The InterPlanetary File System (IPFS) offers a decentralized, content-addressable storage model. Operating on today’s Internet, IPFS inherits the fundamental security and performance limitations of the Border Gateway Protocol (BGP). SCION, a next-generation Internet architecture, provides a compelling solution by introducing path-aware networking (PAN), which grants endhosts fine-grained path control, multipath capabilities, and a robust security foundation. In this work, we enhance IPFS with a native SCION integration. We engineer a multipath transport into libp2p, the networking stack of IPFS, and leverage SCION’s Control-Plane Public Key Infrastructure (CP-PKI) for cryptographic peer validation. Our comprehensive evaluation across a testbed and a production network demonstrates that our approach hardens IPFS against routing-based attacks while reducing content retrieval times by up to 2.9x. These findings validate pathaware networking as a potent foundation for securing and accelerating decentralized systems.

The SCION Next-Generation Network (NGN) architecture has expanded steadily since 2017, with today 20+ ISPs offering SCION connectivity. In production, IP-to-SCION-to-IP translation by SCION-IP-Gateways (SIGs) is used, such that applications are unaware of the NGN communication. To accelerate innovation and deployments, our aim is to increase the number of native SCION use cases, where the application is fully SCION-aware and optimizes communication across all path choices offered by the network. We set out to achieve two core objectives: (1) facilitating simple native connectivity for applications, and (2) enhancing the scalability of SCION deployment at academic sites.

In-band network telemetry (INT) is a powerful tool for gathering status information from network components in a distributed and timely way. Until now, INT has mostly been deployed in data center environments or single operator WANs, because it lacks mechanisms for authentication and is not widely standardized. SCION is a novel, path-based Internet architecture providing strong resilience and security properties. In this paper, we propose Inter-domain In-band Network Telemetry (ID-INT) as a protocol extension for SCION. ID-INT leverages SCION’s public key infrastructure to authenticate telemetry data while augmenting SCION’s end host path control with real-time network information. Promising applications of ID-INT include intra-AS path tracing, congestion control, SLA verification, and carbon-aware routing. We implement ID-INT in the open-source SCION stack and provide a proof of concept for an AS-hosted telemetry collection service. We show that cryptographically authenticated ID-INT can be fully implemented in the SCION router’s fast-path with no measurable impact on router performance. If optional encryption is employed in addition to authentication, router throughput drops by no more than 13% even if every packet carries telemetry.

Today's BGP-based Internet offers almost no control over forwarding paths to end users. Few opportunities exist to influence paths on an application-by-application basis, e.g., the traffic class field in the IP header, but that is usually dropped when a packet reaches the first ISP. In contrast, path-aware networking (PAN) as implemented in the SCION Internet architecture offers a wealth of path choices from which applications can pick desirable subsets. However, only a few dozen applications support SCION natively today. Instead, SCION deployments connect legacy applications through SCIONIP Gateways (SIGs) that provide an overlay IP network on top of SCION. In this work, we introduce an alternative SCION to legacyIP compatibility mechanism suitable for P4-programmable hardware that can replace the SIG, called SCION-IP translation. The SCION-IP Translator addresses two issues of the SIG. First, the SIG requires fragmentation and reassembly of the underlying IP packets - a process not easily done in P4. More importantly, SIGs form an overlay network on top of SCION. Native SCION applications cannot communicate with legacy applications. Thus, the SIG hinders native application development. The main innovation of the SCION-IP Translator is that it translates headers directly without introducing another (tunneling) layer to the network stack. We prototype a SCION-IP Translator using Intel Tofino switches of the first and second generation and provide insights from deploying SCION-IP translation in a university network participating in the SCION Education, Research and Academic network (SCIERA).

Today's Internet frequently suffers from unreliable packet forwarding and security vulnerabilities, while it largely depends on the Border Gateway Protocol (BGP) that obscures routing complexities from end-hosts. Path-Aware Networking (PAN) is a novel paradigm that aims to overcome BGP's limitations by offering end-hosts control over packet routing. However, an optimal path choice based on PAN requires realtime performance data about the different path options. Programmable data planes and Software-Defined Networking (SDN) enable to add real-time telemetry data to user packets. However, existing implementations are limited to single administrative domains, lacking the security and privacy required for interdomain telemetry. This paper proposes the first hardware implementation of Inter-Domain In-band Network Telemetry (ID-INT), extending the P4-based SCION border router targeting Tofino 2. Leveraging SCION's path-aware features and robust security mechanisms, like the DRKey system for secure key derivation, our ID-INT implementation on Tofino ensures telemetry data authenticity and integrity. We demonstrate the benefits of ID-INT on Tofino by evaluating the Deadline-aware Multipath Transport Protocol (DMTP) as an example application utilizing ID-INT data to improve its responsiveness to network conditions. Our testbed experiments show that ID-INT enables DMTP to dynamically adjust the sending rate based on instantaneous queue length data, highlighting ID-INT's practical benefits in enhancing an application's performance.

There has been a lot of effort in improving coupled congestion control in multipath scenarios by locating shared bottlenecks. However, without transparency and control in the network, these approaches come with high complexity potentially impacting performance in uncongested conditions. At the same time, Path-Aware Networking provides new opportunities to applications and protocols to identify shared bottlenecks more effectively. In this work, we propose UMCC, an uncoupled multipath congestion control algorithm based on SCION that identifies and locates shared bottlenecks. We demonstrate the fairness of UMCC by comparing it with MPTCP at a shared bottleneck. Our results show that UMCC can precisely locate the exact bottleneck up to 70% of the time and increases throughput by 35% by avoiding shared bottlenecks.

SCION is a next-generation Internet architecture enabling path-awareness for end-host applications. Contrary to today's single-path Internet, where paths are chosen based on BGP, path-awareness is a novel property allowing applications to select paths for their traffic based on specific goals, e.g. maximizing the bandwidth or minimizing the latency. To this end, SCION offers a list of path alternatives along with static path properties based on which SCION end hosts can select their paths. However, since the properties of these paths are often dynamically changing, SCION end hosts also need to rely on self-performed probes for their path selection. Such probes are usually time consuming and create a lot of overhead. In this paper, we therefore design and implement a system for sharing dynamic path properties in SCION-based networks, enabling Applications to select paths in a more informed manner. Our implemented Path Oracle derives to-be-expected performance metrics of paths from donated end-to-end path performance data. For this purpose, the Path Oracle offers two interfaces to applications, one for querying path scores and one for donating measured metrics. The Path Oracle tracks the currently achievable throughput on paths by maintaining a network view on a link level, enabling to derive scores for paths without previously received data donations. The service incorporates donated measurements of applications performing network-bound input or output into its network view. The implemented Path Oracle and throughput service were evaluated in the global SCIONLab test network. Here the Path Oracle was able to successfully determine paths offering the best throughput. In addition we conducted further experiments to evaluate the system under a range of different simulated network configurations and high load scenarios, achieving significant performance gains.

Distributed denial-of-service (DDoS) attacks are a growing threat in the Internet. For example, the increasing number of small low-powered devices participating in the Internet of Things can be hijacked by botnets and used to perpetrate powerful DDoS attacks if they are not secured correctly. Different works have already investigated how such attacks may be detected using efficient probabilistic data structures known as “sketches”. Additionally, software-defined networking and data plane programmability have created new opportunities to develop new DDoS attack detection approaches that are performed entirely by the data plane. In this work, we specifically investigate an approach that uses a combination of HYPERLOGLOG and COUNTMIN sketches to detect DDoS attacks in P4-programmable network switches. We present an implementation of this approach for a software-based P4 switch and evaluate its accuracy, achieved detection latencies and its effect on throughput in an emulated environment. Our implementation achieves detection latencies as low as 0.97 s. The impact on switch throughput is limited to approximately 10% if the final detection step is offloaded to the controller. We explore the impact of different sketch sizes on detection accuracy and find a trade-off between accuracy and memory requirements.

In the current Internet, BGP is used by network operators to determine the inter-domain forwarding paths for traffic based on their local view of the network. For endhosts however, there is no way to a) influence this path and b) benefit from additional paths. We hypothesize that with path-aware networking as enabled by SCION, endhosts can overcome these limitations and aggregate otherwise unused capacities in the network. Consequently, we present our BitTorrent over SCION implementation, which enables multipath communication and intelligent path selection for endhosts in global torrent networks. Comparing our implementation against BitTorrent over BGP and BGP-M in a small-scale Internet topology, we observe an increase in goodput of 48% through multipathing compared to BitTorrent over BGP and 33% compared to the BGP-M candidate, which confirms our hypothesis.

