One of the recent methods for effectively utilising the available bandwidth of the network as conditions change is in-network packet trimming. It has advantages for applications which rely on low-latency and low-loss, such as video streaming. Packet trimming allows a network node to reduce the size of packets as they travel across the network, by removing some of the content, during the journey of each packet. In our previous work, we presented a system for layered SVC video streaming, and showed how the trimming process can be successfully implemented and utilised as a virtualized edge deployed function. If trimming is needed, there is a task which has to unpack all the video content and meta-data, including associated significance values, then determine which data to remove, and finally repack and rebuilding a new packet. Conversely, a simple scheme for packet trimming, in the form of a basic buffer cutting process, has been implemented in switches. This process keeps the first bytes of a packet, and drops the rest. Here we investigate if a buffer cutting process can be used for layered video, and describe the issues arising and the design aspects needed to adapt from packet rebuilding to packet cutting.

The utilization of the packet trimming technique as part of 6G has previously been described. It allows a network node to reduce packet sizes, by removing some of the content, during the journey of each packet. This approach has been successful in many ways, but can be limited when there is very little bandwidth. In this paper, we propose an approach using an architecture suitable for 6G, which overcomes some of these issues by removing extra content from the packets, when network conditions are restricted.

We propose ACCT, an end-to-end learning-based congestion control mechanism for Internet flows that aims to achieve an optimal sending rate through collaboration between applications and the network elements along the network path. Specifically, an ACCT sender can exploit explicit feedback from the network about its ability to satisfy the application's desired sending rate. Unlike existing schemes, ACCT does not blindly adjust its sending rate to either of these signals; instead, it considers them advisory because the bottleneck may be at routers that do not support ACCT signaling. To detect such bottlenecks and strictly control the end-to-end delay for latency-sensitive applications, ACCT actively measures the end-to-end queuing delay and regulates its sending rate to keep queuing delay within an acceptable margin. Finally, ACCT heavily relies on a Reinforcement Learning (RL) agent to adjust the aggressiveness of its sending rate. We propose a novel approach to formulate the RL agent, exploiting multiple reward functions simultaneously to train the agent. We implemented the ACCT congestion control module in NS-3, which communicates with the RL agent via a technology-agnostic protocol. We evaluated performance across various network scenarios in both wired and wireless networks. Across all experiments, ACCT significantly outperforms commonly used TCP variants such as CUBIC; it provides network fairness when competing with other ACCT flows, and finally, ACCT flows coexist well when they carry heterogeneous traffic.

This paper presents DNS++, a re-design of the Internet's name resolution system that addresses dynamic information and privacy. DNS++ uses a pub/sub overlay to send updates about a given service to interested clients, allowing them to (re)select between replicas according to their requirements, as updates about services and their features dynamically change. Since third-party brokers in the overlay are not always trusted for the confidentiality of the content flowing through them, clients' privacy is preserved in DNS++ through homomorphic encryption. Brokers are prevented from accessing encrypted service information but can perform homomorphic match and forward service updates to relevant clients through the overlay accordingly. Assuming that forwarding tables in each broker are implemented via ordered data structures, the time required for adding a new client's subscription, and to perform homomorphic match between existing subscriptions and service updates, would grow logarithmically with the number of entries within a table. This is shown by our performance evaluation, which confirms that DNS++ is feasible to be deployed with an acceptable performance overhead.

Despite the breakthroughs in end-to-end encryption that keeps the content of Internet data confidential, the fact that packet headers contain source and IP addresses remains a strong violation of users' privacy. This paper describes a routing mechanism that allows for connections to be established where no provider, including the final destination, knows who is connecting to whom. The system makes use of inter-domain source routing with public key cryptography to establish connections and simple private symmetric encryption in the data path that allows for fully stateless packet transmission. We discuss the potential implications of real deployment of our routing mechanism in the Internet.

