Due to rush-hour effects, transient analysis may be necessary to model and optimize vehicle sharing systems. However, existing methods for transient modeling face computational challenges at scale. To solve this, we present trajectory-based iteration (TBI), a method for the efficient decomposed modeling of vehicle-sharing systems, and we extend it to a scalable method for non-convex, transient optimization problems. The proposed TBI method works by iteratively solving spatial submodels, then periodically updating the vehicle flows between submodels. We then present experimental results where our method results in a 37% improvement in runtime for solving fluid models for mobility systems. Then, we present a case study in which our optimization method results in an estimated 22% improvement in the objective function for a vehicle-sharing system with offline rebalancing.

This poster presents the first steps towards a data-driven methodology to design material for raising awareness on personal data collection and use by smartphone apps for mobility services (e.g., trip planning). Through an online survey, we collected a sample of 300 responses from regular users of these apps in the Paris region, in France, who share their perspectives on privacy concerns and their need to be better informed.

We investigate how environmental data collected from a low-cost, distributed Internet-of-Things platform can be used to identify factors affecting air quality in highly dense urban environments. In this work, we focus on the the case of Bangkok. We use data collected from the SEA-HAZEMON platform to analyze the incidence of several parameters on PMx level and assess its potential to forecast CO2 emission.

The ability to predict network dynamics is a challenging problem for communication providers as this can have an impact on the performance of the applications running on top of their infrastructure. In this paper we use a data-driven approach to investigate whether observing the variation of the connectivity properties of a network over time can provide insights onto the occurrence of network topological changes. In that direction, we propose a methodology to model various patterns of network dynamics and develop a user-configurable software tool enabling the collection of network connectivity metric data for any type of communication network topology and based on a wide range of dynamics patterns. We use our tool to constitute a dataset of network connectivity metric samples based on 126 real network topologies and 15 different types of dynamics patterns. The analysis performed based on the obtained dataset shows that identifying how connectivity metrics vary has potential for characterizing network topological changes.

Emerging services with demand for high perfor¬mance and resource availability call for network management solutions that enable distributed, flexible and adaptive im¬plementation of their components. Software-based networks, characterized by modular structures, are key in supporting management functionality with the desired level of agility. An important challenge in realizing a modular management plane is the handling of interactions between independent decision¬making applications operating in parallel, which can compromise service delivery. This paper presents LogiSig, a novel approach to automatically detect and classify interactions manifesting in the management plane of software-based networks. LogiSig builds upon a fine-grained taxonomy of possible interaction types and simple system-level operations to realize its functionality. Our evaluation results show that LogiSig can efficiently determine interactions between application pairs.

Network operators have recently been developing multi-Gbps traffic monitoring tools on commodity hardware, as part of the packet-processing pipelines realizing software dataplanes. These solutions allow the execution of sophisticated per-packet monitoring using the processing power available on servers. Although advances in packet capture have enabled the interception of packets at high rates, bottlenecks can still arise in the monitoring process as a result of concurrent access to shared processor resources, variations of the traffic skew, and unbalanced packet-rate spikes. In this paper we present an adaptive monitoring framework, MONA, which is resilient to bottlenecks while maintaining the accuracy of monitoring reports above a user-specified threshold. MONA dynamically reduces the measurement task sets under adverse conditions, and reconfigures them to recover potential accuracy degradations. To quantify the monitoring accuracy at run time, MONA adopts a novel task-independent technique that generates accuracy estimates according to recently observed traffic characteristics. With a prototype implementation based on a generic packet-processing pipeline, and using well-known measurements tasks, we show that MONA achieves lossless traffic monitoring for a wide range of conditions, significantly enhances the level of monitoring accuracy, and performs adaptations at the time scale of milliseconds with limited overhead.

