploying it in a wireless network environment could result in sub-optimal decisions due to the high fluctuations. To this end, we propose in this paper an intelligent streaming architecture (denoted BBGDASH+), which leverages the power of time series forecasting to allow for an accurate and scalable networkbased guidance. Further, we conduct an initial investigation of parameter settings for the forecasting algorithms in a wireless testbed. Overall, the experimental results indicate the potential of the proposed approach to improve video delivery in wireless network conditions.

Today's end-user devices have multiple access networks available and can achieve better application performance by distributing traffic across access networks. However, matching application traffic to the most suitable access network or bundling them is non-trivial, given varying application needs and network performance characteristics. Therefore, we propose an application-informed approach for access network selection (IANS). Based on the size of a Web resource, we select the better access network in terms of latency and available downstream capacity. We implement IANS within our Socket Intents prototype and evaluate its benefits for Web page loads under a variety of network conditions and for various Web pages. IANS provides the highest speedups for scenarios with asymmetric network conditions and for scenarios with low downstream capacity. Here, IANS improves relevant Web metrics by between 500 and 1000 ms in the median, compared to using the better of the two access networks, and may also outperform MPTCP. This confirms that IANS improves application performance over using a single network and, in several scenarios, even using MPTCP.

Network Functions Virtualization (NFV) is among the latest network revolutions, bringing flexibility and avoiding network ossification. At the same time, all-software NFV implementations on commodity hardware raise performance issues with respect to ASIC solutions. To address these issues, numerous software acceleration frameworks for packet processing have appeared in the last few years. Common among these frameworks is the use of batching techniques. In this context, packets are processed in groups as opposed to individually, which is required at high-speed to minimize the framework overhead, reduce interrupt pressure, and leverage instruction-level cache hits. Whereas several system implementations have been proposed and experimentally benchmarked, the scientific community has so far only to a limited extent attempted to model the system dynamics of modern NFV routers exploiting batching acceleration. In this paper, we fill this gap by proposing a simple generic model for such batching-based mechanisms, which allows a very detailed prediction of highly relevant performance indicators. These include the distribution of the processed batch size as well as queue size, which can be used to identify loss-less operational regimes or quantify the packet loss probability in high-load scenarios. We contrast the model prediction with experimental results gathered in a high-speed testbed including an NFV router, showing that the model not only correctly captures system performance under simple conditions, but also in more realistic scenarios in which traffic is processed by a mixture of functions.

The ongoing network softwarization trend holds the promise to revolutionize network infrastructures by making them more flexible, reconfigurable, portable, and more adaptive than ever. Still, the migration from hard-coded/hard-wired network functions toward their software-programmable counterparts comes along with the need for tailored optimizations and acceleration techniques so as to avoid or at least mitigate the throughput/latency performance degradation with respect to fixed function network elements. The contribution of this paper is twofold. First, we provide a comprehensive overview of the host-based network function virtualization (NFV) ecosystem, covering a broad range of techniques, from low-level hardware acceleration and bump-in-the-wire offloading approaches to high-level software acceleration solutions, including the virtualization technique itself. Second, we derive guidelines regarding the design, development, and operation of NFV-based deployments that meet the flexibility and scalability requirements of modern communication networks.

The softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

With software defined networking (SDN), operators benefit from a higher flexibility, cost efficiency, as well as programmability of their networks. Since modern networks are comprised of a multitude of heterogeneous devices and also include non-SDN legacy devices, network management systems (NMSs) are often used in order to monitor and configure the network. Although both, the SDN controller and the NMS, have a centralized view of the network, they operate at different time scales and deal with information at different levels of granularity. In this work, we investigate the impact on the network performance when an NMS regularly provides information to an SDN controller. To this end, we design, implement, and compare three interaction mechanisms based on the ONOS controller. These represent different trade-offs regarding the complexity of the resulting system and its performance. In addition to the default ONOS controller, we develop two extended versions. One performs hash-based load balancing on equal cost paths while the other utilizes external NMS information via ONOS's intent and annotation framework to optimize control plane decisions. In addition to evaluations that show a significant performance improvement when using the optimized controllers, we present a parameter study that highlights the performance impact of network characteristics like the flow interarrival time, the flow duration, and the number of active flows.

