Search

Simulation and emulation are popular approaches for experimentation in Computer Networks. However, due to their respective inherent drawbacks, existing solutions cannot perform both fast and realistic control plane experiments. To close this gap, we introduce Horse. Horse is a hybrid solution with an emulated control plane, for realism, and simulated data plane, for speed. Our decoupling of the control and data plane allows us to speed up the experiments without sacrificing control plane realism.

Social Web 2.0 features have become a vital component in a variety of multimedia systems, e.g., YouTube, Last.fm. Interestingly, adult video websites are also starting to adopt these Web 2.0 principles, giving rise to the term "Porn 2.0". This paper examines a large Porn 2.0 social network, through data covering 563k users. We explore a number of unusual behavioural aspects that set this apart from more traditional multimedia social networks. We particularly focus on the role of gender and sexuality, to understand how these different groups behave. A number of key differences are discovered relating to social demographics, modalities of interaction and content consumption habits, shedding light on this understudied area of online activity.

A Click-based network function (NF) has significant advantages for pipeline development, including modularity, extensibility, and programmability. Despite these features, its internal architecture has unfortunately not kept up with some specific problems of the software upgrade. To motivate our article, we analyzed a series of use cases to identify the limitations of native Click. These limitations include the inefficiencies in modifying modules, integrating modules, and recovering states. To bridge the gap, we present three novel enhancements in our Click upgrade (Click-UP) system: 1) modular state abstraction refines each type of stateful operations as an atom operation and decouples it from the pipeline, letting separately managing logics for stateless operations and stateful operations become practical; 2) essential module integration manages dependencies between modules, avoiding shipping unnecessary modules with neutral functionalities to the target NF; and 3) local state migration migrates needed states seamlessly from the old NF to the target NF at local memory. Our evaluation demonstrates that Click-UP reduces the context code required for module modification by 12–81%, cutting down the NF integration time by 78–96% and the service disruption time by 76–93%, as compared to the software upgrade performance represented by native Click.

Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments., Comment: Accepted for Publication in Elsevier IoT Journal, 2022

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing., Comment: 49 page, 15 figures, 10 tables

The global pandemic has forced lecturers and academics to transform how computer science is taught. Discover how innovative technology and techniques are being used during lectures that can now reach an international audience.

When network is undergoing problems such as congestion, scan attack, DDoS attack, etc , measurements are much more important than usual. In this case, traffic characteristics including available bandwidth, packet rate, and flow size distribution vary drastically, significantly degrading the performance of measurements. To address this issue, we propose the Elastic sketch. It is adaptive to currently traffic characteristics. Besides, it is generic to measurement tasks and platforms. We implement the Elastic sketch on six platforms: P4, FPGA, GPU, CPU, multi-core CPU, and OVS, to process six typical measurement tasks. Experimental results and theoretical analysis show that the Elastic sketch can adapt well to traffic characteristics. Compared to the state-of-the-art, the Elastic sketch achieves 44.6 ~ 45.2 times faster speed and 2.0 ~ 273.7 smaller error rate.

Finding top- $k$ elephant flows is a critical task in network traffic measurement, with many applications in congestion control, anomaly detection and traffic engineering. As the line rates keep increasing in today’s networks, designing accurate and fast algorithms for online identification of elephant flows becomes more and more challenging. The prior algorithms are seriously limited in achieving accuracy under the constraints of heavy traffic and small on-chip memory in use. We observe that the basic strategies adopted by these algorithms either require significant space overhead to measure the sizes of all flows or incur significant inaccuracy when deciding which flows to keep track of. In this paper, we adopt a new strategy, called count-with-exponential-decay , to achieve space-accuracy balance by actively removing small flows through decaying, while minimizing the impact on large flows, so as to achieve high precision in finding top- $k$ elephant flows. Moreover, the proposed algorithm called HeavyKeeper incurs small, constant processing overhead per packet and thus supports high line rates. Experimental results show that HeavyKeeper algorithm achieves 99.99% precision with a small memory size, and reduces the error by around 3 orders of magnitude on average compared to the state-of-the-art.

