Traceroute is the most widely used Internet path analysis tool today to study the topology of the Internet and to diagnose routing failures as well as poor performance events. A major limitation of traceroute when the destination is not controllable by the user is its inability to measure reverse paths, i.e., the path from any given destination back to the source. This is a major drawback for ISPs, who need to understand the performance of the Internet paths connecting popular services (e.g., YouTube and Facebook) to their customers. Even if public servers and distributed measurement platforms can provide partial reverse path visibility through ad-hoc measurements, there is still a need for a structured approach capable of analyzing the performance of Internet paths connecting any pair of nodes (servers, routers, hosts, etc.). While the problem of reverse traceroute has been addressed in the past, proposed techniques rely on IP address spoofing – which might lead to security concerns, and assume the availability of certain route-tracking options –, which might not be available. In this paper, we introduce and evaluate DisNETPerf, a new tool which provides exactly the same type of information as traceroute, but for paths connecting arbitrarily selected nodes. DisNETPerf works by firstly locating probes (i.e., measurement points) that are the closest to a given target node, using them to perform traceroute measurements from the target point-of-view to a given destination for path performance monitoring and troubleshooting purposes. We propose two techniques for probe location, and demonstrate that the reverse path (from server to users) can be measured with very high accuracy in certain scenarios. We also analyze relevant characteristics of Internet paths and distributed measurement platforms, which reinforce the applicability and relevance of DisNETPerf in current Internet.

This paper proposes a network proximity service based on the neighborhood models used in recommender systems. Unlike previous approaches, our service infers network proximity without trying to recover the latency between network nodes. By asking each node to probe a number of landmark nodes which can be servers at Google, Yahoo and Facebook, etc., a simple proximity measure is computed and allows the direct ranking and rating of network nodes by their proximity to a target node. The service is thus lightweight and can be easily deployed in e.g. P2P and CDN applications. Simulations on existing datasets and experiments with a deployment over PlanetLab showed that our service achieves an accurate proximity inference that is comparable to state-of-the-art latency prediction approaches, while being much simpler.

This paper investigates the rating of network paths, i.e., acquiring quantized measures of path properties such as round-trip time and available bandwidth. Compared to fine-grained measurements, coarse-grained ratings are appealing in that they are not only informative but also cheap to obtain. Motivated by this insight, we first address the scalable acquisition of path ratings by statistical inference. By observing similarities to recommender systems, we examine the applicability of solutions to a recommender system and show that our inference problem can be solved by a class of matrix factorization techniques. A technical contribution is an active and progressive inference framework that not only improves the accuracy by selectively measuring more informative paths, but also speeds up the convergence for available bandwidth by incorporating its measurement methodology. Then, we investigate the usability of rating-based network measurement and inference in applications. A case study is performed on whether locality awareness can be achieved for overlay networks of Pastry and BitTorrent using inferred ratings. We show that such coarse-grained knowledge can improve the performance of peer selection and that finer granularities do not always lead to larger improvements.

In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two contributions: a conceptual one and an algorithmical one. The conceptual contribution is to formulate – as an example – the real-world problem of preventing SPIT (Spam in VoIP networks), which is currently not satisfyingly addressed by standard techniques, as a sequential learning problem, namely as a contextual multi-armed bandit. Our second contribution is to present CMABFAS, a new algorithm for general contextual multi-armed bandit learning that specifically targets domains with finite actions. We illustrate how CMABFAS could be used to design a fully self-learning SPIT filter that does not rely on feedback from the end-user (i.e., does not require labeled data) and report first simulation results.

This paper presents a formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work is largely different from related previous work: our goal is to rigorously formalize the problem in terms of mathematical decision theory, find the optimal solution to the problem, and derive concrete bounds for its expected loss (number of mistakes the SPIT filter will make in the worst case). This goal is achieved by considering an abstracted scenario amenable to theoretical analysis, namely SPIT detection in an outbound scenario with pure sources. Our methodology is to first define the cost of making an error (false positive and false negative), apply Wald's sequential probability ratio test to the individual sources, and then determine analytically error probabilities such that the resulting expected loss is minimized. The benefits of our approach are: (1) the method is optimal (in a sense defined in the paper); (2) the method does not rely on manual tuning and tweaking of parameters but is completely self-contained and mathematically justified; (3) the method is computationally simple and scalable. These are desirable features that would make our method a component of choice in larger, autonomic frameworks.