In a packet network, the routes taken by traffic can be determined according to predefined objectives. Assuming that the network conditions remain static and the defined objectives do not change, mathematical tools such as linear programming could be used to solve this routing problem. However, networks can be dynamic or the routing requirements may change. In that context, Reinforcement Learning (RL), which can learn to adapt in dynamic conditions and offers flexibility of behavior through the reward function, presents as a suitable tool to find good routing strategies. In this work, we train an RL agent, which we call R2L, to address the routing problem. The policy function used in R2L is a neural network and we use an evolution strategy algorithm to determine its weights and biases. We tested R2L in two different scenarios: static and dynamic networks conditions. In the first, we used a 16-node network and experimented with different reward functions, observing that R2L was able to adapt its routing behavior accordingly. Finally, in the second experiment, we used a 5-node network topology where a given link's transmission rate changed during the simulation. In this scenario, we observed that R2L was able to deliver a competitive performance, compared to heuristic benchmarks, with changing network conditions.

It has been claimed that the deployment of fog and edge computing infrastructure is a necessity to make high-performance cloud-based applications a possibility. However, there are a large number of middle-ground latency-sensitive applications such as online gaming, interactive photo editing and multimedia conferencing that require servers deployed closer to users than in globally centralised clouds but do not necessarily need the extreme low-latency provided by a new infrastructure of micro data centres located at the network edge, e.g., in base stations and ISP Points of Presence. In this paper we analyse a snapshot of today's data centres and the distribution of users around the globe and conclude that existing infrastructure provides a sufficiently distributed platform for middle-ground applications requiring a response time of 20-200 ms. However, while placement and selection of edge servers for extreme low-latency applications is a relatively straightforward matter of choosing the closest, providing a high quality of experience for middle-ground latency applications that use the more widespread distribution of today's data centres, as we advocate in this paper, raises new management challenges to develop algorithms for optimising the placement of and the per-request selection between replicated service instances.

We present the first empirical study of the impact of loss-related errors on TV viewing engagement across disparate platforms, delivery technologies and performance measures. Our dataset comprises anonymized video viewing sessions and data about quality of delivery from a content service provider with a nationwide customer base. We study buffering events on streaming apps, mild and severe packet loss errors on multicast-delivered IPTV to a Set-Top-Box (STB) and signal strength errors on Digital Terrestrial TV. Since these metrics cannot be directly compared to each other, we use engagement as our proxy measure. We first characterize the relationship between each impairment and viewing engagement, investigating confounding factors such as type of content, asset length and connection type. We conclude that the loss of engagement due to poor quality delivery is incurred immediately for on-demand content and in the long-term for live content. We rank impairments across platforms by their impact on engagement.

In this work, we propose a Deep Learning (DL) based solution to the problem of routing traffic flows in computer networks. Routing decisions can be made in different ways depending on the desired objective and, based on that objective function, optimal solutions can be computed using a variety of techniques, e.g. with mixed integer linear programming. However, determining these solutions requires solving complex optimization problems and, thus, cannot be typically done at runtime. Instead, heuristics for these problems are often created but designing them is non-trivial in many cases. The routing framework proposed here presents an alternative to the design of heuristics, whilst still achieving good performance. This is done by building a DL model trained on the optimal decisions over flows from known traffic demands. To evaluate our solution, we focused on the problem of network congestion, even though a wide range of alternative objectives could be fitted into this framework. We ran experiments using two publicly available datasets of networks with real traffic demands and showed that our solution achieves close-to-optimal network congestion values.

Advanced Internet services increasingly rely on many components to implement their functionality. These composite services have three important features: they are expensive to deploy, components need to be placed intelligently close to the users to improve quality of experience and they will potentially consume significant amounts of bandwidth. This paper presents Triptych, a multi-objective optimisation framework that tries to optimise according these three dimensions to help the three main stakeholders in the Internet ecosystem: users, application providers and network providers. Triptych implements evolutionary computation approaches for this complex problem, which simultaneously optimise service deployment costs, latency-based user utility and network congestion. These algorithms provide possible operating points, bringing important tools for network managements and resource allocation. A large set of simulations under different scenarios are provided to validate the algorithms.