Current IP-based networks support a wide range of delay-sensitive applications such as live video streaming of network gaming. Providing an adequate quality of experience to these applications is of paramount importance for a network provider. The offered services are often regulated by tight Service Level Agreements (SLAs) that needs to be continuously monitored. Since the first step to guarantee a metric is to measure it, delay measurement becomes a fundamental operation for a network provider. In many cases, the operator needs to measure the delay on all network links. We refer to the collection of all link delays as the Link Delay Vector (LDV). Typical solutions to collect the LDV impose a substantial overhead on the network. In this paper, we propose a solution to measure the LDV in real-time with a low-overhead approach. In particular, we inject some flows into the network and infer the LDV based on the delay of those flows. To this end, the monitoring flows and their paths should be selected minimizing the network monitoring overhead. In this respect, the challenging issue is to select a proper combination of flows such that by knowing their delay it is possible to solve a set of linear equations and obtain a unique LDV. This combination of monitoring flows should be optimal according to some criteria and should meet some feasibility constraints. We first propose a mathematical formulation to select the optimal combination of flows, in form of an Integer Linear Programming (ILP) problem. Then we develop a heuristic algorithm to overcome the high computational complexity of existing ILP solvers. As a further step, we propose a meta-heuristic algorithm to solve the above-mentioned equations and infer the LDV. The challenging part of this step is the volatility of link delays. The proposed solution is evaluated over real-world emulated network topologies using the Mininet network emulator. Emulation results show the accuracy of the proposed solution with a negligible networking overhead in a real-time manner.

This editorial note aims to first inform the SIGCOMM community on the reviewing process in place currently at CCR, and second, share our plans to make CCR a more open and welcoming venue by making changes to the review process, adding more value to the SIGCOMM community.

Hypergiants, such as Google or Netflix, are important organisations in the Internet ecosystem, due to their sheer impact in terms of traffic volume exchanged. However, beyond naming specific instances, the research community still lacks a sufficiently crisp understanding of them. In this paper we analyse PeeringDB data and identify features that differentiate hypergiants from the other organisations. To this end, we first characterise the organisations present in PeeringDB, allowing us to identify discriminating properties of these organisations. We then use these properties to separate the data in two clusters, differentiating hypergiants from other organisations. We conclude this paper by investigating how hypergiants and other organisations exploit the IXP ecosystem to reach the global IPv4 space.

The importance of IXPs to interconnect different networks and exchange traffic locally has been well studied over the last few years. However, far less is known about the role IXPs play as a platform to enable large-scale content delivery and to reach a world-wide customer base. In this paper, we study the infrastructure deployment of a content hypergiant, Netflix, and show that the combined worldwide IXP substrate is the major corner stone of its Content Delivery Network. To meet its worldwide demand for high-quality video delivery, Netflix has built a dedicated CDN. Its scale allows us to study a major part of the Internet ecosystem, by observing how Netflix takes advantage of the combined capabilities of IXPs and ISPs present in different regions. We find wide disparities in the regional Netflix deployment and traffic levels at IXPs and ISPs across various local ecosystems. This highlights the complexity of large-scale content delivery as well as differences in the capabilities of IXPs in specific regions. On a global scale we find that the footprint provided by IXPs allows Netflix to deliver most of its traffic directly from them. This highlights the additional role that IXPs play in the Internet ecosystem, not just in terms of interconnection, but also allowing players such as Netflix to deliver significant amounts of traffic.