Traditional network functions (NFs) such as firewalls are implemented in costly dedicated hardware. By decoupling NFs from physical devices, network function virtualization enables virtual network functions (VNF) to run in virtual machines (VMs). However, VNFs are vulnerable to various faults such as software and hardware failures. To enhance VNF fault tolerance, the deployment of backup VNFs in stand-by VM instances is necessary. In case of stateful VNFs, stand-by instances require constant state updates from active instances during its operation. This will guarantee a correct and seamless handover from failed instances to stand-by instances after failures. Nevertheless, such state updates to stand-by instances could consume significant network bandwidth resources and lead to potential admission failures for VNF requests. In this paper, we study the fault-tolerant VNF placement problem with the optimization objective of admitting as many requests as possible. In particular, the VNF placement of active/stand-by instances, the request routing paths to active instances, and state transfer paths to stand-by instances are jointly considered. We devise an efficient heuristic algorithm to solve this problem, and propose a bicriteria approximation algorithm with performance guarantees for a special case of the problem. Simulations with realistic settings show that our algorithms can significantly improve the request admission rate compared to conventional approaches.

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.

The trend of softwarization within networks, such as SDN, NFV, and 5G, has increased the need for a network operating system and all its associated elements. In this paper we discuss how the layering and abstractions that commonly appear within computer operating systems are missing within the domain of networking. As such, the lack of abstractions means that interacting with and managing networks has already become a difficult and sometimes cumbersome task. The softwarization process is magnifying this effect. The abstractions that appear in operating systems hide the underlying features of the hardware, presenting elements to programmers and system managers that are easier to understand and to interact with. In this sense, the networking world is far behind. We present some of the lessons learned from these operating system abstractions and consider what could appear in the networking world.

The software-defined networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDN-based management architectures, it is of paramount importance to design a monitoring system that can provide timely and consistent updates to heterogeneous management applications. To support such applications operating with low latency requirements, the monitoring system should scale with increasing network size and provide precise network views with minimum overhead on the available resources. In this paper, we present a novel, self-adaptive, decentralized framework for resource monitoring in SDN. Our framework enables accurate statistics to be collected with limited burden on the network resources. This is realized through a self-tuning, adaptive monitoring mechanism that automatically adjusts its settings based on the traffic dynamics. We evaluate our proposal based on a realistic use case scenario, where a content distribution service and an on-demand gaming platform are deployed within an ISP network. The results show that reduced monitoring latencies are obtained with the proposed framework, thus enabling shorter reconfiguration control loops. In addition, the proposed adaptive monitoring method achieves significant gain in terms of monitoring overhead, while preserving the performance of the services considered.

Network operators have recently been developing multi-Gbps traffic monitoring tools that execute on commodity hardware and are part of the packet-processing pipelines realizing software dataplanes. These solutions allow sophisticated tasks to be performed on a per-packet basis, without relying on sampling or passive trace analysis, by leveraging the processing power available on servers. Although advances in packet capture have enabled intercepting packets from network cards at high rates, bottlenecks can still arise in the monitoring process as a result of concurrent access to shared processor resources, variations of the traffic skew, and unbalanced packet-rate spikes. In this paper we present an adaptive traffic monitoring approach that copes with emerging bottlenecks by timely detecting changes in the operational conditions and reconfiguring monitoring-related operations for subsets of traffic flows. Our solution performs responsive adaptations at the time scale of milliseconds and does not require a significant amount of resources. To demonstrate the capabilities of our approach we implemented it as part of a generic packet-processing pipeline and show that lossless traffic monitoring can be achieved for a wide range of conditions.

The Software-Defined Networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDN-based management architectures, it is of paramount importance to design a monitoring system that can provide frequent and consistent updates to heterogeneous management applications. For the monitoring functionality to scale according to the requirements of large-scale networks a distributed monitoring approach is required. In this paper we present a decentralized approach for resource monitoring in SDN, which is designed to support a wide range of measurement tasks and requirements in terms of monitoring rates and information granularity levels. Our solution leverages effective processing of the monitoring requests to reduce the consumption of limited resources, such as the control plane bandwidth of OpenFlow switches. To demonstrate the benefits of the proposed approach, our evaluation is based on a realistic and demanding use case, where a distributed management application coordinates a content distribution service in an ISP network.

Recent approaches have proposed to empower Internet Service Providers (ISPs) with caching capabilities that can allow them to implement their own cache management strategies and as such have better control over the utilization of their resources. In this demo paper, we present CacheMAsT (Cache Management Analysis and Visualization Tool), a decision support tool to visualize the configuration and performance of in-network cache management approaches. CacheMAsT is aimed at assisting researchers and engineers in analyzing and evaluating the different factors that can affect the performance of a cache management strategy and ultimately decide on the optimal approach to apply.