Multi-user video conferencing is a ubiquitous technology. Increasingly end-hosts in a conference are assisted by cloud-based servers that improve the quality of experience for end users. This paper evaluates the impact of strategies for placement of such servers on user experience and deployment cost. We consider scenarios based upon the Amazon EC2 infrastructure as well as future scenarios in which cloud instances can be located at a larger number of possible sites across the planet. We compare a number of possible strategies for choosing which cloud locations should host services and how traffic should route through them. Our study is driven by real data to create demand scenarios with realistic geographical user distributions and diurnal behaviour. We conclude that on the EC2 infrastructure a well chosen static selection of servers performs well but as more cloud locations are available a dynamic choice of servers becomes important.

The dominant application in today's Internet is content streaming, which is increasingly relying on caches to meet the stringent conditions on the latency between content servers and end-users. These systems routinely face the challenges of limited bandwidth capacities and network server failures, which degrade caching performance. In this paper, we study the problem of optimally allocating content over a resilient caching network, in which each cache may fail under some situations. Given content request rates and multiple routing paths, we formulate an optimization problem to maximize the expected caching gain, i.e., the reduction of latency due to intermediate caching. The offline version of this problem is NP-hard. We first propose a centralized, offline algorithm and show that a solution with (1-1/e) approximation ratio to the optimal can be constructed. We then propose a distributed ascent algorithm based on the concave relaxation of the expected gain. Informed by the results of our analysis, we finally propose a distributed resilient caching algorithm (DR-Cache) that is simple and adaptive to network failures. We show numerically that DR-Cache significantly outperforms other candidate algorithms under synthetic requests, as well as real world traces over a class of network topologies.

This paper describes how mobile device apps can inadvertently broadcast personal information through their use of wireless networks despite the correct use of encryption. Using a selection of personas we illustrate how app usage can be tied to personal information. Users would likely assume the confidentiality of personal information (including age, religion, sexuality and gender) when using an encrypted network. However, we demonstrate how encrypted traffic pattern analysis can allow a remote observer to infer potentially sensitive data passively and undetectably without any network credentials. Without the ability to read encrypted WiFi traffic directly, we process the limited side-channel data available (timings and frame sizes) to enable remote app detection. These side-channel data measurements are represented as histograms and used to construct a Random Forest classifier capable of accurately identifying mobile apps from the encrypted traffic they cause. The Random Forest algorithm was able to correctly identify apps with a mean accuracy of ∼99% within the training set. The classifier was then adapted to form the core of a detection program that could monitor multiple devices in real-time. Tests in a closed-world scenario showed 84% accuracy and demonstrated the ability to overcome the data limitations imposed by WiFi encryption. Although accuracy suffers greatly (67%) when moving to an open-world scenario, a high recall rate of 86% demonstrates that apps can unwittingly broadcast personal information openly despite using encrypted WiFi. The open-world false positive rate (38% overall, or 72% for unseen activity alone) leaves much room for improvement but the experiment demonstrates a plausible threat nevertheless. Finally, avenues for improvement and the limitations of this approach are identified. We discuss potential applications, strategies to prevent these leaks, and consider the effort required for an observer to present a practical privacy threat to the everyday WiFi user. This paper presents and demonstrates a nuanced and difficult to solve privacy vulnerability that cannot not be mitigated without considerable changes to current- and next-generation wireless communication protocols.