This paper studies the problem of compressing the forwarding information base (FIB), but taking a wider perspective. Indeed, FIB compression goes beyond sheer compression, as the gain in memory use obtained from the compression has consequences on the updates that will have to be applied to the compressed FIB. We are interested in the situation where forwarding rules can change over time, e.g., due to border gateway protocol (BGP) route updates. Accordingly, we frame FIB compression as an online problem and design competitive online algorithms to solve it. In contrast to prior work which mostly focused on static optimizations, we study an online variant of the problem where routes can change over time and where the number of updates to the FIB is taken into account explicitly. The reason to consider this version of the problem is that leveraging temporal locality while accounting for the number of FIB updates helps to keep routers CPU load low and reduces the number of FIB updates to be transferred, e.g., from the network-attached software-defined network controller to a remote switch. This paper introduces a formal model which is an interesting generalization of several classic online aggregation problems. Our main contribution is an O(w) -competitive algorithm, where ${w}$ is the length of an IP address. We also derive a lower bound which shows that our result is asymptotically optimal within a natural class of algorithms, based on so-called sticks .

This July 2020 issue contains four technical papers, the second paper of our education series, as well as two editorial notes.

Presentation of the April 2020 issue of CCR.

DNS is a vital component for almost every networked application. Originally it was designed as an unencrypted protocol, making user security a concern. DNS-over-HTTPS (DoH) is the latest proposal to make name resolution more secure. In this paper we study the current DNS-over-HTTPS ecosystem, especially the cost of the additional security. We start by surveying the current DoH landscape by assessing standard compliance and supported features of public DoH servers. We then compare different transports for secure DNS, to highlight the improvements DoH makes over its predecessor, DNS-over-TLS (DoT). These improvements explain in part the significantly larger take-up of DoH in comparison to DoT. Finally, we quantify the overhead incurred by the additional layers of the DoH transport and their impact on web page load times. We find that these overheads only have limited impact on page load times, suggesting that it is possible to obtain the improved security of DoH with only marginal performance impact.

Under increasing scrutiny, many web companies now offer bespoke mechanisms allowing any third party to file complaints (e.g., requesting the de-listing of a URL from a search engine). While this self-regulation might be a valuable web governance tool, it places huge responsibility within the hands of these organisations that demands close examination. We present the first large-scale study of web complaints (over 1 billion URLs). We find a range of complainants, largely focused on copyright enforcement. Whereas the majority of organisations are occasional users of the complaint system, we find a number of bulk senders specialised in targeting specific types of domain. We identify a series of trends and patterns amongst both the domains and complainants. By inspecting the availability of the domains, we also observe that a sizeable portion go offline shortly after complaints are generated. This paper sheds critical light on how complaints are issued, who they pertain to and which domains go offline after complaints are issued., The Web Conference 2019

The advent of software defined networking enables flexible, reliable and feature-rich control planes for data center networks. However, the tight coupling of centralized control and complete visibility leads to a wide range of issues among which scalability has risen to prominence due to the excessive workload on the central controller. By analyzing the traffic patterns from a couple of production data centers, we observe that data center traffic is usually highly skewed and thus edge switches can be clustered into a set of communication-intensive groups according to traffic locality. Motivated by this observation, we present LazyCtrl, a novel hybrid control plane design for data center networks where network control is carried out by distributed control mechanisms inside independent groups of switches while complemented with a global controller. LazyCtrl aims at bringing laziness to the global controller by dynamically devolving most of the control tasks to independent switch groups to process frequent intra-group events near the datapath while handling rare inter-group or other specified events by the controller. We implement LazyCtrl and build a prototype based on Open vSwitch and Floodlight. Trace-driven experiments on our prototype show that an effective switch grouping is easy to maintain in multi-tenant clouds and the central controller can be significantly shielded by staying “lazy”, with its workload reduced by up to 82 percent.