Path aware networking (PAN) is an approach that allows endpoints to participate in the end-to-end path selection, letting them choose paths best suited for each application. This approach offers numerous potential benefits including rapid fail-over, concurrent use of parallel paths, and QoS enabled networks, even spanning multiple domains. The dynamic interconnection of different autonomous systems (ASes) in path aware networks offers both challenges and opportunities for network service providers, which in turn provide opportunities for traffic engineering previously not possible. The SCION path-aware network architecture has been designed from the ground up with security in mind, and features a trust structure that can serve as a basis for more dynamic interconnection between ASes. In this paper, we describe a prototype spot market that lets the ASes sell time-limited excess capacity, allowing buyers to divert traffic to cheaper alternatives temporarily. We believe this market allows for new opportunities both in traffic engineering and inter-domain connectivity that have not existed before. The market benefits all parties involved, as the formerly wasted bandwidth is now used, and provides additional revenue—in varying degrees—to all the participating entities.

SCION, as a next-generation network architecture to overcome the technical problems of the Internet, brings its own performance challenges to reach high speed packet processing. The current SCION upstream implementation has two performance limitations. First, the SCION Dispatcher does not perform well under high load. Second, the SCION libraries used to encapsulate/decapsulate SCION packets limit the performance of applications. Xiondp is an XDP-based bypass of the SCION Dispatcher and an optimised implementation of a SCION application library, that solves these performance problems. In this paper, we demonstrate the performance impact of both optimisations compared to the original setup via the SCION Dispatcher. Our testbed consists of two servers, connected via a direct 10Gbit/s link. To show the performance impact, we use a synthetic benchmark and a filetransfer tool and demonstrate the performance in an intra-AS and inter-AS setup.

SCION is a next-generation network architecture promising many benefits over the current Internet like path-awareness and built-in trust. Especially SCION’s native multi-path properties provide new potentials for extreme performance networking applications. However, the performance of SCION’s reference implementation is currently limited as two of its central elements, the SCION dispatcher as well as the packet processing in the client library, have a low processing throughput, leading to packet loss under high loads. In order to fully unlock SCION’s potentials at ultra-high peak data rates, we propose two optimizations: The SCION Dispatcher bypass through the use of XDP and an optimized SCION connection implementation for efficient packet processing. We show that our dispatcher bypass is theoretically able to approximately quadruple the bandwidth, while in a real-world scenario the throughput increases by factor of 3. Additionally, in conjunction with the optimized SCION connection, throughput can theoretically be increased sevenfold, while a threefold increase was observed in the real-world scenario.

Path aware networking (PAN) is a promising approach that enables endpoints to participate in end-to-end path selection. PAN unlocks numerous benefits, such as fast failover after link failures, application-based path selection and optimization, and native interdomain multi-path. The utility of PAN hinges on the availability of a large number of high-quality path options. In an inter-domain context, two core questions arise. Can we deploy such an architecture natively in today's Internet infrastructure without creating an overlay relying on BGP? Can we build a scalable multi-path routing system that provides a large number of high-quality paths?

Network testbeds have empowered networking re-search and facilitated scientific progress. However, current testbeds focus mainly on experiments involving the current Inter-net. In this paper, we propose SCIONLAB, a novel global network testbed that enables exciting research opportunities and experimentation with the SCION next-generation Internet architecture. New users can join SCIONLAB as a full-fledged autonomous system with minimal effort and administrative overhead, and directly gain unfettered access to its inter-domain routing system. Based on a well-connected network topology consisting of globally distributed nodes, SCIONLAB enables new experiments, such as inter-domain multipath communication, path-aware networking, exploration of novel routing policies, and new approaches for DDoS defense. SCIONLAB has been operational since 2016 and has supported diverse research projects. We describe the design and implementation of SCIONLAB, and present use cases that illustrate exciting research opportunities.

Content delivery networks (CDNs) carry more than half of the video content in today's Internet. By placing content in caches close to the users, CDNs help increasing the Quality of Experience, e.g., by decreasing the delay until a video playback starts. Existing works on CDN cache performance focus mostly on distinct caching metrics, such as hit rate, given an abstract workload model. Moreover, the nature of the geographical distribution and connection of caches is often oversimplified. In this work, we investigate the performance of cache hierarchies while taking into account the presence of a mixed content workload comprising multiple categories, e.g., news, comedy, and music. We consider the performance of existing caching strategies in terms of cache hit rate and deterioration costs in terms of write operations. Further, we contribute a design and an evaluation of a content category-aware caching strategy, which has the benefit of being sensitive to changing category-specific content popularity. We evaluate our caching strategy, denoted as ACDC (Adaptive Content-Aware Designed Cache), using multiple caching hierarchy models, different cache sizes, and a real world trace covering one week of YouTube requests observed in a large European mobile ISP network. We demonstrate that ACDC increases the cache hit rate for certain hierarchies up to 18.39% and decreases transmission latency up to 12%. Additionally, a decrease in disk write operations up to 55% is observed.

The demand for high-bandwidth, low-latency services is rapidly increasing. Content Distribution Networks (CDNs) have addressed this by providing content from within or close to the Internet Service Provider (ISP). Still, the most common bottleneck for high service quality is the 'last mile' between ISP and end user. Serving content from small caches on end-user devices promises to increase service quality of the respective content. Similarly, fog computing promises to provide low-latency services from arbitrary nodes within the network. Both require additional functionality provided by network functions virtualization (NFV), redirecting traffic to the appropriate destinations. Still, the cost and performance of possible solutions are not well analyzed. Hence, this paper analyzes the forwarding and computing performance of a number of single-board computers (SBCs) from which models for the performance and energy cost of different loads are derived. Furthermore, the development of energy efficiency gains over the last years is analyzed, confirming Koomey's law also for SBCs, leading to an increase of computational efficiency by a factor of 5.5 to 7.5 over the course of four years, which relates to a doubling time of 1.62 years.

Bare-metal switches (BMS) are switches sold without an operating system (OS), i.e. including only the switch hardware. For network operators this may not only provide a cost advantage, it also allows them to customize the OS behavior to their own needs. The aim of this demonstration is to show the applicability of BMS in a carrier-grade environment. To this end, a representative use case has been implemented on a BMS, which includes the healing of RTP packet loss by automatically selecting a stream with the least packet loss from redundant uplinks. This use case demonstrates the flexibility of BMS to be adaptable to even such specific needs in a network.

Video streaming is responsible for the largest portion of traffic in fixed and mobile networks. Yet, forecasts expect this amount to grow further. Especially for mobile devices connected to cellular networks, high QoE video streaming can be a challenge as the user data volume is metered and eventually limited. Also, the connection quality may vary severely. Prefetching videos is an approach to mitigate this issue. Here, videos that the user is likely to watch in advance are prefetched on the user's smartphone, e.g., while he is connected to WiFi. However, this approach can only be efficient if only the videos that are interesting for the respective user are prefetched. This constitutes a major estimation and prediction challenge. To this end, this paper presents three contributions: First, a user study over multiple months that draws valuable insights on the user video request behavior. Second, we propose a novel privacy-preserving prefetching framework denoted vFetch that prefetches videos based, e.g., on the user's affinity of YouTube channels. Third, a trace-based evaluation and parameter study that demonstrates vFetch's efficiency with a hit rate of ~50% for a 50 GB cache.

The current ecosystem of network elements, such as switches and appliances, is largely dominated by devices supplied and sold with a bundled operating system, and software dedicated to manage the device's forwarding hardware, however, these platforms are not open-source and cannot be arbitrarily customized, and there is no cost transparency or flexibility in choosing software different to the bundled components.,,,,In this paper, we explore the capabilities of bare-metal switches, which are equipped with commodity switching hardware components, but shipped without an operating system. We evaluate the feasibility of these commonly lower-cost devices to meet the requirements of a customized, carrier-grade network function. Therefore, we have implemented a prototype on generic hardware, re-using as much open-source software as possible. Our Broadband Remote Access Server (BRAS) prototype can lower the cost compared to proprietary network appliances, and, known to have a hardware backplane capacity of 720 Gbps, the merchant-silicon / ASIC approach can highly outperform the state of the art of current x86-based virtualized network functions, while implementing the most important BRAS features.

BTLive is a novel, unique, mesh/push-based peer-to-peer (P2P) live video streaming mechanism. Previous studies show that it achieves significantly lower streaming delays than other approaches but suffers from a high overhead, where peers receive up to 33% duplicate video chunks. Other P2P streaming mechanisms, most of which are either mesh/pull or tree/push-based, do not experience duplicates at such a severe level. As a result, the problem of duplicate transmission has not explicitly been studied so far. However, to make BTLive's streaming approach more practical, it is critical to drastically reduce its overhead. To this end, this paper presents the design of a novel adaptive mechanism which, applied to BTLive, reduces the number of duplicates to less than 4%, while preserving its superior delivery performance. While the mechanism is applicable without changing the original BTLive protocol, also an extension to the protocol is proposed to further improve the duplicate avoidance. It reduces the amount of duplicates to less than 1%, without significantly increasing the overhead or playback delay. Thereby this work finally shows how a long envisioned low-delay and, at the same time, low-overhead P2P-based live stream delivery can be achieved.