The emergence of distributed clouds opens up new research challenges for service deployment. Composite services consist of multiple components, potentially located in different geographical locations, which need to be interconnected and invoked in the correct order according to the overall service work-flow. The placement of composite services over distributed cloud node locations raises new challenges for efficient deployment and management. In this paper, we design exact models of the composite service placement problems using mixed integer linear program, and compare these to solutions based on genetic algorithms. We use a utility function, based initially on latency metrics, to evaluate the quality of service (QoS) of the deployed composite service. By maximizing the utility with respect to deployment cost, our approach can provide good QoS for users while satisfying budget constraints for service providers. Based on simulations using real data-center locations and traffic demand patterns, we show that our algorithms are scalable under a range of scenarios.

This paper is focused on techniques for maximizing utility across all users within a total network transit cost budget. We present a new method for selecting between replicated servers distributed over the Internet. First, we introduce a novel utility framework that factors in quality of service metrics. Then we design an optimization algorithm, solvable in polynomial time, to allocate user requests to servers based on utility while satisfying network transit cost constraints, mapping service names to service instance locators. We then describe an efficient, low overhead distributed model which only requires knowledge of a fraction of the data required by the global optimization formulation. Next, a load-balancing variant of the algorithm is explored that substantially reduces blocking caused by congested servers. Extensive simulations show that our method is scalable and leads to higher user utility compared with mapping user requests to the closest service replica, while meeting network traffic cost constraints. We discuss several options for real-world deployment that require no changes to end-systems based on either the use of SDN controllers or extensions to the current DNS system.

Cloud computing has revolutionized service delivery by providing on-demand invocation and elasticity. To reap these benefits, computation has been displaced from client devices and into data centers. This partial centralization is undesirable for applications that have stringent locality requirements, e.g., low latency. This problem could be addressed with large numbers of smaller cloud resources closer to users. However, as cloud computing diffuses from within data centers and into the network, there will be a need for cloud resource allocation algorithms that operate on resource-constrained computational units that serve localized subsets of customers. In this paper, we present a mechanism for service provisioning in distributed clouds where applications compete for resources. The mechanism operates by enabling execution zones to assign resources based on Vickrey auctions and provides high-quality probabilistic models that applications can use to predict the outcomes of such auctions. This allows applications to use knowledge of the locality distribution of their clients to accurately select the number of bids to be sent to each execution zone and their value. The proposed mechanism is highly scalable, efficient, and validated by extensive simulations.

This paper describes TARDIS (Traffic Assignment and Retiming Dynamics with Inherent Stability) which is an algorithmic procedure designed to reallocate traffic within Internet Service Provider (ISP) networks. Recent work has investigated the idea of shifting traffic in time (from peak to off-peak) or in space (by using different links). This work gives a unified scheme for both time and space shifting to reduce costs. Particular attention is given to the commonly used 95th percentile pricing scheme.

Cloud Computing facilitates the new generation of IT and the provisioning of services by introducing virtualization concepts throughout the network. Multi-tenancy introduces the capability to share cloud assets among service users. Service providers currently lack innovative means to enable true multi-tenancy in a cloud environment. There is an urgent need for technologies that introduce tenant security, traffic identification and tenant separation for better billing and fairness. This paper introduces a technology that tackles all these requirements by defining the concept of a Tenant-ID that is incorporated into the OSI Layer 2 frame header. We outline the concept of Tenant-IDs, the adapted 802.1q header and a way to automatically configure tenants via Cloud automation tools and software-defined networking approaches. The paper concludes by outlining interoperability to relevant layer-2 protocols and by introducing use-cases benefiting from the Tenant-ID concept.

This paper considers fundamental measurements which drive TCP flows: throughput, RTT and loss. It is clear that throughput is, in some sense, a function of both RTT and loss. In their seminal paper Padyhe et al [1] begin with a mathematical model of the TCP sliding window evolution process and come up with an equation showing that TCP throughput is (roughly) proportional to 1/RTT√p where p is the probability of packet loss. Their equation is shown to be consistent with data gathered on several links. This paper takes the opposite approach and analyses a large number of packet traces from well-known sources in order to create a data-driven estimate of the functions which relate TCP, loss and RTT. Regression analysis is used to fit models to connect the quantities. The fitted models show different behaviour from that expected in [1].