The prevalence of Internet video streaming challenges the design and operation of modern networks. Content centric networking (CCN) has been proposed to address the challenges through ubiquitous in-network caching. While the expected benefits include higher performance and lower bandwidth consumption, CCN introduces new privacy issues at layer 3. This is because adversaries could infer the content consumed by others by checking cached data in routers. In this paper, we first analyze the design space to improve both caching performance and cache privacy for video delivery in CCN. In light of the observation that these two metrics need to be balanced, we propose CodingCache. It adopts network coding and random forwarding to exploit the potentials of multipath routing in CCN to improve both the diversity of cached content along different paths and the anonymity set for consumers. We evaluate CodingCache through extensive experiments based on a real-world topology and a unique data set of video access logs from a large-scale commercial video service. Our results demonstrate that, compared with the existing CCN strategies, CodingCache is able to increase the cache hit rate while also improve the use of caches across the network, together with reasonable cache privacy.

International audience; Software Defined Internet eXchange Points (SDXs) are a promising solution to the long-standing limitations and problems of interdomain routing. While proposed SDX architectures have improved the scalability of the control plane, these solutions have ignored the underlying fabric upon which they should be deployed. In this paper, we present Umbrella, a software defined interconnection fabric that complements and enhances those architectures. Umbrella is a switching fabric architecture and management approach that improves the overall robustness, limiting control plane dependency and suitable for the topology of any existing Internet eXchange Point (IXP). We validate Umbrella through a real-world deployment on two production IXPs, TouSIX and NSPIXP-3, and demonstrate its use in practice, sharing our experience of the challenges faced.

When network is undergoing problems such as congestion, scan attack, DDoS attack, etc., measurements are much more important than usual. In this case, traffic characteristics including available bandwidth, packet rate, and flow size distribution vary drastically, significantly degrading the performance of measurements. To address this issue, we propose the Elastic sketch. It is adaptive to currently traffic characteristics. Besides, it is generic to measurement tasks and platforms. We implement the Elastic sketch on six platforms: P4, FPGA, GPU, CPU, multi-core CPU, and OVS, to process six typical measurement tasks. Experimental results and theoretical analysis show that the Elastic sketch can adapt well to traffic characteristics. Compared to the state-of-the-art, the Elastic sketch achieves 44.6 ∼ 45.2 times faster speed and 2.0 ∼ 273.7 smaller error rate.

Assessing the impact of changes in a production network (e.g., new routing protocols or topologies) requires simulation or emulation tools capable of providing results as close as possible to those from a real-world experiment. Large traffic loads and complex control-data plane interactions constitute significant challenges to these tools. To meet these challengeswe propose a model for the fast and convenient evaluation of SDN as well as legacy networks. Our approach emulates the network's control plane and simulates the data plane, to achieve high fidelity necessary for control plane behavior, while being capable of handling large traffic loads. We design and implement a proof of concept from the proposed model. The initial results of the prototype, compared to a state-of-the-art solution, shows it can increase the speed of network experiments by nearly 95% in the largest tested network scenario.

Content-centric networking (CCN) is a promising framework to rebuild the Internet's forwarding substrate around the concept of content. CCN advocates ubiquitous in-network caching to enhance content delivery, and thus each router has storage space to cache frequently requested content. In this work, we focus on the cache allocation problem, namely, how to distribute the cache capacity across routers under a constrained total storage budget for the network. We first formulate this problem as a content placement problem and obtain the optimal solution by a two-step method. We then propose a suboptimal heuristic method based on node centrality, which is more practical in dynamic networks with frequent content publishing. We investigate through simulations the factors that affect the optimal cache allocation, and perhaps more importantly we use a real-life Internet topology and video access logs from a large scale Internet video provider to evaluate the performance of various cache allocation methods. We observe that network topology and content popularity are two important factors that affect where exactly should cache capacity be placed. Further, the heuristic method comes with only a very limited performance penalty compared to the optimal allocation. Finally, using our findings, we provide recommendations for network operators on the best deployment of CCN caches capacity over routers.