Modern enterprise applications are often designed as distributed architectures, e.g., thin client computing and thus degradations in network related Quality of Service (QoS) parameters may also negatively impact the user-perceived Quality of Experience (QoE) of the application. In this work, we create a model to predict the perceived application quality based on measurements of objective technical parameters. For this, we gathered a data set in a cooperating enterprise over a timespan of nearly three months. As the obtained data set is subject to bias that originates from seasonal effects as well as a limited and predefined set of technical parameters, we further evaluate how to identify segments of the data that lead to misclassification. Last, we quantify the trade-off between the gain in the QoE prediction accuracy and the amount of filtered data.

HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. It enables the dynamic adaptation of video quality by splitting the video clip into small segments and providing multiple quality levels per segment. Current HAS streaming services typically utilize segments of equal durations. However, this leads to video encoding overhead as segments have to start with I -frames, independently of the encoded video content. In this paper we evaluate the prospects of variable segment durations, where video segments are aligned to the video characteristics. We evaluate the reduction of the encoding overhead and investigate its impact on the stalling probability using a theoretical model. It turns out that the variable approach outperforms the fixed approach in 86% of the evaluated cases with respect to video stalls.

The large share of traffic in the Internet generated by video streaming services puts high loads on access and aggregation networks, resulting in high costs for the content delivery infrastructure. To reduce the bandwidth consumed while maintaining a high playback quality, video players use policies that control and limit the buffer level by using thresholds for pausing and continuing the video download. This allows shaping the bandwidth consumed by video streams and limiting the traffic wasted in case of playback abortion. Especially in mobile scenarios, where the throughput can be highly variant, the buffer policy can have a high impact on the probability of interruptions during video playback. To find the optimal setting for the buffer policy in each network condition, the relationship between the parameters of the buffer policy, the network throughput dynamics, and the corresponding video playback behavior needs to be understood. To this end, we model the video buffer as GI/GI/1 queue with pq -policy using discrete-time analysis. By studying the stochastic properties of the buffer-level distribution, we are able to accurately evaluate the impact of network and video bitrate dynamics on the video playback quality based on the buffer policy. We find a fundamental relationship between the bandwidth variation and the expected interarrival time of segments, meaning that overproportionately more bandwidth is necessary to prevent stalling events for high bandwidth variation. The proposed model further allows to optimize the trade-off between the traffic wasted in case of video abortion and video streaming quality experienced by the user.

One of the benefits when network operators adopt the Software Defined Networking (SDN) paradigm is the ability to monitor the traffic in the network without an additional network management system. Usually, SDN controllers utilize OpenFlow statistics messages in order to regularly gather information about all flows in the network. However, using the same polling interval for all flows does not take into account the heterogeneity of real world traffic and thus results in an imbalance between monitoring accuracy and control plane overhead. In particular, frequent querying results in a high resource consumption at the controller. This work proposes a Selective Flow Monitoring (SFM) mechanism that allows administrators to classify flows according to their individual requirements in terms of monitoring frequency, e.g., less frequent polling of elephant flows and frequent polling of QoS sensitive VoIP connections. We compare the performance of the SFM mechanism with the default monitoring scheme in a testbed featuring the Open Network Operating System (ONOS) controller. In this context, the CPU utilization of the controller is used as performance indicator. After identifying relevant influence factors like the number of flows and switches in the network, we investigate the viability of the approaches in different scenarios. Finally, we provide guidelines regarding their choice.