When designing distributed systems and Internet protocols, designers can benefit from statistical models of the Internet that can be used to estimate their performance. However, it is frequently impossible for these models to include every property of interest. In these cases, model builders have to select a reduced subset of network properties, and the rest will have to be estimated from those available. In this paper we present a technique for the analysis of Internet round trip times (RTT) and its relationship with other geographic and network properties. This technique is applied on a novel dataset comprising ~19 million RTT measurements derived from ~200 million RTT samples between ~54 thousand DNS servers. Our main contribution is an information-theoretical analysis that allows us to determine the amount of information that a given subset of geographic or network variables (such as RTT or great circle distance between geolocated hosts) gives about other variables of interest. We then provide bounds on the error that can be expected when using statistical estimators for the variables of interest based on subsets of other variables.

This paper presents a mechanism for inferring user behaviour from encrypted wireless network activity. Aside from being within range, this mechanism operates without any level of network access, and without the need to break any encryption. It demonstrates how an entirely passive, external observer can detect if and when a person is using Skype. It is further demonstrated that this detection ability remains even when Skype traffic is interleaved with confounding simultaneous traffic such as BitTorrent. The metrics utilised by the detection process are a consequence of efficient data transfer. It is reasonable to believe that similar methods would be effective on the majority of modern wireless communications, and not specific to the protocols underlying the demonstration. The approach shown challenges the assumption that secure cryptography means secure information and adaptations of the mechanism used may help guide the analysis of increasingly large volumes of encrypted data.

There is growing evidence that mutually beneficial outcomes can be achieved when content distribution overlays and their underlying ISPs collaborate through open interfaces. We further contribute to this body of work by considering consolidated topology construction strategies that integrate the information provided by multiple ISPs. We focus on situations with potentially conflicting, asymmetric preference costs, since these situations are expected to benefit more from the tradeoffs provided by consolidation to produce an overlay topology with desirable global properties. In this paper we develop a generic model for the multi-domain consolidation of ISP preferences expressed as costs for pairwise peer connections, where peers are grouped into clusters based on topology criteria. Using this model, we propose two consolidated topology construction strategies: Shared Cost, designed to provide a tradeoff for preference cost asymmetries, and Low Cost, designed to reduce the overall preference cost that the overlay imposes on all its underlying ISPs. We evaluate these two models through extensive simulations over a wide range of ISP and peer cluster sizes, and we show that preference consolidation can provide ISPs with outcomes more aligned with their preferences than those provided by non-consolidated operation.

Abstract This article presents three methods to forecast accurately the amount of traffic in TCP/IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt‐Winters). In order to assess their accuracy, several experiments were held using real‐world data from two large Internet service providers. In addition, different time scales (5 min, 1 h and 1 day) and distinct forecasting lookaheads were analysed. The experiments with the neural ensemble achieved the best results for 5 min and hourly data, while the Holt‐Winters is the best option for the daily forecasts. This research opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.

Evolutionary Algorithms (EAs) have been used to develop methods for Traffic Engineering (TE) over IP-based networks in the last few years, being used to reach the best set of link weights in the configuration of intra-domain routing protocols, such as OSPF. In this work, the multiobjective nature of a class of optimization problems provided by TE with Quality of Service constraints is identified. Multiobjective EAs (MOEAs) are developed to tackle these tasks and their results are compared to previous approaches using single objective EAs. The effect of distinct genetic representations within the MOEAs is also explored. The results show that the MOEAs provide more flexible solutions for network management, but are in some cases unable to reach the level of quality obtained by single objective EAs. Furthermore, a freely available software application is described that allows the use of the mentioned optimization algorithms by network administrators, in an user-friendly way by providing adequate user interfaces for the main TE tasks.