Innovation in interdomain routing has remained stagnant for over a decade. Recently, IXPs have emerged as economically-advantageous interconnection points for reducing path latencies and exchanging ever increasing traffic volumes among, possibly, hundreds of networks. Given their far-reaching implications on interdomain routing, IXPs are the ideal place to foster network innovation and extend the benefits of SDN to the interdomain level. In this paper, we present, evaluate, and demonstrate EN- DEAVOUR, an SDN platform for IXPs. ENDEAVOUR can be deployed on a multi-hop IXP fabric, supports a large number of use cases, and is highly-scalable while avoiding broadcast storms. Our evaluation with real data from one of the largest IXPs, demonstrates the benefits and scalability of our solution: ENDEAVOUR requires around 70% fewer rules than alternative SDN solutions thanks to our rule partitioning mechanism. In addition, by providing an open source solution, we invite ev- eryone from the community to experiment (and improve) our implementation as well as adapt it to new use cases., European Union’s Horizon 2020 research and innovation programme under the ENDEAVOUR project (grant agreement 644960).

Headers are a critical part of HTTP, and it has been shown that they are increasingly subject to middlebox manipulation. Although this is well known, little is understood about the general regional and network trends that underpin these manipulations. In this paper, we collect data on thousands of networks to understand how they intercept HTTP headers in-the-wild. Our analysis reveals that 25% of measured ASes modify HTTP headers. Beyond this, we witness distinct trends among different regions and AS types; e.g., we observe high numbers of cache headers in poorly connected regions. Finally, we perform an in-depth analysis of the types of manipulations and how they differ across regions.

International audience; While innovation in inter-domain routing has remained stagnant for over a decade, Internet Exchange Points (IXPs) are consolidating their role as economically advantageous interconnection points for reducing path latencies and exchanging ever increasing amounts of traffic. As such, IXPs appear as a natural place to foster network innovation and assess the benefits of Software-Defined Networking (SDN), a recent technological trend that has already boosted innovation within data-center networks. In this paper, we give a comprehensive overview of use cases for SDN at IXPs, which leverage the superior vantage point of an IXP to introduce advanced features like load-balancing and DDoS mit-igation. We discuss the benefits of SDN solutions by analyzing real-world data from one of the largest IXPs. We also leverage insights into IXP operations to not only shape benefits for members but also for operators.

Today, a large fraction of Internet traffic is originated by Content Delivery Networks (CDNs). To cope with increasing demand for content, CDNs have deployed massively distributed infrastructures. These deployments pose challenges for CDNs as they have to dynamically map end-users to appropriate servers without being full+y aware of the network conditions within an Internet Service Provider (ISP) or the end-user location. On the other hand, ISPs struggle to cope with rapid traffic shifts caused by the dynamic server selection policies of the CDNs. The challenges that CDNs and ISPs face separately can be turned into an opportunity for collaboration. We argue that it is sufficient for CDNs and ISPs to coordinate only in server selection, not routing, in order to perform traffic engineering. To this end, we propose Content-aware Traffic Engineering (CaTE), which dynamically adapts server selection for content hosted by CDNs using ISP recommendations on small time scales. CaTE relies on the observation that by selecting an appropriate server among those available to deliver the content, the path of the traffic in the network can be influenced in a desired way. We present the design and implementation of a prototype to realize CaTE, and show how CDNs and ISPs can jointly take advantage of the already deployed distributed hosting infrastructures and path diversity, as well as the ISP detailed view of the network status without revealing sensitive operational information. By relying on tier-1 ISP traces, we show that CaTE allows CDNs to enhance the end-user experience while enabling an ISP to achieve several traffic engineering goals.

The rapid progress of urbanization not only enables higher populations living in modern cities, but also engenders many “urban diseases,” such as air pollution, traffic congestion, and increased energy consumption. Mobile big data, with advanced communication and information technologies, can be collected through a variety of data sources, including cellular networks, the Internet of Things (IoT), social networks, and so on. This allows detailed analysis on the moving patterns of citizens and behaviors of devices deployed in urban areas, which helps grasp the outlines and the inner flows of cities. Based on those observations, prediction of events and their involved crowds and departments in cities can lead the city to become more intelligent and greener. The articles in this special section offer a comprehensive overview of the state-of-the-art developments in technology, application, and theory for mobile big data for urban analytics.