The management of network infrastructures has become increasingly complex over time, which is mainly attributed to the introduction of new functionality to support emerging services and applications. To address this important issue, research efforts in the last few years focused on developing software-defined networking solutions. While initial work proposed centralized architectures, their scalability limitations have led researchers to investigate a distributed control plane. Controller placement algorithms and mechanisms for building a logically centralized network view are some examples of challenges addressed in this context. A critical issue that requires specific attention concerns the communication between distributed entities involved in decision-making processes. To this end, we propose SigMA, a signaling framework that supports communication between the different entities of a decentralized management and control system. We also define the communication primitives and interfaces involved in such a decentralized environment. The benefits of SigMA are illustrated through three realistic network resource management use cases with different communication requirements. Based on simulation, we demonstrate the flexibility and extensibility of our solution in satisfying these requirements, thus effectively supporting advanced decentralized decision-making processes.

The expansion of Distributed Denial of Service (DDoS) for hire websites, known as Booters, has radically modified both the scope and stakes of DDoS attacks. Until recently, however, Booters have only received little attention from the research community. Given their impact, addressing the challenges associated with this phenomenon is crucial. In this paper, we present a rigorous methodology to identify a comprehensive set of existing Booters in the Internet. The methodology relies on well-defined mechanisms to generate a Booter blacklist, from crawling suspect URLs to characterizing and classifying the collected URLs. The list obtained using the methodology presented in this paper has a classification accuracy of 95.5%, which is 10.5% better compared to previous work. We also demonstrate the usage of our methodology applied by the Dutch NREN, SURFNet, which started using our blacklist to extend their Booters' activities monitoring.

The management of network infrastructures has become increasingly complex over time, which is mainly attributed to the introduction of new functionality to support emerging services and applications. To address this important issue, research efforts in the last few years focused on developing Software-Defined Networking solutions. While initial work proposed centralized architectures, their scalability limitations have led researchers to investigate a distributed control plane, with controller placement algorithms and mechanisms for building a logically centralized network view, being examples of challenges addressed. A critical issue that has not been adequately addressed concerns the communication between distributed decision-making entities to ensure configuration consistency. To this end, this paper proposes a signaling framework that can allow the exchange of information in distributed management and control scenarios. The benefits of the proposed framework are illustrated through a realistic network resource management use case. Based on simulation, we demonstrate the flexibility and extensibility of our solution in meeting the requirements of distributed decision-making processes.

Content delivery networks (CDNs) have been the prevalent method for the efficient delivery of content across the Internet. Management operations performed by CDNs are usually applied only based on limited information about Internet Service Provider (ISP) networks, which can have a negative impact on the utilization of ISP resources. To overcome these issues, previous research efforts have been investigating ISP-operated content delivery services, by which an ISP can deploy its own in-network caching infrastructure and implement its own cache management strategies. In this paper, we extend our previous work on ISP-operated content distribution and develop a novel scalable and efficient distributed approach to control the placement of content in the available caching points. The proposed approach relies on parallelizing the decision-making process and the use of network partitioning to cluster the distributed decision-making points, which enables fast reconfiguration and limits the volume of information required to take reconfiguration decisions. We evaluate the performance of our approach based on a wide range of parameters. The results demonstrate that the proposed solution can outperform previous approaches in terms of management overhead and complexity while offering similar network and caching performance.

Traffic engineering (TE) functionality aims to control and fine-tune the routing configuration and bandwidth allocation in order to optimize the use of network resources and avoid the build-up of congestion. The performance of a given TE scheme is, however, strongly influenced by the degree of flexibility offered in distributing the traffic load. Multipath routing coupled with arbitrary traffic splitting are two essential ingredients for achieving the desired flexibility. Current proposals for multipath routing in OpenFlow have mostly focused on equal splitting solutions, which impose limitations in terms of the level of control that can be achieved. In this paper, we investigate a new approach, which exploits the properties of bit-masking operations to enable flexible TE in OpenFlow networks. The proposed solution relies on the matching entry feature and the multiple table pipeline capability of OpenFlow, and as such, is in line with the current standard. Based on empirical evaluation, we illustrate the influence of the considered masking parameters and how these can be configured to achieve the desired traffic splitting ratios. The results demonstrate that our solution can achieve a similar level of splitting accuracy as the one obtained with a hash-based approach. However, in contrast to current proposals, it does not require complex extensions to the OpenFlow protocol and can be easily implemented in an OpenFlow environment.