This paper presents Packet Re-cycling (PR), a technique that takes advantage of cellular graph embeddings to reroute packets that would otherwise be dropped in case of link or node failures. The technique employs only one bit in the packet header to cover any single link failures, and in the order of log2(d) bits to cover all non-disconnecting failure combinations, where d is the diameter of the network. We show that our routing strategy is effective and that its path length stretch is acceptable for realistic topologies. The packet header overhead incurred by PR is very small, and the extra memory and packet processing time required to implement it at each router are insignificant. This makes PR suitable for loss-sensitive, mission-critical network applications.

In this paper, we propose a mechanism for packet marking called Probabilistic Congestion Notification (PCN). This scheme makes use of the 1-bit Explicit Congestion Notification (ECN) field in the Internet Protocol (IP) header. It allows the source to estimate the exact level of congestion at each intermediate queue. By knowing this, the source could take avoiding action either by adapting its sending rate or by using alternate routes. The estimation mechanism makes use of time series analysis both to improve the quality of the congestion estimation and to predict, ahead of time, the congestion level which subsequent packets will encounter. The proposed protocol is tested in ns-2 simulator using a background of real Internet traffic traces. Results show that the methods can successfully calculate the congestion at any queue along the path with low error levels.

In this paper, we propose a routing technique, “recursive Loop-Free Alternates (rLFAs)”, to alleviate packet loss due to transient link failures. The technique consists of a backup path calculation with corresponding re-routing scheme based on the Loop-Free Condition (LFC) as defined in the basic specification for IP Fast Re-Route (IPFRR). Under this routing strategy, nodes calculate backup paths by modifying the weights of links in the primary shortest path tree. If a failure occurs, the detecting node determines the number of recursions, which indicates the number of times packets must be forwarded along the alternate next hops to bypass the failed link. This technique guarantees full repair coverage for single link failures. We evaluate the performance of our proposed technique through simulations and show that the incurred overheads, the stretch of its pre-computed alternate paths, and the failure-state Maximum Link Utilisation (MLU) are minimal.

This paper criticises the notion that long-range dependence is an important contributor to the queuing behaviour of real Internet traffic. The idea is questioned in two different ways. Firstly, a class of models used to simulate Internet traffic is shown to have important theoretical flaws. It is shown that this behaviour is inconsistent with the behaviour of real traffic traces. Secondly, the notion that long-range correlations significantly affects the queuing performance of traffic is investigated by destroying those correlations in real traffic traces (by reordering). It is shown that the longer ranges of correlations are not important except in one case with an extremely high load.

Although direct reciprocity (Tit-for-Tai) contribution systems have been successful in reducing freeloading in peer- to-peer overlays, it has been shown that, unless the contribution network is dense, they tend to be slow (or may even fail) to converge. On the other hand, current indirect reciprocity mechanisms based on reputation systems tend to be susceptible to sybil attacks, peer slander and whitewashing. In this paper we present PledgeRoute, an accounting mechanism for peer contributions that is based on social capital. This mechanism allows peers to contribute resources to one set of peers and use this contribution to obtain services from a different set of peers, at a different time. PledgeRoute is completely decentralised, can be implemented in both structured and unstructured peer-to-peer systems, and it is resistant to the three kinds of attacks mentioned above. To achieve this, we model contribution transitivity as a routing problem in the contribution network of the peer-to-peer overlay, and we present arguments for the routing behaviour and the sybilproofness of our contribution transfer procedures on this basis. Additionally, we present mechanisms for the seeding of the contribution network, and a combination of incentive mechanisms and reciprocation policies that motivate peers to adhere to the protocol and maximise their service contributions to the overlay.

Unsolicited e-mail (spam) is a severe problem due to intrusion of privacy, online fraud, viruses and time spent reading unwanted messages. To solve this issue, Collaborative Filtering (CF) and Content-Based Filtering (CBF) solutions have been adopted. We propose a new CBF-CF hybrid approach called Symbiotic Data Mining (SDM), which aims at aggregating distinct local filters in order to improve filtering at a personalized level using collaboration while preserving privacy. We apply SDM to spam e-mail detection and compare it with a local CBF filter (i.e. Naive Bayes). Several experiments were conducted by using a novel corpus based on the well known Enron datasets mixed with recent spam. The results show that the symbiotic strategy is competitive in performance when compared to CBF and also more robust to contamination attacks.