Years after the initial development of the current routing protocols we still lack an understanding of the impact of various parameters on the routes chosen in today's Internet. Network operators are struggling to optimize their routing, but the effectiveness of those efforts is limited. In this article, we study sensitivity of routing stretch and diversity metrics to factors such as policies, topology, IGP weights, etc. using statistical techniques. We confirm previous findings that routing policies and AS size (in number of routers) are the dominating factors. Surprisingly, we find that intra-domain factors only have marginal impact on global path properties. Moreover, we study path inflation by comparing against the paths that are shortest in terms of AS-level/router-level hops or geographic distances. Overall, the majority of routes incur reasonable stretch. From the experience with our Internet-scale simulations, we find it hard to globally optimize path selection with respect to the geographic length of the routes, as long as inter-domain routing protocols do not include an explicit notion of geographic distance in the routing information.

Comparison of graph structures is a frequently encountered problem across a number of problem domains. Comparing graphs requires a metric to discriminate which features of the graphs are considered important. The spectrum of a graph is often claimed to contain all the information within a graph, but the raw spectrum contains too much information to be directly used as a useful metric. In this paper we introduce a metric, the weighted spectral distribution, that improves on the raw spectrum by discounting those eigenvalues believed to be unimportant and emphasizing the contribution of those believed to be important. We use this metric to optimize the selection of parameter values for generating Internet topologies. Our metric leads to parameter choices that appear sensible given prior knowledge of the problem domain: the resulting choices are close to the default values of the topology generators and, in the case of some generators, fall within the expected region. This metric provides a means for meaningfully optimizing parameter selection when generating topologies intended to share structure with, but not match exactly, measured graphs.

Despite the large number of papers on network topology modeling and inference, there still exists ambiguity about the real nature of the Internet AS and router level topology. While recent findings have illustrated the inaccuracies in maps inferred from BGP peering and traceroute measurements, existing topology models still produce static topologies, using simplistic assumptions about power law observations and preferential attachment. Today, topology generators are tightly bound to the observed data used to validate them. Given that the actual properties of the Internet topology are not known, topology generators should strive to reproduce the variability that characterizes the evolution of the Internet topology over time. Future topology generators should be able to express the variations in local connectivity that makes today's Internet: peering relationships, internal AS topology and routing policies each changing over time due to failures, maintenance, upgrades and business strategies of the network. Topology generators should capture those dimensions, by allowing a certain level of randomness in the outcome, rather than enforcing structural assumptions as the truths about Internet's evolving structure, which may never be discovered

Today, the Internet is a large multimedia delivery infrastructure, with websites such as YouTube appearing at the top of most measurement studies. However, most traffic studies have ignored an important domain: adult multimedia distribution. Whereas, traditionally, such services were provided primarily via bespoke websites, recently these have converged towards what is known as “Porn 2.0”. These services allow users to upload, view, rate, and comment on videos for free (much like YouTube). Despite their scale, we still lack even a basic understanding of their operation. This article addresses this gap by performing a large-scale study of one of the most popular Porn 2.0 websites: YouPorn. Our measurements reveal a global delivery infrastructure that we have repeatedly crawled to collect statistics (on 183k videos). We use this data to characterise the corpus, as well as to inspect popularity trends and how they relate to other features, for example, categories and ratings. To explore our discoveries further, we use a small-scale user study, highlighting key system implications.