The preferred channel for listening to music is shifting towards the Internet and especially to mobile networks. Here, the overall traffic is predicted to grow by 45% annually till 2021. However, the resulting increase in network traffic challenges mobile operators. As a result, methods are researched to decrease costly transit traffic and the traffic load inside operator networks using in-network and client-side caching. Additionally to traditional reactive caching, recent works show that proactive caching increases cache efficiency. Thus, in this work, a mobile network using proactive caching is assumed. As music represents the most popular content category on YouTube, this work focuses on studying the potential of proactively caching content of this particular category using a YouTube trace containing over 4 million music video user sessions. The contribution of this work is threefold: First, music content-specific user behavior is derived and audio features of the content are analyzed. Second, using these audio features, genre and mood classifiers are compared in order to guide the design of new proactive caching policies. Third, a novel trace-based evaluation methodology for music-specific proactive in-network caching is proposed and used to evaluate novel proactive caching policies to serve either an aggregate of users or individual clients.

Video constitutes the majority of traffic in ISP networks. The demand for more and higher quality videos is growing rapidly while network capacities are limited. Today, OTT video is mostly delivered over CDNs. For ISP-internal video such as IPTV channels, IP multicast provides an efficient delivery method. However, this does not scale for OTT videos due to their large number and their Zipf-distributed popularity. SDN-based multicast enables ISPs to deliver content based on efficient network-level multicast. However, so far this concept has not been applied for OTT Video-on-Demand (VoD) scenarios. To this end, this paper's contribution is threefold. First, a novel network-based multicast approach for OTT VoD delivery is presented, named VoDCast. Second, trace-based network simulations are designed and conducted. Third, the achievable bandwidth reduction and practicability of VoDCast is shown based on a two-week YouTube trace of a nation-wide mobile provider. VoDCast is able to decrease traffic volume and variance, while keeping network provider costs in terms of state overhead and state change low.

Network functions virtualization (NFV) environments can provide increased elasticity and flexibility for operators, as network-related services can be scaled and moved as needed. Such operations require a seamless transfer of state to provide a service without interruption or performance degradation. In this paper, we propose and analyze a novel approach for efficient and seamless NFV state transfers. Our approach is based on the concept of statelets, which are compact representations of information in incoming packets that change the state of a virtualized network function. We present and describe slim migration (SliM), a system for efficient NFV state transfers using a statelet interface that we have implemented as an add-on to the data plane development kit, a high-performance packet I/O library. We have evaluated SliM in a testbed and present results that show its benefits in terms of lower delays and lower packet-loss rates. Moreover, our analysis and evaluation results show that SliM offers three times as much shared link capacity to the dataplane as previous approaches, while reducing the duration of a state transfer to one-third of their time.

Multi-layer resilience is one of the prominent new concepts for modern carrier networks; it efficiently combines the advantages of the optical and the packet layer. However, not all features offered by modern optical transport networks can be fully used by the packet layer yet. In case of a fiber cut, an optical protection mechanism restores the original IP topology after a short transient time. But such an optical restoration is expected to use a new, longer light-path, which in turn might affect the optical capacity of the link. Bit-rate flexible optical transceivers are able to utilize the remaining optical capacity efficiently by adapting the network link capacity accordingly. However, the packet layer is not able to cope with fluctuating link capacities; often the policy is to rather shut down the link completely instead of using the remaining capacity. Consequently, this paper proposes a Segment Routing-based approach with superimposed Software-defined Networking (SDN) to allow the IP network to benefit from these new features. The minimally invasive, gradual deployment of the system is investigated, while keeping other proven and resilient technologies and systems unmodified. Using the topology and traffic matrix of a large German carrier the feasibility of such a deployment is evaluated.

Instance migration and scale-in/out operations in network functions virtualization (NFV) require state transfer mechanisms, which are known to cause service degradation through increased jitter and packet loss. Techniques such as packet duplication for state synchronization mitigate this problem, however, they incur significant additional costs. In this paper, we provide a novel interface to the VNF to announce “statelets” for incoming packets, which comprise only the information in the packet which is required for a VNF's internal state change. Based on this interface, we design and implement SliM, a statelet-based framework for seamless VNF migration. First evaluation results show that SliM operates seamlessly at very high dataplane utilization of physical links, up to 3 times the utilization level at which existing approaches are failing due to insufficient bandwidth for state synchronization.

Reputation networks are an important building block of distributed systems whenever reliability of nodes is an issue. However, reputation ratings can easily be undercut: colluding nodes can spread good ratings for each other while third parties are hardly able to detect the fraud. There is strong analytical evidence that reputation networks cannot be constructed in a way to guarantee security. Consequently, only statistical approaches are promising. This work pursues a statistical approach inspired by the idea that colluding node's behavior changes the local structure of a reputation network. To measure these structural changes, we extend a graph analysis method originating from molecular biology and combine it with a machine learning approach to analyze fingerprints of node's interactions. We evaluate our method using an adaptive Peer-to-Peer (P2P) streaming system and show that a correct classification of up to 98% is possible.

Video traffic is the main driver of Internet traffic volume. Thus, content providers and Content Delivery Networks (CDNs) are searching for ways to provide reliable video transmission at a low cost. Hybrid CDN/Peer-to-Peer (P2P) deployments like Akamai NetSession have been shown to combine the high reliability of a CDN backbone and the low cost of P2P networks. In the near future, the biggest barrier for user adoption will fall: the installation of a dedicated P2P client software will be replaced by website embedded browser-to-browser communication logic. However, this requires the explicit consent of users, and, since users need to share their upload capacity, their willingness to participate in such a system. In this work, the efficiency of incentive mechanisms trading a higher Quality of Experience (QoE) of video transmission for user's consent to utilize their upload capacity are investigated. This is the first study to investigate the question of incentives in distributed, adaptive streaming systems from a user perspective using a crowd working approach. The work presents results from 192 test subjects. We identify three classes of users and show how behavioral economics can be utilized to increase the impact of an incentive scheme.

Adaptive streaming systems gain rising relevance for streaming services. Therefore, the same video is offered in multiple quality versions to clients for adaptation during playback. However, optimizing adaptation in a Quality of Experience (QoE) centric way is difficult. Current systems maximize bit rate, ignoring that different types of adaptation (resolution, framerate, quantization) correlate differently and in a non-linear way with user's perception. User validated video quality metrics can provide precise quality information. However, measurements of state-of-the-art metrics show either high computational intensity or weak correlation with subjective tests. This makes large-scale offline quality assessment processing intensive while real-time constrained scenarios like live streaming and video conferencing are hardly supportable. Consequently, this work presents the Real-Time Video Quality Metric (RT-VQM), a real-time, Graphics Processing Unit (GPU) supported version of the widely used Video Quality Metric (VQM). RT-VQM introduces efficient filtering operations, hardware-supported scaling and high-performance feature pooling. The approach outperforms VQM by a factor of 30, thus enabling a real-time assessment of up to 9 parallel video stream representations up to High Definition (HD) 720 resolution at 30fps.

Instead of retrieving content from a mobile infrastructure, devices may transmit it between each other, which is referred to as Device-to-Device (D2D) content delivery. This paradigm especially helps the mobile network operator to offload its infrastructure from the delivery of popular content. Private-Share is an application for the Android platform, which enables the user to synchronize content with D2D techniques over Wi-Fi Direct. While being useful for the device owner, it is also a platform for research on several aspects of user behavior and various D2D transmission-or environmental parameters. This paper introduces PrivateShare and discusses first results, which show that most Wi-Fi Direct neighbors are encountered during usual busy times, but also a certain fraction of likely residential devices are encountered overnight.

Mobile data traffic is increasing rapidly and wireless spectrum is becoming a more and more scarce resource. This makes it highly important to operate mobile networks efficiently. In this paper we are proposing a novel lightweight measurement technique that can be used as a basis for advanced resource optimization algorithms to be run on mobile phones. Our main idea leverages an original packet dispersion based technique to estimate per user capacity. This allows passive measurements by just sampling the existing mobile traffic. Our technique is able to efficiently filter outliers introduced by mobile network schedulers and phone hardware. In order to asses and verify our measurement technique, we apply it to a diverse dataset generated by both extensive simulations and a week-long measurement campaign spanning two cities in two countries, different radio technologies, and covering all times of the day. The results demonstrate that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow. The only requirement for the input data is that it should consist of a few consecutive packets that are gathered periodically. This makes the measurement algorithm a good candidate for inclusion in OS libraries to allow for advanced resource optimization and application-level traffic scheduling, based on current and predicted future user capacity.