The basic concept of “an Open Industry Framework” is proposed. Current industry framework is closed and industries are making their efforts to produce best quality products in their own particular field. This is because current industries developed on the basis of their inventions. But quickly increasing diversification and frequent and extensive changes are calling for an open framework. In this new framework, industries operate on the basis of Parallel Distributed Processing model. They process multiple tasks in parallel among different types of industries. To achieve such a goal, modular production is called for but current ones are not fully integrated between design and manufacturing. We have to develop a module system which integrates design and manufacturing and which can be shared across industries. When such a system is developed, industries will be divided into two groups. One is subsystem suppliers and the other is system integrators. The subsystem here works in the same way as Lego pieces. Such a framework will enable to distribute production activities to local factories according to their capabilities so that it will increase local employment and their buying power. And we do not have to carry heavy final product over a long distance. Thus, an Open Industry Framework will reduce time to market, cost and energy consumption considerably and industries can be more flexible and adaptive and can satisfy the basic needs of customers.

Migrating services to the cloud brings all the benefits of elasticity, scalability and cost-cutting. However, migrating services among different cloud infrastructures or outside of the cloud is not an obvious task. In addition, distributing services among multiple cloud providers, or on a hybrid installation requires a custom implementation effort that must be repeated at each infrastructure change. This situation raises the lock-in problem and discourages cloud adoption. Cloud computing open standards were designed to face this situation and to bring interoperability and portability to cloud environments. However, they target isolated resources, and do not take into account the notion of complete services. In this paper, we introduce an extension to OCCI, a cloud computing open standard, in order to support complete service definition and management automation. We support this proposal with an open-source framework for service management through compliant cloud

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a few pairs and to predict the other ones without actually measuring them. This paper formulates the prediction problem as matrix completion where the unknown entries in a pairwise distance matrix constructed from a network are to be predicted. By assuming that the distance matrix has low-rank characteristics, the problem is solvable by low-rank approximation based on matrix factorization. The new formulation circumvents the well-known drawbacks of existing approaches based on Euclidean embedding. A new algorithm, so-called Decentralized Matrix Factorization by Stochastic Gradient Descent (DMFSGD), is proposed. By letting network nodes exchange messages with each other, the algorithm is fully decentralized and only requires each node to collect and to process local measurements, with neither explicit matrix constructions nor special nodes such as landmarks and central servers. In addition, we compared comprehensively matrix factorization and Euclidean embedding to demonstrate the suitability of the former on network distance prediction. We further studied the incorporation of a robust loss function and of nonnegativity constraints. Extensive experiments on various publicly available datasets of network delays show not only the scalability and the accuracy of our approach, but also its usability in real Internet applications.

In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two contributions: a conceptual and an algorithmical one. The conceptual contribution is to formulate the real-world problem of preventing HTTP-based attacks on web servers as a one-shot sequential learning problem, namely as a contextual multi-armed bandit. Our second contribution is to present CMABFAS, a new and computationally very cheap algorithm for general contextual multi-armed bandit learning that specifically targets domains with finite actions. We illustrate how CMABFAS could be used to design a fully self-learning meta filter for web servers that does not rely on feedback from the end-user (i.e., does not require labeled data) and report first convincing simulation results.

We present the design of DISco, a storage and communication middleware that enables distributed and task-centric autonomic control of networks. DISco allows multi-agent identification of anomalous situations (challenges) and assists coordinated remediation that will maintain service at an acceptable level, although degraded. The history of agents decisions, their context and outcomes is tracked as the situation evolves, and information is automatically gathered and organised to ease further human-assisted diagnosis. We then explore the feasibility of using state of the art peer-to-peer publish/subscribe and storage systems as building blocks for this service. The ability of those systems to support range queries and aggregation will be a key factor for their suitability to the task.

This paper presents the first formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work deviates from related earlier work where this problem is only addressed by ad-hoc solutions. Our goal is to rigorously formalize the problem in terms of mathematical decision theory, find the optimal solution to the problem, and derive concrete bounds for its expected loss (number of mistakes the SPIT filter will make in the worst case). This goal is achieved by considering a scenario amenable to theoretical analysis, namely SPIT detection in an outbound scenario with pure sources. Our methodology is to first define the cost of making an error, apply Wald's sequential probability ratio test, and then determine analytically error probabilities such that the resulting expected loss is minimized. The benefits of our approach are: (1) the method is optimal (in a sense defined in the paper); (2) the method does not rely on manual tuning and tweaking of parameters but is completely self-contained and mathematically justified; (3) the method is computationally simple and scalable. These are desirable features that would make our method a component of choice in larger, autonomic frameworks.