The advent of software defined networking enables flexible, reliable and feature-rich control planes for data center networks. However, the tight coupling of centralized control and complete visibility leads to a wide range of issues among which scalability has risen to prominence. We observe that data center traffic is usually highly skewed and thus edge switches can be grouped according to traffic locality. As a result, the workload of the central controller could be highly reduced if we carry out distributed control inside those groups. Based on the above observation, we present LazyCtrl, a novel hybrid control plane design for data center networks. LazyCtrl aims at bringing laziness to the central controller by dynamically devolving most of the control tasks to independent switch groups to process frequent intra-group events using distributed control mechanisms, while handling rare inter-group or other specified events by the controller. We implement LazyCtrl and build a prototype based on Open vSwich and Floodlight. Trace-driven experiments on our prototype show that an effective switch grouping is easy to maintain in multi-tenant clouds and the central controller can be significantly shielded by staying lazy, with its workload reduced by up to 82%.

This paper presents a new publicly available dataset from GÉANT, the European Research and Educational Network. This dataset consists of traffic matrices built using full IGP routing information, sampled Netflow data and BGP routing information of the GÉANT network, one per 15 minutes interval for several months. Potential benefits of publicly available traffic matrices comprise improving our understanding of real traffic matrices, their dynamics, and to make possible the benchmarking of intradomain traffic engineering methods

The Internet's layered architecture and organizational structure give rise to a number of different topologies, with the lower layers defining more physical and the higher layers more virtual/logical types of connectivity structures. These structures are very different, and successful Internet topology modeling requires annotating the nodes and edges of the corresponding graphs with information that reflects their network-intrinsic meaning. These structures also give rise to different representations of the traffic that traverses the heterogeneous Internet, and a traffic matrix is a compact and succinct description of the traffic exchanges between the nodes in a given connectivity structure. In this paper, we summarize recent advances in Internet research related to (i) inferring and modeling the router-level topologies of individual service providers (i.e., the physical connectivity structure of an ISP, where nodes are routers/switches and links represent physical connections), (ii) estimating the intra-AS traffic matrix when the AS's router-level topology and routing configuration are known, (iii) inferring and modeling the Internet's AS-level topology, and (iv) estimating the inter-AS traffic matrix. We will also discuss recent work on Internet connectivity structures that arise at the higher layers in the TCP/IP protocol stack and are more virtual and dynamic; e.g., overlay networks like the WWW graph, where nodes are web pages and edges represent existing hyperlinks, or P2P networks like Gnutella, where nodes represent peers and two peers are connected if they have an active network connection.

Today, the complexity of ISPs' networks make it difficult to investigate the implications of internal or external changes on the distribution of traffic across their network. In this article we explain the complexity of building models of large ISPs' networks. We describe the various aspects important to understanding the routing inside an AS. We present an open source routing solver, C-BGP, that eases the investigation of changes in the routing or topology of large networks. We illustrate how to build a model of an ISP on a real transit network and apply the model on two "what-if" scenarios. The first scenario studies the impact of chances in the Internet connectivity of a transit network. The second investigates the impact of failures in its internal topology.

We present an application of multiple-objectives evolutionary optimisation to the problem of engineering the distribution of the interdomain traffic in the internet. We show that this practical problem requires such a heuristic due to the potentially conflicting nature of traffic-engineering objectives. Furthermore, having to work on the parameter's space (BGP routing) of the real problem makes such techniques as evolutionary optimisation very easy to use. We show the successful application of our algorithm to two practically relevant problems in interdomain traffic engineering.