Mobile applications such as VoIP, (live) gaming, or video streaming have diverse QoS requirements ranging from low delay to high throughput. The optimization of the network quality experienced by end-users requires detailed knowledge of the expected network performance. Also, the achieved service quality is affected by a number of factors, including network operator and available technologies. However, most studies measuring the cellular network do not consider the performance implications of network configuration and management. To this end, this paper reports about an extensive data set of cellular network measurements, focused on analyzing root causes of mobile network performance variability. Measurements conducted on a 4G cellular network in Germany show that management and configuration decisions have a substantial impact on the performance. Specifically, it is observed that the association of mobile devices to a point of presence (PoP) within the operator's network can influence the end-to-end performance by a large extent. Given the collected data, a model predicting the PoP assignment and its resulting RTT leveraging Markov chain and machine learning approaches is developed. RTT increases of 58% to 73% compared to the optimum performance are observed in more than 57% of the measurements. Measurements of the response and page load times of popular websites lead to similar results, namely, a median increase of 40% between the worst and the best performing PoP.

A number of today's over-the-top (OTT) services could greatly benefit from a flexible, efficient, and scalable network-layer multicast support on the Internet. IP multicast showed not to meet these requirements, hindering its adoption in more than individual network islands. As a consequence, content delivery networks (CDNs) emerged as alternative and implemented one-to-many multicasting at the application layer. Yet, due to practical reasons and conflicting stakeholder interests, CDN deployments usually end at the border of Internet service provider networks, making one-to-many deliveries at an Internet-scale challenging up to date. In a previous work, the authors presented software-defined multicast (Sdm) to mitigate this problem by unlocking network-layer multicast support for OTT services inside ISP networks. In this paper, DYNSDM is proposed to complement Sdm in key aspects, allowing for a more holistic support of relevant scenarios aspects, and enabling a fine-granular traffic engineering of multicast traffic inside ISP networks. For this, DYNSDM presents a detailed design of the ISP-internal traffic and service management process and introduces a set of novel SDN-based network-layer mechanisms for traffic load balancing as well as handling of group and network dynamics. DYNSDM was implemented as prototype and extensively evaluated, showing its flexibility, high traffic efficiency, good scalability, and superior ability to balance traffic load inside ISP networks.

Dynamic network service chaining allows network operators to apply network services to customer traffic on demand and in a highly flexible manner. SDN and particularly OpenFlow-based approaches have been presented that exploit the flexibility and feature richness of OpenFlow for service chaining in data center settings. These systems often use network architectures similar to MPLS, where network traffic is processed at the network edges and the network core conducts simple packet forwarding only. This approach is suitable for many use cases due to the complementary characteristics of the hardware devices used in the core and the virtual switches used at the edge on virtualization hosts. Yet, this leads to a concentration of processing flow updates solely at the network edges, which can cause lowered QoS during flow update peaks, e.g. when a virtualization host fails. To tackle this problem, in this paper a concept is presented and evaluated that offloads OpenFlow rule updates from software-based edge switches to exploit hitherto unused resources on hardware switches in the core of the network. Furthermore, an analytical model is presented that allows describing the expected gain of the approach based on characteristics of the used OpenFlow devices.

Mobile applications such as VoIP, (live) gaming, or video streaming have diverse QoS requirements ranging from low delay to high throughput. The optimization of the network quality experienced by end-users requires detailed knowledge of the expected network performance. Also, the achieved service quality is affected by a number of factors, including network operator and available technologies. However, most studies focusing on measuring the cellular network do not consider the performance implications of network configuration and management. To this end, this paper reports about an extensive data set of cellular network measurements, focused on analyzing root causes of mobile network performance variability. Measurements conducted over four weeks in a 4G cellular network in Germany show that management and configuration decisions have a substantial impact on the performance. Specifically, it is observed that the association of mobile devices to a Point of Presence (PoP) within the operator's network can influence the end-to-end RTT by a large extent. Given the collected data a model predicting the PoP assignment and its resulting RTT leveraging Markov Chain and machine learning approaches is developed. RTT increases of 58% to 73% compared to the optimum performance are observed in more than 57% of the measurements.

Device-to-Device (D2D) content delivery is an emerging approach, where end-user devices exchange content with other end-user devices in communication range, instead of retrieving content from an operator's infrastructure. This way, the operator network can be offloaded from congestion caused by the transmission of popular content, and the content consumer's quality of experience may increase. However, D2D content delivery is only effective in situations where a device in proximity has the requested content available, which is more likely to happen with popular content in crowded areas. The availability of content in communication range of a consumer constitutes an upper bound of the success of a D2D content delivery mechanism, which is referred to as the potential of D2D delivery. This paper provides a quantitative answer to the question of this potential, and identifies the most important properties a D2D mechanism must provide. An evaluation model is proposed and developed, which can be applied to real-world mobile user traces to determine the quota of content requests that could be served via D2D content delivery. The model is applied on a dataset of a major European Internet service provider and the evaluation results are discussed. The paper concludes that there is potential to deliver up to 60% of requests for popular content via D2D, if a reliable mechanism to predict a user's content consumption is available.

A number of today's over-the-top (OTT) services could greatly benefit from a scalable and efficient network-layer multicast support on the Internet. IP multicast showed to not meet these requirements and, thus, is not available for this purpose. Content Delivery Networks emerged as global alternative but usually end at the border of ISP networks. Software-Defined Multicast (SDM) is proposed in a previous work by the authors, enabling ISP-internal network-layer multicast delivery of OTT traffic. While it coins fundamental concepts, it does not detail the ISP-internal traffic and service management and leaves important questions unanswered. To this end, DYNSDM is proposed in this paper, detailing the multicast planning and management, proposing a novel network-layer multi-tree mechanism to distribute traffic on links inside the ISP network, and introducing mechanisms to handle group and network dynamics. DYNSDM was prototypically evaluated, showing its high traffic efficiency, good scalability, and superior traffic distribution characteristics.

Software Defined Network (SDN) solutions bring larger flexibility to network providers and enable improvement of the users' experience for diverse multimedia networked services. Following this successful approach, we propose a Software Defined Health (SDH) solutions, bringing flexibility to health services providers and enabling an improvement of health state of their users. In this paper we coin the term and explain a set of research challenges that need to be addressed in order to fulfil the SDH vision.

Energy efficiency of networking equipment has become an important factor for private consumers as well as business customers in the recent years. This trend is driven by two factors: the proliferation of new mobile devices as well as energy prices as cost driver. Battery capacity as well as dynamic distribution of workloads adapted to energy costs are an active field of research. This demo contributes to this discussion by presenting a framework allowing the assessment of energy costs along the content distribution chain.

Live streaming of large-scale events such as the Olympic Games with a huge number of viewers is challenging, as the streaming infrastructure needs to scale fast and big, and often in an unpredictable manner. Peer-to-peer (P2P) live streaming (Peercasting) has proven to be beneficial in these scenarios, as resources are scaling inherently with the number of nodes. However, churn behavior in a node's neighborhood may result in fluctuating downstream bandwidth and thus freezing (stalling) playback. Related work tries to mitigate this effect by using layered video codecs, focusing on single-dimensional scalability in mesh-pull based systems. Yet, the benefits of multidimensional scalability (resolution, frame rate, and quantization) combined with coexisting pull-/push mechanisms introduced by modern hybrid P2P streaming architectures have not been studied in detail. Consequently, this work proposes a new scheduling algorithm taking these aspects into account. The evaluation shows large benefits for end-users by reducing the frequency of stalls by 90% even under extreme conditions.

Mobile data consumption has seen a considerable rise and is predicted to further increase. This is mainly caused by mobile video consumption. A promising solution is offloading cellular traffic to WiFi. Here, Multipath TCP (MPTCP) enables a seamless user experience for handover and load balancing. However, its influence on the energy consumption of smartphones is not yet well analyzed. To this end, this paper determines the energy cost of using MPTCP for mobile constant bit rate streaming on two Nexus devices and compares it to the power consumption using single interface TCP streaming. From this, recommendations for an optimal assignment of sub-streams to interfaces are derived. The measurements indicate that using MPTCP on certain smartphones causes a 20% lower energy expense compared to the cost of both interfaces individually. However, MPTCP using multiple interfaces should only be used if the requested data rate cannot be supported by a single interface.

