Davide Cherubini

Inversion of MLP neural networks for direct solution of inverse problems

In this work, a neural-based approach for inverse problems in the field of electromagnetic devices design is presented. A multilayer perceptron neural network is first trained to solve the analysis problem of the studied system. As a design problem can be formulated as an inverse problem, i.e., starting from the design requirements the optimal values of the design parameters have to be obtained, the input of the neural network will correspond to the design parameters while the output is the objective function of the optimization problem. In this work, a procedure is presented which performs the inversion of the trained neural network when the design requirements are assigned to the output.

Primary and backup paths optimal design for traffic engineering in hybrid IGP/MPLS networks

The paper describes an optimization model which aims at minimizing the maximum link utilization of IP telecommunication networks under the joint use of the traditional IGP protocols and the more sophisticated MPLS-TE technology. The survivability of the network is taken into account in the optimization process implementing the path restoration scheme. This scheme benefits of the Fast Re-Route (FRR) capability allowing service providers to offer high availability and high revenue SLAs (Service Level Agreements). The hybrid IGP/MPLS approach relies on the formulation of an innovative Linear Programming mathematical model that, while optimizing the network utilization, provides optimal user performance, efficient use of network resources, and 100% survivability in case of single link failure. The possibility of performing an optimal exploitation of the network resources throughout the joint use of the IGP and MPLS protocols provides a flexible tool for the ISP (Internet Service Provider) networks traffic engineers. The efficiency of the proposed approach is validated by a wide experimentation performed on synthetic and real networks. The obtained results show that a limited number of LSP tunnels have to be set up in order to significantly reduce the congestion level of the network while at the same time guaranteeing the survivability of the network.

End-to-End Service Quality for Cloud Applications

This paper aims to highlight the importance of End-to-End (E2E) service quality for cloud scenarios, with focus on telecom carrier-grade services. In multi-tenant distributed and virtualized cloud infrastructures, enhanced resource sharing raises issues in terms of performance stability and reliability. Moreover, the heterogeneity of business entities responsible for the cloud service delivery, threatens the possibility of offering precise E2E service levels. Setting up proper Service-Level Agreements (SLAs) among the involved players, may become overly challenging. However, problems may be mitigated by a thoughtful intervention of standardization. The paper reviews some of the most important efforts in research and industry to tackle E2E service quality and concludes with some recommendations for additional research and/or standardization effort required to be able to deploy mission critical or interactive real-time services with high demands on service quality, reliability and predictability on cloud platforms.

Linear programming models for traffic engineering in 100% survivable networks under combined IS-IS/OSPF and MPLS-TE

This paper concerns the problem of minimizing the maximum link utilization of IP telecommunication networks under the joint use of traditional IGP routing protocols, such as IS-IS and OSPF, and the more sophisticated MPLS-TE technology. It is shown that the problem of choosing the optimal routing, both under working conditions and under single link failure scenarios, can be cast as a linear program of reasonable size. The proposed model is validated by a computational experimentation performed on synthetic and real networks: the obtained results show that the new approach considerably reduces the maximum link utilization of the network with respect to simply optimizing the IGP weights, at the cost of adding a limited number of label switched paths (LSPs). Optimizing the set of IGP weights within the overall approach further improves performances. The computational time needed to solve the models matches well with real-time requirements, and makes it possible to consider network design problems.

Brokering SLAs for End-to-End QoS in Cloud Computing

In this paper, we present a brokering logic for providing precise end-to-end QoS levels to cloud applications distributed across a number of different business actors, such as network service providers (NSP) and cloud providers (CSP). The broker composes a number of available offerings from each provider, in a way that respects the QoS application constraints while minimizing costs incurred by cloud consumers.

Data Centre Optimisation Enhanced by Software Defined Networking

Contemporary Cloud Computing infrastructures are being challenged by an increasing demand for evolved cloud services characterised by heterogeneous performance requirements including real-time, data-intensive and highly dynamic workloads. The classical way to deal with dynamicity is to scale computing and network resources horizontally. However, these techniques must be coupled effectively with advanced routing and switching in a multi-path environment, mixed with a high degree of flexibility to support dynamic adaptation and live-migration of virtual machines (VMs). We propose a management strategy to jointly optimise computing and networking resources in cloud infrastructures, where Software Defined Networking (SDN) plays a key enabling role.

Online evolution of femtocell coverage algorithms using genetic programming

The wide adoption of smartphones has resulted in an exponential increase in the demand for wireless data. To address this problem, operators have started deploying large numbers of small cells. In order to operate such small cell network cost-effectively they need to be able to intelligently optimize their configuration, which can be achieved by applying machine learning techniques such as genetic programming. The use of genetic programming has previously been used to derive joint coverage algorithms for a group of enterprise femtocells. However, the evolution of the algorithms was performed in an offline manner, on a pre-defined simulation model of the deployment scenario. In this paper, an approach to perform the evolution in an online manner using an automated model building process is presented. The model building process uses network traces as inputs to create a hierarchical Markov model that is shown to be able to capture the behavior of the femtocell network well. It is shown that the resulting environment model can effectively drive the on-line evolution of coverage optimization algorithms.

Confidential Execution of Cloud Services

In this paper, we present Confidential Domain of Execution (CDE), a mechanism for achieving confidential execution of software in an otherwise untrusted environment, e.g., at a Cloud Service Provider. This is achieved by using an isolated execution environment in which any communication with the outside untrusted world is forcibly encrypted by trusted hardware. The mechanism can be useful to overcome the challenging issues in guaranteeing confidential execution in virtualized infrastructures, including cloud computing and virtualized network functions, among other scenarios. Moreover, the proposed mechanism does not suffer from the performance drawbacks typical of other solutions proposed for secure computing, as highlighted by the presented novel validation results.