Today, most multi-connected autonomous systems (AS) need to control the flow of their interdomain traffic for both performance and economical reasons. This is usually done by manually tweaking the BGP configurations of the routers on an error-prone trial-and-error basis. In this paper, we demonstrate that designing systematic BGP-based traffic engineering techniques for stub ASes are possible. Our approach to solve this traffic engineering problem is to allow the network operator to define objective functions on the interdomain traffic. Those objective functions are used by an optimization box placed inside the AS that controls the interdomain traffic by tuning the iBGP messages distributed inside the AS. We show that the utilization of an efficient evolutionary algorithm allows to both optimize the objective function and limit the number of iBGP messages. By keeping a lifetime on the tweaked routes, we also show that providing stability to the interdomain path followed by the traffic is possible. We evaluate the performance of solution based on traffic traces from two stub ASes of different sizes. Our simulations show that the interdomain traffic can be efficiently engineered by using not more than a few iBGP advertisements per minute.Our contribution in this paper is to demonstrate that by carefully thinking the design of the interdomain traffic engineering technique, stub ASes can engineer their outbound traffic over relatively short timescales, by exclusively tweaking their BGP routes, and with a minimal burden on BGP. Systematic BGP-based traffic engineering for stub ASes is thus possible at a very limited cost in terms of iBGP messages.

Traffic engineering is performed by means of a set of techniques that can be used to better control the flow of packets inside an IP network. We discuss the utilization of these techniques across interdomain boundaries in the global Internet. We first analyze the characteristics of interdomain traffic on the basis of measurements from three different Internet service providers and show that a small number of sources are responsible for a large fraction of the traffic. Across interdomain boundaries, traffic engineering relies on a careful tuning of the route advertisements sent via the border gateway protocol. We explain how this tuning can be used to control the flow of incoming and outgoing traffic, and identify its limitations.

Even though a key driver for Information-Centric Networking (ICN) has been the rise in Internet video traffic, there has been surprisingly little work on analyzing the interplay between ICN and video ? which ICN caching strategies work well on video work- loads and how ICN helps improve video-centric quality of experience (QoE). In this work, we bridge this disconnect with a trace- driven study using 196M video requests from over 16M users on a country-wide topology with 80K routers. We evaluate a broad space of content replacement (e.g., LRU, LFU, FIFO) and content placement (e.g., leave a copy everywhere, probabilistic) strategies over a range of cache sizes. We highlight four key findings: (1) the best placement and re- placement strategies depend on the cache size and vary across improvement metrics; that said, LFU+probabilistic caching [37] is a close-to-optimal strategy overall; (2) video workloads show considerable caching-related benefits (e.g., -- 10% traffic reduction) only with very large cache sizes (≥ 100GB); (3) the improvement in video QoE is low (≥ 12%) if the content provider already has a substantial geographical presence; and (4) caches in the middle and the edge of the network, requests from highly populated regions and without content servers, and requests for popular content contribute most to the overall ICN-induced improvements in video QoE.

This paper attends to the well-known problem of compressing the Forwarding Information Base of a router or switch, while preserving a correct forwarding. In contrast to related work, we study an online variant of the problem where BGP routes can change over time, and where the number of updates to the FIB are taken into account explicitly. Minimizing the number of FIB updates is important, especially when they are sent across the network (e.g., from the network-attached SDN controller). This paper pursues a competitive analysis approach and introduces a formal model which is an interesting generalization of several classic online aggregation problems. The main contribution is a O (w)-competitive algorithm, where w is the length of an IP address. We also derive a lower bound which shows that our result is asymptotically optimal within a natural class of algorithms.

Today’s web has a huge, diverse ecosystem of third-party websites collecting information about users and providing them with content such as targeted advertisements. In this paper we study this ecosystem of third-party websites. We sample every continent, targeting the 500 most popular websites in the US, UK, Australia, China, Egypt, Iran and Syria. This allows us to contrast the commonplace, westerndominated views of the web with less studied countries. We find 2,097 third-party web services, reflecting the diversity of services and types of application/content they involve, e.g., advertisement, ad trackers, CDNs, news, sport, and pornography. We find those third-party web services offering ad tracking services to be the most prevalent. In addition to the usual suspects (e.g., DoubleClick and Google), we find a rich ecosystem of local third-party websites that are country and language dependent

Software-Defined Networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound APIs, network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms -- with a focus on aspects such as resiliency, scalability, performance, security and dependability -- as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment., Comment: Version 2.01: 61 pages, 11 figures, 17 tables, 579 references