Online media services are reshaping the way video content is watched. People with similar interests tend to request same content. This provides enormous potential to predict which content users are interested in. Besides, mobile devices are commonly used to watch videos which popularity is largely driven by its social success. In this paper, we design CPSys a Central Predictor System to prefetch relevant videos for each user. To fine tune our prefetching system, we rely on a large dataset collected from a large mobile carrier in Europe. The rationale of our prefetching strategy is first to form a graph and build implicit or explicit ties between similar users. On top of this graph, we propose the Most Popular and Most Recent (MPMR) policy to predict relevant videos for each user. We show that CPSys can achieve high performance with respect to the correct prediction ratio and by significantly reducing the traffic overhead. We further show that CPSys outperforms other prefetching schemes that have been presented and studied in the state of the art. At the end, we provide a proof-of-concept implementation of our prefetching system.

Mobile network operators are expected to face significant traffic increase in the upcoming years. One alternative method is to intelligently move transmissions to times of network underutilization, either on 3G/4G or by offloading to WiFi. Video content, predicted by Cisco to constitute 69% of mobile traffic, offers the greatest potential for offloading. To this end, the demonstrated app strives to relieve the mobile network in a two ways. First, long-term prefetching of promising videos based on posts from the user's Online Social Network feed is performed. The knowledge about which video is likely being requested in the near future offers the opportunity to schedule the transmission according to its probability of being watched. Second, the approach is complemented with short-term prefetching, which is used whenever a content could not be downloaded by long-term prefetching. In this case, resources are optimized so as to maximize the communication efficiency while preserving the quality of service. The demonstrated app considers the smartphone's observed cellular network history to optimize the mobile throughput. A customized video player implements both the long-term and short-term prefetching. It reduces both the load on mobile networks, decreases playback pausing events and hereby achieves a high QoE. Thus, the player addresses both the operators' and the users' needs.

Internet Service Providers (ISPs) need to cope with a growing amount of over-the-top (OTT) traffic, often without a share in the high revenues of the content providers. To achieve an efficient global delivery of content, today content providers usually employ content delivery networks (CDNs) located at the edge of ISP networks from where content is delivered to end users via IP unicast. Many OTT services could benefit from a better support within the ISP's network, e.g. by packet duplication to deliver OTT video streams. While traditional solutions like IP multicast did not prevail, SDN-based alternatives have started to gain attention recently. In contrast to traditional approaches, SDN enables ISPs to support network services in a more manageable and flexible manner. However, the approaches proposed so far are quite rigid and keep state at every network device, independent of the multicast group size. To alleviate this problem, this paper proposes a new approach termed ASDM enabling ISPs to dynamically adjust the tradeoff between bandwidth and state for any multicast service. It is shown that, given ISP-defined bandwidth and state cost functions, the optimal parameter for ASDM can be derived and applied for a transparent multicast-to-unicast conversion achieving the desired characteristics. The proposed approach results in up to 30% bandwidth reduction compared to unicast while using only a seventh of the network state compared to traditional multicast.

Mobile data traffic is increasing rapidly and wireless spectrum is becoming a more and more scarce resource. This makes it highly important to operate the mobile network efficiently. In this paper we are proposing a novel lightweight measurement technique that can be used as a basis for advanced resource optimization algorithms to be run on mobile phones. Our main idea leverages an original packet dispersion based, technique to estimate both per user capacity and asymptotic dispersion rate. This allows passive measurements using only existing mobile traffic. Our technique is able to efficiently filter outliers introduced by mobile network schedulers. In order to verify the feasibility of our measurement technique, we run a week-long measurement campaign spanning two cities in two countries, different radio technologies, and covering all times of the day. The campaign demonstrates that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow. The only requirement for the input data is that it should consist of a few consecutive packets that are gathered periodically. This makes the measurement algorithm a good candidate for inclusion in OS libraries to allow for advanced resource optimization and application-level traffic scheduling, based on current and predicted future user capacity.

Recent studies show that an increasing number of over-the-top live streams is delivered over the Internet. For the delivery of those streams, the dynamically changing and potentially large number of users imposes a major challenge. Flash crowds, where the number of users multiplies or significantly drops in a very small time frame, can cause serious degradations in the streaming performance. Due to the missing support for global network-layer multicast, overlay-based approaches have been broadly studied, showing that, with relaxed time constraints, they can scale well with the number of users. Yet, to support flash crowds, scaling has to happen quickly to keep up also with rapidly changing populations. Only a few approaches exist that focus on this aspect by influencing the streaming topology and, so far, it is not clear if and how these mechanisms can be applied to state-of-the-art hybrid streaming systems. Therefore, in this paper, TOPT is proposed, integrating new as well as existing mechanisms in a common framework. The evaluation shows that the streaming topology, indeed, plays a major role during flash crowds. The lightweight and decentralized tree-forming and topology optimization mechanisms of TOPT, combined with tracker extensions to attach new peers in batches, greatly help improving the streaming performance in terms of reduced playback interruptions by more than 60% and slight reduction in communication overhead at an acceptable increase in average startup delays by 24%.

Video streaming constitutes the dominant portion of today’s traffic on the Internet and will grow in the coming years. In order to provide for a low cost distribution of bulky video content, Peer-to-Peer (P2P) approaches are a viable way to cut down server bandwidth cost by utilizing user’s upstream bandwidth to redistribute data. However, users need an incentive to participate in such a system. The related work on incentive schemes has focused on using bandwidth contribution as a measure for contribution to the system’s performance, ignoring that user perceived Quality of Experience (QoE) is not necessarily maximized by maximizing bandwidth, but by delivering the right data in the right order. Consequently, this work presents QTrade, a topology agnostic incentive scheme for adaptive P2P video streaming systems based on user-validated video quality metrics. QTrade is evaluated on top of an existing adaptive streaming overlay. The results show that QTrade utilizes bandwidth more efficiently by providing incentive to distribute parts of the video with a high QoE. Moreover, up to 70% less and shorter rebuffering events are observed for cooperative peers while maintaining a 10 to 11 times worse performance in terms of rebuffering events for non-cooperative peers.

A wireless Ad Hoc network consisting of a source and multiple receiving nodes is considered. The source wants to transmit a common message throughout the whole network. The message has to be spread in a multi-hop fashion, as the transmit powers at the source and the nodes are limited. The goal of this paper is to find the multi-hop broadcast tree with a minimum energy consumption in the network. To reach this goal, a new decentralized game theoretic approach is proposed which considers the following two aspects jointly for the first time: Firstly, it optimizes the transmit powers at the source and at the individual intermediate nodes. Secondly, it employs maximum ratio combining at the receiving nodes following the fact that a node can receive several copies of the message from different sources in different time slots. The game is modeled such that the nodes are incentivized to forward the message to their neighbors. In terms of the total transmit energy, the results show that the proposed algorithm outperforms other conventional algorithms.

Computation offloading is an upcoming approach to increase battery life of mobile devices overburdened by resource-consuming applications. In multi-hop networks, computation offloading poses new challenges since intermediate devices are required to relay tasks of others along the path to the server. The decision of a device about whether to offload or not depends thus on the provided energy of relay devices and on the decisions of other offloading devices since relay resources need to be shared. This also implies that for energy minimization, optimal decisions are topology-dependent. This paper introduces a novel theoretical framework for energy minimization of computation offloading in multi-hop wireless networks which formulates the energy minimization problem as a binary linear problem. Proving its equivalence to a multi-dimensional knapsack problem allows us to specify a greedy heuristic, which shows very good performance, with a maximal deviation of less than 5% from the optimal results. From simulations and analytical results for different topologies, we derive under which conditions computation offloading in multi-hop networks is beneficial.

For transmitting data in scenarios showing a high user density, infrastructure based and multihop Ad hoc communication can be combined to benefit from the reliability of a stable backbone network and the increased coverage of multihop communication. Such scenarios have been investigated from a cross layer perspective in the recent years mainly focusing on pure performance optimization. However, the question of providing incentives to nodes to forward data has largely been ignored in the cross layer domain, even though providing incentives is vital for the network: each node represents a user comparing his or her satisfaction and the cost to decide on his or her participation. A likely reason for the gap in cross layer incentive research is the necessity to model users as well as the network in order to express a user's utility, which requires knowledge in both fields. In order to foster future research in the area of cross layer incentive schemes, this work proposes a general cross layer simulation model combining user and network models. Moreover, an instantiation of the simulation model for the use case of live video broadcasting is presented.