To cope with the increasing demands of mobile devices and the limited capacity of cellular networks, mobile connections are offloaded to WiFi. The access capacity is further increased by aggregating backhaul bandwidth of WiFi access links. To analyze the performance of aggregated access links we develop a model for two and more cooperating systems sharing capacities using an offloading scheme. The state probabilities of the different cooperating systems in the analytic model are determined by a fixed point iterative procedure. By investigating an inner and outer composite system we are able to analyze the system in imbalanced load conditions where the system reaches its full potential utilizing spare bandwidth. To evaluate the robustness of the system against users that try to exploit the system, the bandwidth received by prioritized users is quantified.

Software-defined Networking (SDN) provides an increased flexibility and cost savings by separating the data from the control plane. Despite these benefits, this separation also results in a greater attack surface as new devices and protocols are deployed. OpenFlow is one of these protocols and enables the communication between the switch and the controller. Ideally this connection takes place over an encrypted TLS channel, but as this feature is marked optional, it is not supported by all devices. This allows an attacker to eavesdrop and alter the communication, hence resulting in a comprised network. In this work, we demonstrate a new approach for authentication based on device fingerprinting to enhance the security in scenarios, where cryptographic mechanisms are unavailable.

This paper details a measurement study on the impact of different HTTP-based application layer protocols, namely HTTP/1, HTTP/2 and QUIC, on video streaming performance. In this context we evaluate the influence on the initial delay until video playout is started using the live version of the YouTube platform. Furthermore, we evaluate how different network parameters, i.e. bandwidth, RTTs and packet loss influence the different protocols. This work presents an overview over the characteristics of the compared protocols and presents a detailed measurement methodology on how the data has been obtained. Finally, the observed data is evaluated in the context of YouTube video streaming.

A key requirement for Active and Assisted Living (AAL) environments is the exchange of data between different communication endpoints to support wide range of services and applications. Used communication protocols need to support the bidirectional flow of information and have to be optimized with regard to security or latency constraints. To address these issues, RESTful approaches have recently gained much attention from the community. In this context, different application layer transport protocols can be used to realize the required data exchange. Besides HTTP/1.1, developed and standardized in the 1990s, new protocols like HTTP/2 and the QUIC transfer protocol my be suitable candidates. The impact of the different protocols on the overall performance for web and AAL services is still an open research question. This paper narrows this gap by conducting a measurement-based comparison of the three described protocols with regard to their performance in terms of web page loading times for Google web services.

Modern Software Defined Networking (SDN) applications rely on sophisticated packet processing. However, there is a mismatch between control plane requirements and data plane capabilities caused by increasing hardware heterogeneity. To overcome this challenge, we propose TableVisor, a proxy-layer for the OpenFlow control channel that enables the flexible and scalable abstraction of multiple physical devices into one emulated data plane switch that meets the requirements of the control plane application. TableVisor registers with the SDN controller as a single switch with use-case specific capabilities. It translates the instructions and rules from the control application towards the appropriate physical device where they are executed. In this paper, we present the updated architecture and functionality of TableVisor as well as first evaluation results based on testbed experiments.

Similar to many modern applications, enterprise applications like SAP are often implemented in a distributed fashion and consequently suffer from network degradations resulting in impairments like increased loading delays. While the influence of these impairments on the perceived quality of users is well researched for consumer applications and network services, their impact in a business environment is still unclear. To address this gap we develop a non-intrusive software tool for continuously collecting subjective ratings on the performance of an enterprise application from a large number of employees. Based on the feedback from two field studies in a company we briefly discuss challenges of QoE monitoring in the context of enterprises. As a first step towards building QoE models, we combine the subjective ratings with technical monitoring data and observer a negative correlation between the user satisfaction and the overall load of the server infrastructure.