This paper addresses the large-scale acquisition of end-to-end network performance. We made two distinct contributions: ordinal rating of network performance and inference by matrix completion. The former reduces measurement costs and unifies various metrics which eases their processing in applications. The latter enables scalable and accurate inference with no requirement of structural information of the network nor geometric constraints. By combining both, the acquisition problem bears strong similarities to recommender systems. This paper investigates the applicability of various matrix factorization models used in recommender systems. We found that the simple regularized matrix factorization is not only practical but also produces accurate results that are beneficial for peer selection.

In this study, a nuclear hybrid energy system (NHES) with large-scale hydrogen storage integrated with a gas turbine cycle is proposed as a flexible system for load following. The proposed system consists of a nuclear reactor, a steam Rankine cycle, a hydrogen electrolyzer, a storage system for hydrogen in an underground salt cavern, and a Brayton cycle that uses hydrogen as fuel to generate additional electricity to meet peak demand. A dynamic mathematical model is developed for each subsystem of the NHES. To evaluate the potential benefits of the system, a one-year study is conducted, using scaled grid demand data from ISO New England. The dynamic simulation results show that the system is capable of meeting the demand of the grid without additional electricity from outside sources for 93% of the year, while decreasing the number of ramping cycles of the nuclear reactor by 92.7%. There is also potential for economic benefits as the system only had to ramp up and down 7.4% of the year, which increased the nuclear capacity factor from 86.3% to 98.3%. The simulation results show that the proposed hybrid system improves the flexibility of nuclear power plants, provides more electricity, and reduces greenhouse gas emissions.

In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two contributions: a conceptual one and an algorithmical one. The conceptual contribution is to formulate -- as an example -- the real-world problem of preventing SPIT (Spam in VoIP networks), which is currently not satisfyingly addressed by standard techniques, as a sequential learning problem, namely as a contextual multi-armed bandit. Our second contribution is to present CMABFAS, a new algorithm for general contextual multi-armed bandit learning that specifically targets domains with finite actions. We illustrate how CMABFAS could be used to design a fully self-learning SPIT filter that does not rely on feedback from the end-user (i.e., does not require labeled data) and report first simulation results.

In large-scale networks, full-mesh active probing of end-to-end performance metrics is infeasible. Measuring a small set of pairs and predicting the others is more scalable. Under this framework, we formulate the prediction problem as matrix completion, whereby unknown entries of an incomplete matrix of pairwise measurements are to be predicted. This problem can be solved by matrix factorization because performance matrices have a low rank, thanks to the correlations among measurements. Moreover, its resolution can be fully decentralized without actually building matrices nor relying on special landmarks or central servers. In this paper we demonstrate that this approach is also applicable when the performance values are not measured exactly, but are only known to belong to one among some predefined performance classes, such as "good" and "bad". Such classification-based formulation not only fulfills the requirements of many Internet applications but also reduces the measurement cost and enables a unified treatment of various performance metrics. We propose a decentralized approach based on Stochastic Gradient Descent to solve this class-based matrix completion problem. Experiments on various datasets, relative to two kinds of metrics, show the accuracy of the approach, its robustness against erroneous measurements and its usability on peer selection.

Overlay routing is a promising way to improve the quality of service in the Internet but its main drawback is scalability: measuring the characteristics of the paths, exchanging the measurement results between the nodes and computing the best routes in the full mesh overlay network generally imply a high consumption of resources. In this paper, we design the basis of a lightweight self-organising one-hop overlay routing mechanism improving the latencies: we define criteria that rely on the information provided by an Internet Coordinate System (ICS) in order to provide a small set of potential one-hop shortcuts for any given path in the network with a small measurement cost. Our best criterion does not guarantee to find the best shortcut for any given path in a network but, even in networks with hundreds or thousands of nodes, it will restrict the search for potential shortcuts to about one or two percent of the total number of nodes.

With the spread of new and innovative Internet services such as SIP-based communications, the challenge of protecting and defending these critical applications has been raised. In particular, SIP firewalls attempt to filter the signaling unwanted activities and attacks based on the knowledge of the SIP protocol. Optimizing the SIP firewall configuration at real-time by selecting the best filtering rules is problematic because it depends on both natures of the legal traffic and the unwanted activities. More precisely, we do not know exactly how the unwanted activities are reflected in the SIP messages and in what they differ from the legal ones. In this paper, we address the case of Spam over Internet Telephony (SPIT) mitigation. We propose an adaptive solution based on extracting signatures from learnt decision trees. Our simulations show that quickly learning the optimal configuration for a SIP firewall leads to reduce at lowest the unsolicited calls as reported by the users under protection. Our results promote the application of machine learning algorithms for supporting network and service resilience against such new challenges.