The collaboration of Internet service providers (ISPs) and content distribution network (CDN) providers was shown to be beneficial for both parties in a number of recent works. Influencing CDN edge server (surrogate) selection allows the ISP to manage the rising amount of traffic emanating from CDNs to reduce the operational expenditures (OPEX) of his infrastructure, e.g., by preventing peered traffic. At the same time, including the ISP's hidden network knowledge in the surrogate selection process influences the quality of service a CDN provider can deliver positively. As a large amount of CDN traffic is video-on-demand traffic, this paper investigates the topic of CDN/ISP collaboration from a perspective of high-volume long-living flows. These types of flows are hardly manageable with state-of-the-art Dynamic Name Service (DNS)-based redirection, as a reassignment of flows during the session is difficult to achieve. Consequently, varying load of surrogates caused by flash crowds and congestion events in the ISP's network are hard to compensate. This paper presents a novel approach promoting ISP and CDN collaboration based on a minimal deployment of software-defined networking switches in the ISP's network. The approach complements standard DNS-based redirection by allowing for a migration of high-volume flows between surrogates in the backend even if the communication has state information, such as Hyper Text Transfer Protocol sessions. In addition to a proof-of-concept, the evaluation identifies factors influencing performance and shows large performance increases when compared to standard DNS-based redirection.

Network management systems (NMS) need to support a wide range of application requirements while ensuring an efficient use of networking resources. Application control of Software Defined Networking (SDN) promises to drastically reduce OPEX and increase flexibility of NMS by automatically translating application requirements into network policies and subsequently into network behavior. The current research in this area focuses mainly on top-down approaches aiming at expressiveness, generality, and control plane resource efficiency. However, the expressive power and flexibility of Open Flow, a widely used SDN implementation, comes at the cost of high complexity when high-level policies are automatically translated into Open Flow rules, which can lead to an inefficient use of data plane resources. To counter this problem, this paper proposes a bottom-up approach to application-controlled SDN which aims to leverage scenario- and application-specific information for optimization. Thereby, the proposed approach is able to satisfy diverse application requirements and use the available data plane resources more efficiently than comparable approaches.

Real-time entertainment constitutes the majority of traffic in today's mobile networks. The data volume is expected to increase in the near future, whereas the mobile bandwidth capacity is likely to increase significantly slower. Especially peak hour traffic often leads to overloaded mobile networks and poor user experience. This increases costs for the mobile operator, which has to adapt to the peak demand by capacity over provisioning. The new approach proposed in this paper aims to leverage the user's context and video meta-information to unleash the potential of video prefetching. Based on observed user interactions with social networks, the videos a user consumes from social neighbours can be predicted. Moreover, the user's daily routine even enables a prediction of the time when videos are consumed as well as the network capabilities available at that point. First results show that partial prefetching based on content categories provides a potential for efficiently offloading mobile networks. Additionally, the user experience can be improved as freezing playbacks of videos can be decreased. Initial results show a high potential for category-based prefeching.

An increasing number of households is connected to the Internet via DSL or cable, for which home gateways are required. The optimization of these - caused by their large number - is a promising area for energy efficiency improvements. Since no power models for home gateways are currently available, the optimization of their power state is not possible. This paper presents PowerPi, a power consumption model for the Raspberry Pi which is used as a substitute to conventional home gateways to derive the impact of typical hardware components on the energy consumption. The different power states of the platform are measured and a power model is derived, allowing to estimate the power consumption based on CPU and network utilization only. The proposed power model estimates the power consumption resulting in a RMSE of less than 3.3%, which is slightly larger than the maximum error of the measurements of 2.5%.

Peer-to-Peer (P2P) has proven to be a scalable approach for content distribution, while reducing the load for the content provider. As P2P systems depend on each node's participation, reciprocal incentive mechanisms for stimulating contribution are a major building block in those systems. However, reciprocal incentive schemes fall short for resource-poor devices, as they lead to service degradation for those nodes. This paper presents vINCENT, an incentive scheme allowing resource-poor devices to seek support from resource-rich devices. The novel scheme is based on virtual nodes which form a trusted domain around multiple devices. vINCENT has been implemented and evaluated in a P2P live streaming scenario. The solution provides good playback quality to all devices within a virtual node, given a sufficient contribution of the virtual node as a whole. At the same time, free riding nodes are isolated efficiently and the game theoretic properties of reciprocity are preserved.

The transmission of video content accounts for a large share of today's Internet traffic. While Video-on-Demand (VoD) substantially contributes to this, live streaming events such as video broadcasts from the Olympic Games can cause very high traffic volumes in the short term as well. Such peaks along with high fluctuations triggered by sudden changes in the behavior of users make the design of live streaming systems particularly challenging. Peer-to-Peer (P2P) has proven to be a scalable approach for disseminating content to a large number of users. Accordingly, the body of research offers numerous P2P live streaming approaches tailored towards specific scenarios and assumptions. However, no single approach is able to perform well under all possible conditions. Keeping up a high performance when conditions are changing is a challenge, since topology management and scheduling mechanisms cannot be exchanged easily. Therefore, this paper proposes TRANSIT, a new approach going beyond existing works in that it supports transitions between different live streaming mechanisms. TRANSIT makes different configurations of such mechanisms seamlessly exchangeable to enable the optimal choice of configurations for a wide range of live streaming scenarios. The approach is evaluated using measurement- and trace-based workloads. The results show that TRANSIT is able to maintain a high performance at a low overhead in highly fluctuating environments, whereas static configurations show serious performance degradations.

Over the years, the demand for high bandwidth services, such as live and on-demand video streaming, steadily increased. The adequate provisioning of such services is challenging and requires complex network management mechanisms to be implemented by Internet service providers (ISPs). In current broadband network architectures, the traffic of subscribers is tunneled through a single aggregation point, independent of the different service types it belongs to. While having a single aggregation point eases the management of subscribers for the ISP, it implies huge bandwidth requirements for the aggregation point and potentially high end-to-end latency for subscribers. An alternative would be a distributed subscriber management, adding more complexity to the management itself. In this paper, a new traffic management architecture is proposed that uses the concept of Software Defined Networking (SDN) to extend the existing Ethernet-based broadband network architecture, enabling a more efficient traffic management for an ISP. By using SDN-enabled home gateways, the ISP can configure traffic flows more dynamically, optimizing throughput in the network, especially for bandwidth-intensive services. Furthermore, a proof-of-concept implementation of the approach is presented to show the general feasibility and study configuration tradeoffs. Analytic considerations and testbed measurements show that the approach scales well with an increasing number of subscriber sessions.

The peer-to-peer approach can greatly help to cope with highly dynamic live streaming workload by using idle client resources. Yet, P2P streaming typically comes at the cost of increased streaming delays caused by the inevitable multi-hop forwarding of content by peers within the overlay. Various P2P streaming approaches have been proposed aiming at a good tradeoff between flexibility, streaming delay, and costs in terms of traffic overhead for both content providers and clients. Recently, BitTorrent Inc. released a new P2P live streaming system termed BTLive, specifically targeted at low delay and low overhead. For content providers investigating the applicability of BTLive's approach, it is essential to understand its properties as well as its limitations. So far, no publicly available study exists that quantitatively analyzes BTLive's performance. To this end, this paper presents a measurement study of the official beta version of BTLive. The study aims to answer the following key questions: How peer-to-peer is BTLive? How delay optimized is BTLive? What is the overhead of BTLive? To answer these questions, traces of real BTLive traffic between a broadcast server and a number of peers deployed across Europe have been analyzed.

Peer-assisted delivery of video content has shown a great potential to reduce upload bandwidth requirements for content providers by exploiting idle client resources in the video dissemination process. As primary content sources, the servers run by content providers play a critical role in such systems, making their adequate provisioning a key part of the streaming mechanism. While dynamic resource provisioning has been studied before, little is known about resource allocation for streaming of scalable media content. Besides the pure amount of resources, here, the quality level of the delivered video content becomes relevant. The spreading of video blocks with the wrong quality can lead to situations where peers are forced to reduce their video qualities, despite them having enough download capacity. To address this problem, in this paper, a new SVC-based adaptation policy and a request-based extension to it are proposed, enabling content providers to manage their streaming services in a video quality-aware manner. Prototypical evaluations show that the mechanisms outperform existing quality-agnostic approaches in terms of delivered SVC video quality.

The Peer-to-Peer (P2P) paradigm offers scalable means to perform bulk data distribution, e.g., for small businesses which cannot afford huge upfront investments, by incorporating user's resources in the dissemination process. Due to the proliferation of smartphones with wireless broadband connectivity and the increasing convergence of fixed and mobile platforms, a growing number of users are expected to participate in P2P content distribution networks wirelessly. However, the P2P approach only works if users are willing to contribute resources. A commonly applied incentive scheme is the well-known Tit-for-Tat approach, where each peer is forced to contribute as much bandwidth to the network as he consumes. Nevertheless, reciprocal schemes discriminate resource poor mobile devices in terms of energy and upload bandwidth, as they are device-bound instead of being user-bound. In this work, an incentive scheme featuring virtual nodes is presented, which allows mobile devices to seek help from other devices owned by its user, e.g., the user's home gateway or a supporting cloud instance. Preliminary results are presented in the scope of a P2P streaming scenario.