Software defined networking (SDN) has emerged as a promising networking paradigm overcoming various drawbacks of current communication networks.The control and data plane of switching devices is decoupled and control functions are centralized at the network controller. In SDN, each new flow introduces additional signaling traffic between the switch and the controller. Based on this traffic, rules are created in the flow table of the switch, which specify the forwarding behavior. To avoid table overflows, unused entries are removed after a predefined time-out period. Given a specific traffic mix, the choice of this time-out period affects the tradeoff between signaling rate and table occupancy. As a result, network operators have to adjust this parameter to enable a smooth and efficient network operation. Due to the complexity of this problem caused by the various traffic flows in a network, a suitable abstraction is necessary in order to derive valid parameter values in time. The contribution of this paper is threefold. First, we formulate a simple analytical model that allows optimizing the network performance with respect to the table occupancy and the signaling rate. Second, we validate the model by means of simulation. Third, we illustrate the impact of the time-out period on the signaling traffic and the flow table occupancy for different data-plane traffic mixes and characteristics. This includes scenarios with single application instances, as well as multiple application instances of different application types in an SDN-enabled network.

This article investigates several network-assisted streaming approaches that rely on active cooperation between video streaming applications and the network. We build a Video Control Plane that enforces Video Quality Fairness among concurrent video flows generated by heterogeneous client devices. For this purpose, a max-min fairness optimization problem is solved at runtime. We compare two approaches to actuate the optimal solution in an Software Defined Networking network: The first one allocates network bandwidth slices to video flows, and the second one guides video players in the video bitrate selection. We assess performance through several QoE-related metrics, such as Video Quality Fairness, video quality, and switching frequency. The impact of client-side adaptation algorithms is also investigated.

Software Defined Networking (SDN) is a major paradigm in the field of current communication networks. SDN is used as the basis of many new networks although few performance models are available in the literature, and the majority of performance evaluations are based primarily on practical measurements. To fill this gap, we develop an analytical model to assess SDN control plane traffic as well as the occupancy of the flow table of an SDN switch. The contribution of this work is the formulation of the model for the performance-decisive parameters control-plane traffic and flow table occupancy and the application of the model for different data plane traffic characteristics. In the end, there is a discussion about the setting of time-out values for storing flow entries in the switch flow table depending on the traffic characteristics in the data plane. The trade-off between the signaling traffic in the control plane and the occupancy of the flow table is discussed to minimize both.

In this paper we investigate several network-assisted streaming approaches which rely on active cooperation between video streaming applications and the network. We build a Video Control Plane which enforces Video Quality Fairness among concurrent video flows generated by heterogeneous client devices. To the purpose, a max-min fairness optimization problem is solved at run-time. We compare two approaches to actuate the optimal solution in an SDN network: the first one allocating network bandwidth slices to video flows, the second one guiding video players in the video bitrate selection. Performance is assessed through several QoE-related metrics, such as Video Quality Fairness, video quality, and switching frequency. The impact of client-side adaptation algorithms is also investigated.

Delay effects can impact the Quality of Experience of interactive systems, which motivates research assessing delay impairments, mostly for web based systems. Current studies follow individual methodologies and typically assesses individual and custom-made web pages, whose construction requires expert knowledge in web technologies. Further, a range of native, non-web applications cannot be easily modified for delay studies. Thus, a generalized methodology for assessing delay impacts for a broad range of applications that is accessible to researchers without (web) development expertise is still missing. This paper contributes to this open problem by i) presenting a new methodology for reproducible delay assessments in a broad class of systems and ii) presenting an open-source implementation to be used by the community. This methodology particularly aims at making delay assessment available to a broad range of researchers by avoiding programming skills and thus by lowering the barrier for setting-up delay assessments.

Network Functions Virtualization (NFV) is a concept that aims at providing network operators with benefits in terms of cost, flexibility, and vendor independence by utilizing virtualization techniques to run network functions as software on commercial off-the-shelf (COTS) hardware. In contrast, prior solutions rely on specialized hardware for each function. Performance evaluation of such systems usually requires a dedicated testbed for each individual component. Rather than analyzing these proprietary black-box components, Virtualized Network Functions (VNFs) are pieces of software that run on COTS hardware and whose properties can be investigated in a generic testbed. However, depending on the underlying hardware, operating system, and implementation, VNFs might behave differently. Therefore, mechanisms for the performance evaluation of VNFs should be similar to benchmarking of software, where different implementations are compared by applying them to predefined test cases and scenarios. This work presents a first step towards a benchmarking framework for VNFs. Given two different implementations of a VNF that acts as LTE Serving Gateway (SGW), influence factors and key performance indicators are identified and a comparison between the two mechanisms is drawn.