The production of a large-scale monitoring system for a high-speed network leads to a number of challenges. These challenges are not purely technical but also socio-political and legal. The number of stakeholders in such monitoring activity is large including the network operators, the users, the equipment manufacturers and, of course, the monitoring researchers. The MASTS project (measurement at all scales in time and space) was created to instrument the high-speed JANET Lightpath network and has been extended to incorporate other paths supported by JANET(UK). Challenges the project has faced included: simple access to the network; legal issues involved in the storage and dissemination of the captured information, which may be personal; the volume of data captured and the rate at which these data appear at store. To this end, the MASTS system will have established four monitoring points each capturing packets on a high-speed link. Traffic header data will be continuously collected, anonymised, indexed, stored and made available to the research community. A legal framework for the capture and storage of network measurement data has been developed which allows the anonymised IP traces to be used for research purposes.

Significant research has been dedicated to active probing methods used to estimate the end-to-end available bandwidth (avail-bw) of a network path. The measurement speed of an avail-bw estimation tool is vital to real-time network services such as transport control protocol (TCP) slow start, path selection for P2P applications and video streaming services and so on. In addition, a fast avail-bw estimation tool sends less probing traffic to a measured network. Pathload, a one way delay (OWD) based tool, has been widely regarded as one of the most accurate tools, but its estimation speed is often 10 or more seconds per measurement. A fast OWD-based avail-bw estimation scheme has been explored. This scheme is based on the proposed asymptotic OWD comparison model, and an estimation tool called Pathpair has been developed. Validated in a real Internet environment, experimental results show that with Pathpair the measurement speed has been significantly improved (1–3 s per measurement), while preserving accuracy compared with other major tools like Pathload and Spruce.

Peer-to-peer networks providing QoS-enabled services are sensitive to hidden action situations, where the actions of a server peer are hidden from the peers who receive services from it. This is because server peers can choose to strategically minimize their effort, and client peers may be unable to distinguish between cases where the server exerted insufficient effort and cases where the server kept its advertised effort levels but the end-to-end conditions in the network were sufficiently adverse. We propose a principal-agent model for hidden action that gives server peers sufficient incentives to meet their advertised effort levels, without client peers having to decide for each transaction whether the outcome was due to server behavior or network conditions. This allows peers to draft contracts that provide incentives for truthful revelation of QoS capabilities, and to have predictable transaction quality. We then exemplify the model for the case of a mesh-based, pull-oriented streaming system with low delay requirements. For this case, we show how to estimate the model parameters a function of the prevailing network conditions, and how to enforce contract fulfillment through a reciprocative strategy.

OSPFOSPF is the most common intra-domain routing protocol in Wide Area Networks. Thus, optimizing OSPF weights will produce tools for traffic engineering with Quality of Service constraints, without changing the network management model. Evolutionary Algorithms (EAs) provide a valuable tool to face this NP-hard problem, allowing flexible cost functions with several metrics of the network behavior. A novel framework is proposed that enriches current models for network congestion with delay constraints, setting the basis for EAs that allocate OSPF weights, guided by a bi-objective cost function. The results show that EAs make an efficient method, outperforming common heuristics and achieving effective network behavior under unfavorable scenarios.

The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a neural network ensemble (NNE) for the prediction of TCP/IP traffic using a time series forecasting (TSF) point of view. Several experiments were devised by considering real-world data from two large Internet Service Providers. In addition, different time scales (e.g. every five minutes and hourly) and forecasting horizons were analyzed. Overall, the NNE approach is competitive when compared with other TSF methods (e.g. Holt-Winters and ARIMA).