The increased availability and data rates of cellular 3G/4G networks combined with the growing use of mobile applications highly affect the Quality of Experience (QoE) perceived by the end-user. The QoE is affected in two ways: First, the data rates in the networks are low when multiple users simultaneously request content; second, the transmission of data over slow connections consumes a considerable amount of energy compared to faster connections. Both can be avoided by better management of the available resources. This paper proposes a new approach, taking the energy efficiency into account as a key QoE aspect. Based on user mobility models, the available connectivity can be predicted, from which estimates for the energy consumption and expected QoE can be derived. An architecture is sketched, which combines QoE prediction for current and future network connections with energy efficiency on mobile devices.

Determining the energy consumption in large-scale overlay simulations is still an open issue as most existing simulation frameworks are agnostic to that aspect. Especially simulations including mobile devices, such as smartphones or tablet PCs, can benefit from having a energy consumption model in simulations such that newly developed large-scale overlay mechanisms can be evaluated with respect to their energy consumption on mobile devices. Therefore, this paper proposes a component-based energy consumption model, which is based on measurements of existing smartphones. The model causes little computational overhead, thus, being suitable for large-scale simulations. A brief evaluation shows that using our model, the energy consumption can be estimated with a mean error of ±4.7%. Furthermore, the measurements conducted to derive the model show that WiFi and Bluetooth communication are one, respectively two, orders of magnitude more energy efficient than cellular communication.

Mobile ad hoc networks (MANETs) represent a crucial alternative to deploy applications in urban areas. In those networks, it is inevitable that all nodes are aware of the current system state to adapt their behavior according to the varying conditions. However, existing decentralized monitoring solutions for MANETs only locate the required information at a set of nodes, which are in charge of serving the remaining network, while the availability of information depends on the accessibility of those nodes. To avoid these limitations, BlockTree is a novel, fully decentralized monitoring approach for MANETs that leverages each node's resources to capture and distribute the system state to all nodes. Exploiting its hierarchical structure, BlockTree introduces the concept of location-aware monitoring delivering detailed as well as aggregated information. Through robust communication paired with the stateless design, BlockTree provides accurate results in the presence of fast moving nodes or over an error-prone communication medium.

Location-based services have become increasingly popular in the recent years due to the vast deployment of position-aware devices such as smartphones and tablet PCs and the ubiquitous availability of fast Internet connectivity. Existing location-based services are realized as cloud services, which cause considerably high costs. Furthermore, they are not location-aware leading to unnecessary long transmission paths between the users and the cloud infrastructure. The concept of Peer-to-Peer has proven to be a valid alternative for realizing the functionality of location-based services, which resulted in a plethora of approaches for location-based search [1], [4], [5]. Existing concepts, however, suffer from two major drawbacks: (i) they are not robust against high peer churn and (ii) they do not allow for the persistent storage of location-based data. To this end, in this demo we present the prototype of the overlay Geodemlia [3], which allows for both: the persistent storage of location-based information as well as the reliable search even under high churn rates. Location-based information in Geodemlia is stored in a location-aware way, reducing the length of the transmission path for store and search operations.

Existing peer-to-peer (p2p) overlays for location-based services suffer from two major drawbacks: (i) they do not store data persistently under peer churn and (ii) they do not allow for the fast retrieval of large files, especially under asymmetric link conditions. This tremendously limits the use of current and future p2p location-based services as users are not able to share larger files such as high resolution pictures or video snippets. To overcome these two problems, we present GeoSwarm: a reliable multi-source download scheme for p2p location-based services. GeoSwarm combines the strengths of a BitTorrent-like download scheme with the locality awareness of an overlay for location-based search and its built-in replication mechanism. Thereby, a robust and fast download as well as a reliable storage of location-related multimedia content is achieved. Through extensive evaluation, we show that 95% of all downloads in GeoSwarm are carried out successfully even under churn, while downloads benefit from a 100% increased throughput in comparison to traditional single-source downloads.

Internet video streaming causes the second largest transfer volume and is the second fastest growing application class in Internet traffic analysis [3]. In this context, also the streaming of live content becomes increasingly relevant as more traditional broadcasters start delivering content over the Internet. Today, live video streaming services rely on IP-unicast delivery or closed IP-multicast systems inside single administrative domains. Approaches such as Content Delivery Networks (CDNs) are used to improve the unicast delivery of content. They usually end at the edge of the residential broadband access Internet Service Provider (ISP) networks that connect end users to the Internet. For live content delivery, network layer multicast would be desirable for ISPs as well as content providers to reduce the load due to parallel unicast connections for the same content. Because of the well-known drawbacks and limitations of IP-multicast [2], however, network layer multicast support is usually not available.

Existing peer-to-peer overlay approaches for location-based search have proven to be a valid alternative to client-server-based schemes. One of the key issues of the peer-to-peer approach is the high churn rate caused by joining and leaving peers. To address this problem, this paper proposes a new location-aware peer-to-peer overlay termed Geodemlia to achieve a robust and efficient location-based search. To evaluate Geodemlia, a real world workload model for peer-to-peer location-based services is derived from traces of Twitter. Using the workload model, a system parameter analysis of Geodemlia is conducted with the goal of finding a suitable parameter configuration. In addition, the scalability and robustness of Geodemlia is compared to a state-of-the-art tree-based approach by investigating the performance and costs of both overlays under an increasing number of peers, an increasing radius of area searches, an increasing level of churn as well as for different peer placement and search request schemes. The evaluation results reveal that in contrast to the tree-based approach, Geodemlia provides on average a 46% better success ratio as well as a 18% better recall at a moderate higher traffic overhead of 13 bytes/s and an increased average response time of 0.2 s.

The proliferation of wireless broadband technologies and mobile devices has led to an increase in mobile traffic, especially due to a rapid growth of real-time entertainment such as video streaming on smartphones. Mobile peer-to-peer-based content distribution schemes can help to relieve infrastructure based mobile networks, but require participating nodes to provide resources which can drain their battery. Thus, the goal is to exploit mobile peers' resources while minimizing and balancing the energy consumption over all participating devices. Simulation models considering energy consumption lack precision, because they abstract away important parts of the hardware. On the other hand, prototypical energy measurements are more precise, but require a time consuming implementation and assessment. To this end, this demo paper presents a mobile P2P video streaming and benchmarking platform which enables to assess and compare the energy consumption of different approaches in a precise manner through live assessments at runtime. The demonstrated platform includes a simple, yet high-performance tree-based mobile P2P streaming overlay which can be utilized to easily implement and assess further streaming overlay approaches.

Networking researchers complain that the current Internet is ossified, i.e. that it can hardly be changed. We believe that one of the fundamental reasons for that is the lack of appropriate incentives for providers to invest in new technology, especially in the absence of a compelling new architecture and a killer application that would benefit from an alternative architecture. There is a chicken-and-egg problem: In order to come up with exciting new applications, there needs to be an infrastructure supporting them. Researchers have proposed to build network testbeds (e.g. GENI/FIRE) to test new network architectures and protocols at larger scale. However, these testbeds appear to have little attraction for users, in particular for commercially oriented application developers. OpenFlow is an alternative approach enabling experimental protocols in production networks. However, one of its limitations is lack of addressing provider incentives. In this position paper, we therefore sketch the characteristics that we think a new Internet platform should have in order to be compelling. We argue for a platform that offers rich programmability at low performance cost and that separates traffic to enhance security and limit interference among applications. Moreover, the platform should be open and accessible to a wide community of users and have a high usability in terms of being easily programmable by application developers. Finally, we believe the new platform should provide support for running sophisticated applications across multiple provider domains.

The emergence of high bandwidth public wireless networks and miniaturized personal mobile devices gives rise to new mobile healthcare services. To this end, the COPD24 system provides highly customizable vital signs tele-monitoring and tele-treatment for Chronic Obtrusive Pulmonary Disease (COPD) patients based on a body area network (BAN) and a mobile health care (m-health) service platform utilizing next generation public wireless networks. The developed system allows the incorporation of diverse medical sensors via wireless connections, and the live transmission of the measured vital signs to healthcare providers as well as real-time feedback to the patient. The system also incorporates environmental state sensors, e.g., air quality sensors deployed in access points of Future Internet (FI) infrastructure, enabling to derive high-granularity, up to date information about the state of the air the patient is in and thus enabling him to make an informed choice on how to manage his health. In this paper we present the COPD system case study, and we derive its design implications for m-health and FI technologies.