Softwarization and cloudification of networks through software defined networking and network functions virtualisation promise a new degree of flexibility and agility. By moving logic from device firmware into software applications and applying software development mechanisms, innovation can be introduced with less effort. Concrete ways how to operate and orchestrate such systems are not yet defined. The process of making changes to a controller software or a virtualized network function in a production network without the risk of network disruption is not covered by literature. Complexity of systems brings the risk of unexpected side-effects and has so long been a show-stopper for administrators applying changes to networking devices. This paper suggests the adaption of the successful concept of continuous delivery into the software defined networking world. Test-driven development and automatic acceptance tests demonstrate that the software engineering community already found ways to ensure that changes do not break. Applied to network engineering, the adaption of continuous delivery can be seen as an enabler for risk-free and frequent changes in production infrastructure through push button deployments.

Software Defined Networking (SDN) introduces the concept of logically-centralized controllers in charge of managing the forwarding behavior of network elements. The new possibilities enabled through the centralization of control logic come with a certain risk: The controller might become a performance bottleneck. Therefore, ensuring sufficient controller performance is one of the crucial tasks prior to a successful SDN deployment. Furthermore, fine-grained traffic engineering, e.g., to achieve higher link utilization, results in a higher frequency of requests that are sent to the controller, which leads to an increased controller load. It is therefore important to analyze the capabilities of SDN controllers prior to deployment. This paper investigates two software implementations, the OpenDaylight and Ryu controllers. The control message throughput of different controllers has been studied several times already; however, it is not yet known what influence the number and topology of connected switches have. This paper investigates this influence in detail for a fat-tree data center topology and a WAN topology as well as 261 topologies with varying characteristics from the Internet Topology Zoo.

Software Defined Networking (SDN) marks a paradigm shift towards an externalized and logically centralized network control plane. A particularly important task in SDN architectures is that of controller placement, i.e., the positioning of a limited number of resources within a network to meet various requirements. These requirements range from latency constraints to failure tolerance and load balancing. In most scenarios, at least some of these objectives are competing, thus no single best placement is available and decision makers need to find a balanced trade-off. This work presents POCO, a framework for Pareto-based Optimal COntroller placement that provides operators with Pareto optimal placements with respect to different performance metrics. In its default configuration, POCO performs an exhaustive evaluation of all possible placements. While this is practically feasible for small and medium sized networks, realistic time and resource constraints call for an alternative in the context of large scale networks or dynamic networks whose properties change over time. For these scenarios, the POCO toolset is extended by a heuristic approach that is less accurate, but yields faster computation times. An evaluation of this heuristic is performed on a collection of real world network topologies from the Internet Topology Zoo. Utilizing a measure for quantifying the error introduced by the heuristic approach allows an analysis of the resulting trade-off between time and accuracy. Additionally, the proposed methods can be extended to solve similar virtual functions placement problems which appear in the context of Network Functions Virtualization (NFV).

Recently, Software Defined Networking (SDN) has gained a lot of attention, even for the use in core communication networks. When deploying SDN in large core networks, the number and location of controllers must be carefully planned. A first study on this topic by Heller et al. [3] was followed by more detailed publications that included resilience or dynamic controller provisioning. In our previous work [2], we provide an overview over related work and include different resilience issues in the controller placement process. We argued that it is not sufficient to look only at node-to-controller latencies but a controller placement should also fulfill certain resilience constraints especially for the control plane.