In order to support reactive and adaptive operations, Software-Defined Networking (SDN)-based management and control frameworks call for decentralized solutions. A key challenge to consider when deploying such solutions is to decide on the degree of distribution of the management and control functionality. In this paper, we develop an approach to determine the allocation of management and control entities by designing two algorithms to compute their placement. The algorithms rely on a set of input parameters which can be tuned to take into account the requirements of both the network infrastructure and the management applications to execute in the network. We evaluate the influence of these parameters on the configuration of the resulting management and control planes based on real network topologies and provide guidelines regarding the settings of the proposed algorithms.

The heterogeneous nature of the applications, technologies and equipment that today's networks have to support has made the management of such infrastructures a complex task. The Software-Defined Networking (SDN) paradigm has emerged as a promising solution to reduce this complexity through the creation of a unified control plane independent of specific vendor equipment. However, designing a SDN-based solution for network resource management raises several challenges as it should exhibit flexibility, scalability and adaptability. In this paper, we present a new SDN-based management and control framework for fixed backbone networks, which provides support for both static and dynamic resource management applications. The framework consists of three layers which interact with each other through a set of interfaces. We develop a placement algorithm to determine the allocation of managers and controllers in the proposed distributed management and control layer. We then show how this layer can satisfy the requirements of two specific applications for adaptive load-balancing and energy management purposes.

Centralized and offline network management functionality, traditionally deployed by operators, cannot easily deal with the traffic patterns of emerging services, which are becoming more dynamic and unpredictable. As such, decentralized solutions that are flexible and adaptive to traffic and network dynamics are of paramount importance. To this end, we have been developing an in-network management approach in which an intelligent substrate allows the dynamic reconfiguration of resources according to network conditions. The set of nodes forming this logical structure are able to communicate with each other to coordinate their decisions. While in previous work we investigated the use of full-mesh and ring structures to connect the substrate nodes, we consider here a hybrid approach that combines the benefits of the other two. We describe algorithms that can be practically used to compute this hybrid structure and that take into account important criteria such as minimizing the latency and the communication overhead among the substrate nodes. We evaluate the impact of key parameters associated with the construction process.

Management operations performed by Content Delivery Network (CDN) providers consist mainly in controlling the placement of contents at different storage locations and deciding where to serve client requests from. Configuration decisions are usually taken by using only limited information about the carrier networks, and this can adversely affect network usage. In this work we propose an approach by which ISPs can have more control over their resources. This involves the deployment of caching points within their network, which can allow them to implement their own content placement strategies. The work presented in this paper investigates lightweight strategies that can be used by the ISPs to manage the placement of contents in the various network caching locations according to user demand characteristics. The proposed strategies differ in terms of the volume and nature of the information required to determine the new caching configurations. We evaluate the performance of the proposed strategies, in terms of network resource utilization, based on a wide range of user demand profiles and we compare the obtained performance according to metrics we define to characterize the demand. The results demonstrate that the proposed metrics can provide useful indications regarding the performance one strategy can achieve over another and, as such, can be used by the ISP to improve the utilization of network resources.

In order to meet the requirements of emerging demanding services, network resource management functionality that is decentralized, flexible and adaptive to traffic and network dynamics is of paramount importance. In this paper we describe the main mechanisms of DACoRM, a new intra-domain adaptive resource management approach for IP networks. Based on path diversity provided by multi-topology routing, our approach controls the distribution of traffic load in the network in an adaptive manner through periodical re-configurations that uses real-time monitoring information. The re-configuration actions performed are decided in a coordinated fashion between a set of source nodes that form an in-network overlay. We evaluate the overall performance of our approach using realistic network topologies. Results show that near-optimal network performance in terms of resource utilization can be achieved in scalable manner.