The popularity of video sharing over the Internet has increased significantly. High traffic generated by such applications at the source can be better distributed using a peer-to-peer (P2P) overlay. Unlike most P2P systems, LiveShift combines both live and on-demand video streaming while video is transmitted through the peer-to-peer network in a live fashion, all peers participate in distributed storage. This adds the ability to replay time-shifted streams from other peers in a distributed and scalable manner. This paper describes an adaptive fully-distributed mesh-pull protocol that supports the envisioned use case and a set of policies that enable efficient usage of resources, discussing interesting trade-offs encountered. User-focused evaluation results, including both channel switching and time shifting behavior, show that the proposed system provides good quality of experience for most users, in terms of infrequent stalling, low playback lag, and a small proportion of skipped blocks in all the scenarios studied, even in presence of churn.

Resource allocations determine an important management task for operational Grids and networks, especially under the constraint of commercially offered resources. Therefore, the need for an optimal allocation of this task arises, and this paper proposes a trust-incentive-based combinatorial double auction algorithm for these resource allocations in Grids. The key and new contribution is the design of a trust-incentive mechanism, which is integrated into an existing combinatorial double auction algorithm (a) to improve the performance of Grid resource allocation and (b) ensure that trust values of participating bidders (typically Grid users, termed peers) are considered. In the newly developed trust-incentive-based algorithm, each peers' trust value is adopted to adjust their bids in the process of the combinatorial double auction. After each transaction, peers participating in the transaction rate each other to setup and update the bilateral trust relationship. Those simulation results obtained demonstrate that the algorithm proposed can improve the efficiency of resource sharing greatly by providing applicable incentives to trustworthy peers to contribute more resources. Moreover, this algorithm can identify and eliminate malicious peers in the system to enhance the Grid security level in that respect.

In informal data sharing environments, misspellings cause problems for data indexing and retrieval. This is even more pronounced in mobile environments, in which devices with limited input devices are used. In a mobile environment, similarity search algorithms for finding misspelled data need to account for limited CPU and bandwidth. This demo shows P2P fast similarity search (P2PFastSS) running on mobile phones and laptops that is tailored to uncertain data entry and uses available resources efficiently. In this demo, users publish and search for textual content containing misspellings without relying on query logging, as done by Google, and with a minimum distributed indexing infrastructure. Similarity search is supported by using the concept of deletion neighborhood to evaluate the edit distance metric of string similarity.

Incentive schemes in Peer-to-Peer (P2P) networks are necessary to discourage free-riding. One example is the Tit-for-Tat (TFT) incentive scheme, a variant of which is used in BitTorrent to encourage peers to upload. TFT uses data from local observations making it suitable for systems with direct reciprocity. This paper presents CompactPSH, an incentive scheme that works with direct and indirect reciprocity. CompactPSH allows peers to establish indirect reciprocity by finding intermediate peers, thus enabling trade with more peers and capitalizing on more resources. CompactPSH finds transitive paths while keeping the overhead of additional messages low. In a P2P file-sharing scenario based on input data from a large BitTorrent tracker, CompactPSH was found to exploit more reciprocity than TFT which enabled more chunks to be downloaded. As a consequence, peers are allowed to be stricter to fight white-washing without compromising performance.

Peer-to-peer (P2P) systems are, in contrast to client-server (C/S) systems, fault-tolerant, robust, and scalable. While C/S distributed file systems, such as NFS (network file system) or SMB (server message block), do not scale with respect to the number of clients and exhibit a single point of failure, P2P file systems have the potential to cope with an increasing number of participants. Thus, this paper presents DRFS (distributed reliable file system), a P2P file system for cooperative environments. DRFS uses random, content-independent identifiers for data storage, while maintaining high performance and low overhead with many concurrent reads and writes. A dynamic replication mechanism ensures data availability, even under high churn. The application scenario considers an office environment, where DRFS is installed on employees' machines, who store and request files. DRFS has been implemented using the Filesystem in Userspace (FUSE) interface, in order to provide users with transparent read and write operations. Experiments show the benefits of such a peer-to-peer architecture, when a small number of peers reads or writes in parallel: DRFS performs better than NFS, as soon as 6 peers read or write in parallel a 32 MB file. For unpopular files, it is also more reliable than IgorFS.

One of the key challenges in peer-to-peer networks is the design of incentives to encourage peers to share their resources. Incentives are necessary in order to exploit the full potential of these systems. The tit-for-tat incentive scheme, as used in BitTorrent for example, has proven to be a successful approach in P2P file sharing systems, where peers have direct relations and share the same kind of resources. However, in P2P systems where different kind of resources are shared between peers with non-direct relations, the design of incentives remains a challenge. In this paper, a large-scale, fully decentralized P2P collaboration network is shown, where peers share not only bandwidth and storage space, but also contribute by editing articles and voting for or against changes. A new incentive scheme is proposed which supports non-direct relations and provides incentives for sharing different kind of resources. The incentive scheme is based on a reputation system that assigns a reputation value to every peer reflecting its previous behavior in the network. Based on this value, the service level is differentiated, i.e. the higher a peer's reputation the better the quality of service it can get from the network. The service differentiation has been analyzed and simulated with rational, irrational and altruistic peers based on game theory concepts.

Peer-to-peer (P2P) systems show numerous advantages over centralized systems, such as load balancing, scalability, and fault tolerance, and they require certain functionality, such as search, repair, and message and data transfer. In particular, structured P2P networks perform an exact search in logarithmic time proportional to the number of peers. However, keyword similarity search in a structured P2P network remains a challenge. Similarity search for service discovery can significantly improve service management in a distributed environment. As services are often described informally in text form, keyword similarity search can find the required services or data items more reliably. This paper presents a fast similarity search algorithm for structured P2P systems. The new algorithm, called P2P fast similarity search (P2PFastSS), finds similar keys in any distributed hash table (DHT) using the edit distance metric, and is independent of the underlying P2P routing algorithm. Performance analysis shows that P2PFastSS carries out a similarity search in time proportional to the logarithm of the number of peers. Simulations on PlanetLab confirm these results and show that a similarity search with 34,000 peers performs in less than three seconds on average. Thus, P2PFastSS is suitable for similarity search in large-scale network infrastructures, such as service description matching in service discovery or searching for similar terms in P2P storage networks.

The increasing assortment of devices with IP connectivity contributes to the high popularity of video sharing over the Internet. High traffic generated by such applications at the source can be better distributed using a peer-to-peer overlay, since every user forwards information to other users. Current implementations target either live or on demand video streaming. LiveShift is an application that combines both approaches. While video is transmitted through the peer-to-peer network in a live fashion, all peers participate in a distributed storage. This adds ability to replay time-shifted streams from other peers in a distributed and scalable manner. For the demonstration, a decentralized network is used, with peers running on EMANICSLab nodes and notebook computers.

Peer-to-peer (P2P) systems are flexible, robust, and self-organizing resource sharing infrastructures which are typically designed in a fully decentralized manner. However, a key problem of such systems are peers overusing a resource. This paper presents a fully decentralized scheme to achieve fair use in P2P systems, which does not require a priori information about a peer. The approach developed is based on a scarce resource trading scheme (SRTCPU), which utilizes CPU time as a form of payment. SRTCPU provides an incentive to offer CPU time in return of consuming a scarce resource. A distributed DNS has been implemented as an example application that uses SRTCPU.

P2P networks are becoming increasingly popular for a wide variety of applications going beyond pure file sharing. However, a commercial use of P2P technology is currently not possible as efficient and reliable market mechanisms are missing. This paper presents PeerMart, a distributed technology in support of a market for trading P2P services. PeerMart combines the economic efficiency of double auctions with the technical efficiency and resilience of structured P2P networks. The system is implemented on top of a redundant P2P infrastructure and is being evaluated with respect to scalability, efficiency, and reliability.

Auction-based pricing mechanisms offer the flexibility of setting prices for goods dynamically and efficiently based on current supply and demand. This paper presents PeerMart, a new mechanism which combines the economic efficiency of auction-based pricing with the technical performance and resilience of P2P networks. PeerMart supersedes the need for a central auctioneer by distributing broker functionality over all peers in the network. A structured and redundant P2P overlay network design is applied to achieve scalability and robustness even in the presence of malicious peers.

We present a token-based accounting mechanism that alleviates the free riding problem in P2P networks. The approach is complemented by distributed pricing as a flexible and viable scheme to incite users to share valuable content and to efficiently balance requests among all peers based on economic decisions.