Today's Internet does not provide an exchange of information between applications and networks, which may result in poor application performance. Concepts such as application-aware networking or network-aware application programming try to overcome these limitations. The introduction of Software-Defined Networking (SDN) opens a path towards the realization of an enhanced interaction between networks and applications. Hence, a more dynamic and demand-based allocation of network resources to heterogeneous applications can be realized. The implementation of the resource management action, however, may have an impact on the data transport and application quality. This paper summarizes resource management mechanisms provided by current SDN approaches based on OpenFlow and exemplary evaluates implementation prospects and challenges.

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This paper addresses the challenge of assessing and modeling Quality of Experience (QoE) for online video services that are based on TCP-streaming. We present a dedicated QoE model for You Tube that takes into account the key influence factors (such as stalling events caused by network bottlenecks) that shape quality perception of this service. As second contribution, we propose a generic subjective QoE assessment methodology for multimedia applications (like online video) that is based on crowd sourcing - a highly cost-efficient, fast and flexible way of conducting user experiments. We demonstrate how our approach successfully leverages the inherent strengths of crowd sourcing while addressing critical aspects such as the reliability of the experimental data obtained. Our results suggest that, crowd sourcing is a highly effective QoE assessment method not only for online video, but also for a wide range of other current and future Internet applications.

P2P Video-on-Demand (VoD) based on Scalable Video Coding (SVC) (the scalable extension of the H.264/AVC standard) is gaining momentum in the research community, as it provides elegant adaptation to heterogeneous resources and network dynamics. The major question is, how do the adaptation algorithms and designs affect the overall perceived performance of the system? Better yet, how can the performance of an SVC-based VoD system be defined? This paper explores the impact and trade-offs of SVC-based quality adaptation with focus on the SVC layer selection algorithms, which are performed at different streaming stages. We carry out extensive experiments to evaluate the performance in terms of session quality (start-up delay, video stalls) and delivered SVC video quality (layer switches, received layers), and find out that these two metrics exhibit a trade-off. Our analysis and conclusions give multimedia providers insights on how to design and fine-tune their VoD system in order to achieve best performance.

P2P streaming has attracted much attention recently with promises for higher revenues and better load distribution. Still, the majority of P2P video streaming systems today employ the one-size-fits-all concept where the same video bit-rate is offered to all users. Here the promising H.264/Scalable Video Coding (SVC) standard is seen as a necessity in not only supporting heterogeneous resources, but also in reducing the impact of P2P dynamics on the perceived Quality-of-Experience (QoE). In this demonstration we present our streaming application that uses SVC to adapt to different user requirements and resources. The application employs a novel QoE- aware layer selection algorithm that maximizes flexibility through SVC while taking impact on QoE into consideration.

P2P Streaming has attracted much attention recently with promises for more revenues and better load distribution. In parallel, social networking has changed how people interact using the web. One interesting use-case for next generation IPTV is Social TV. In such a system, users are able to watch some media stream and interact with each other at the same time. While deploying P2P Social TV, one inherent problem in P2P streaming systems remains, how to incite users to contribute. In this demonstration we show how social networks can be used to build new incentive mechanisms. Rather than making social relations a mere addition, we build our streaming system on top of a user's social network. This design greatly simplifies the system and requires no further entities for management. In this demonstration we present the first version of StreamSocial that, based on a plug-in based design, allows users to stream videos while performing social interactions.

Introducing location-awareness in overlay networks allows for a higher efficiency of the offered services, e.g., shorter search times or higher throughput. This is especially important for business applications supported by overlays. In this paper, we will illustrate how such an increase in efficiency causes congestion in an otherwise well-dimensioned locality-aware overlay using a Distributed Hash Table (DHT). In particular, we will use a Vertical Handover (VHO) support system using a Pastry substrate as an example. We will describe the occuring congestion problem and identify the underlying reasons. Thus, we describe the basic trade-off between efficiency and load-inequality. Furthermore, we introduce a solution for the specific issue observed, and conduct a performance evaluation identifying the major influence factors and optimization potential for future work.