Lorenzo Maggi

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach.

Controlling flow reconfigurations in SDN

Software-Defined Network (SDN) controllers include mechanisms to globally reconfigure the network in order to respond to a changing environment. While iterative methods are employed to solve flow optimization problems, demands arrive or leave the system changing the optimization instance and requiring further iterations. In this paper, we focus on the general class of iterative solvers considering an exponential decrease over time in the optimality gap. Assuming dynamic arrivals and departures of demands, the computed optimality gap at each iteration Q(t) is described by an auto-regressive stochastic process. At each time slot the controller may choose to apply the current iteration to the network or not. Applying the current iteration improves the optimality gap but requires flow reconfiguration which hurts QoS and system stability. To limit the reconfigurations, we propose two control policies that minimize the flow allocation cost while respecting a network reconfiguration budget. We validate our model by experimenting with a realistic network setting and using standard Linear Programming tools used in the SDN industry. We show that our policies provide a practical means of keeping the optimally gap small within a given reconfiguration constraint.

Virtual function placement for service chaining with partial orders and anti-affinity rules

Software-Defined Networking and Network Function Virtualization are two paradigms that offer flexible software-based network management. Service providers are instantiating Virtualized Network Functions, for example, firewalls, DPIs, gateways—to highly facilitate the deployment and reconfiguration of network services with reduced time-to-value. They use Service Function Chaining technologies to dynamically reconfigure network paths traversing physical and virtual network functions. Providing a cost-efficient virtual function deployment over the network for a set of service chains is a key technical challenge for service providers, and this problem has recently caught much attention from both Industry and Academia. In this article, we propose a formulation of this problem as an Integer Linear Program that allows one to find the best feasible paths and virtual function placement for a set of services with respect to a total financial cost, while taking into account the (total or partial) order constraints for Service Function Chains of each service and other constraints such as end-to-end latency, anti-affinity rules between network functions on the same physical node and resource limitations in terms of network and processing capacities. Furthermore, we propose a heuristic algorithm based on a linear relaxation of the problem that performs close to optimum for large scale instances.

Overlay routing for fast video transfers in CDN

Content Delivery Networks (CDN) are witnessing the outburst of video streaming (e.g., personal live streaming or Video-on-Demand) where the video content, produced or accessed by mobile phones, must be quickly transferred from a point to another of the network. Whenever a user requests a video not directly available at the edge server, the CDN network must 1) identify the best location in the network where the content is stored, 2) set up a connection and 3) deliver the video as quickly as possible. For this reason, existing CDNs are adopting an overlay structure to reduce latency, leveraging the flexibility introduced by the Software Defined Networking (SDN) paradigm. In order to guarantee a satisfactory Quality of Experience (QoE) to users, the connection must respect several Quality of Service (QoS) constraints. In this paper, we focus on the sub-problem 2), by presenting an approach to efficiently compute and maintain paths in the overlay network. Our approach allows to speed up the transfer of video segments by finding minimum delay overlay paths under constraints on hop count, jitter, packet loss and relay node capacity. The proposed algorithm provides a near-optimal solution, while drastically reducing the execution time. We show on traces collected in a real CDN that our solution allows to maximize the number of fast video transfers.

Freeway Traffic Control Considering Capacity Drop Phenomena: Comparison of Different MPC Schemes

This paper proposes different MPC-based traffic controllers in order to reduce congestion in freeway systems via ramp metering. These controllers differ for the adopted prediction model and for the considered cost function to be minimized. In particular, both a standard CTM and a CTM modified version representing the capacity drop phenomenon are used, while the two different cost functions considered penalize congested states in different ways. These MPC controllers are compared via simulation, both evaluating the performances of the controlled freeway system in the different cases, and from a computational point of view.

Not always sparse: Flooding time in partially connected mobile ad hoc networks

In this paper we study mobile ad hoc wireless networks by using the notion of evolving connectivity graphs. In such systems, the connectivity changes over time due to the intermittent contacts of mobile terminals. In particular, we are interested in studying the expected flooding time when full connectivity cannot be ensured at each point in time. Even in this case, due to finite contact times durations, connected components may appear in the connectivity graph. Hence, this represents the intermediate case between extreme cases of fully mobile ad hoc networks and fully static ad hoc networks. By using a generalization of edge-Markovian graphs, we extend the existing models based on sparse scenarios to this intermediate case and calculate the expected flooding time. We also propose bounds that have reduced computational complexity.

Computational analysis of freeway traffic control based on a linearized prediction model

This paper studies a control strategy for reducing congestions in freeway systems by applying ramp metering as control measure. More specifically, the objective is to define a Model Predictive Control scheme to be applied on line, characterized by a finite-horizon optimal control problem with a mixed-integer linear form, in order to be efficiently solved with commercial solvers. In this optimal control problem the prediction model is obtained by linearizing the first-order macroscopic traffic model, hence binary variables must be introduced and the resulting model has a piecewise linear structure. In the paper, the adopted control scheme is first of all analysed in order to evaluate its effectiveness in improving the traffic conditions; secondly, the analysis has been devoted to evaluate the computational time necessary to solve the finitehorizon optimal control problem depending on the problem sizes and the traffic scenarios.

Real-Time Fair Resource Allocation in Distributed Software Defined Networks

The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM) that tackles the fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances in real-time.

Admission control with online algorithms in SDN

By offloading the control plane to powerful computing platforms running on commodity hardware, Software Defined Networking (SDN) unleashes the potential to operate computation intensive machine learning tools and solve complex optimization problems in a centralized fashion. This paper studies such an opportunity under the framework of the centralized SDN Admission Control (AC) problem. We first review and adapt some of the key AC algorithms from the literature, and evaluate their performance under realistic settings. We then propose to take a step further and build an AC meta-algorithm that is able to track the best AC algorithm under unknown traffic conditions. To this aim, we exploit a machine learning technique called Strategic Expert meta-Algorithm (SEA).

Cooperative online native advertisement: A game theoretical scheme leveraging on popularity dynamics

We propose a model for native online advertising, a recent technique able to safeguard users online experience and yet greatly accelerate the virality of a content. As of now, the cost of native advertisement represents a high entrance barrier for potential customers of advertisement hosts. In this paper, we argue that cooperative schemes can lower this entrance barrier. In fact, when different content providers (CPs) advertise their products on the same web-page, virality of the web-page itself can boost the number of potential customers, causing a non linear dynamics in the revenue of CPs. By utilizing a cooperative game theoretical framework, we derive a sufficient condition, depending on systems parameters, under which it is profitable for different CPs to advertise their products together. Under a mild assumption, the condition is also shown necessary.