Data plane assisted state replication with Network Function Virtualization

Modern 5G networks are capable of providing ultra-low latency and highly scalable network services by employing modern networking paradigms such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). The latter enables performance-critical network applications to be run in a distributed fashion directly inside the infrastructure. Being distributed, those applications rely on sophisticated state replication algorithms to synchronize states among each other. Nevertheless, current implementations of such algorithms do not fully exploit the potential of the modern infrastructures, thus leading to sub-optimal performance. In this paper, we propose STARE, a novel state replication system tailored for 5G networks. At its core, STARE exploits stateful SDN to offload replication-related processes to the data plane, ultimately leading to reduced communication delays and processing overhead for VNFs. We provide a detailed description of the STARE architecture alongside a publicly-available P4- based implementation. Furthermore, our evaluation shows that STARE is capable of scaling to big networks while introducing low overhead in the network

Traffic Optimization in Data Center and Software-Defined Programmable Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

"Mothers as Candy Wrappers": Critical Infrastructure Supporting the Transition into Motherhood

Copyright © ACM. The transition into motherhood is a complicated and often unsupported major life disruption. To alleviate mental health issues and to support identity re-negotiation, mothers are increasingly turning to online mothers\u27 groups, particularly private and secret Facebook groups; these can provide a complex system of social, emotional, and practical support for new mothers. In this paper we present findings from an exploratory interview study of how new mothers create, find, use, and participate in ICTs, specifically online mothers\u27 groups, to combat the lack of formal support systems by developing substitute networks. Utilizing a framework of critical infrastructures, we found that these online substitute networks were created by women, for women, in an effort to fill much needed social, political, and medical gaps that fail to see \u27woman and mother\u27 as a whole being, rather than simply as a \u27discarded candy wrapper\u27. Our study contributes to the growing literature on ICT use by mothers for supporting and negotiating new identities, by illustrating how these infrastructures can be re-designed and appropriated in use, for critical utilization

Understanding IOS Implementation Process in an Automotive Manufacturing Company: An Organisational Motivation Perspective

A leading Australian automotive manufacturing company has introduced an internet-enabled electronic data interchange (EDI) system recently that links the company with its small suppliers. In this paper, we use a scientific case study approach to examine the internet-enabled EDI implementation experience of the automotive company, and explain its implementation process by referring to a theoretical model known as the IOS Motivation Model (IMM) which we have developed based on the notion of ‗organizational motivation‘ for IOS adoption [16]. The case study findings highlight the key role of organisational motivation as a determinant of IOS implementation process undertaken by the company. This finding is useful to e-business practitioners because it provides them with a means of assessing IOS implementation related activities, and for researchers, because it provides a theoretical framework for understanding the role of motivation in the activities conducted when implementing a system

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode

Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12,000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large dataset for screening, we use machine learning models to predict the mechanical properties of several new solid electrolytes. We train a convolutional neural network on the shear and bulk moduli purely on structural features of the material. We use AdaBoost, Lasso and Bayesian ridge regression to train the elastic constants, where the choice of the model depended on the size of the training data and the noise that it can handle. Our models give us direct interpretability by revealing the dominant structural features affecting the elastic constants. The stiffness is found to increase with a decrease in volume per atom, increase in minimum anion-anion separation, and increase in sublattice (all but Li) packing fraction. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and 6 solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.Comment: 34 pages, 4 Figures, 3 Table, 7 pages of Supporting Informatio

Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments

The industrial Internet of things (IIoT) has radically modified industrial environments, not only enabling novel industrial applications but also significantly increasing the amount of generated network traffic. Nowadays, a major concern is to support network-intensive industrial applications while ensuring the prompt and reliable delivery of mission-critical traffic flows concurrently traversing the industrial network. To this end, we propose application-aware network traffic management. The goal is to satisfy the requirements of industrial applications through a form of traffic management, the decision making of which is also based on what is carried within packet payloads (application data) in an efficient and flexible way. Our proposed solution targets multi-access edge computing (MEC)-integrated industrial environments, where on-premises and off-premises edge computing resources are used in a coordinated way, as it is expected to be in future Internet scenarios. The technical pillars of our solution are edge-powered in-network processing (eINP) and software-defined networking (SDN). The concept of eINP differs from INP because the latter is directly performed on network devices (NDs), whereas the former is performed on edge nodes connected via high-speed links to NDs. The rationale of eINP is to provide the network with additional capabilities for packet payload inspection and processing through edge computing, either on-premises or in the MEC-enabled cellular network. The reported in-the-field experimental results show the proposal feasibility and its primary tradeoffs in terms of performance and confidentiality

Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs

Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models. However, existing approaches often rely on abstract, complex visualizations that poorly map to the task at hand or require non-trivial ML expertise to interpret. Here, we present two visual analytics modules that facilitate an intuitive assessment of model reliability. To help users better characterize and reason about a model's uncertainty, we visualize raw and aggregate information about a given input's nearest neighbors. Using an interactive editor, users can manipulate this input in semantically-meaningful ways, determine the effect on the output, and compare against their prior expectations. We evaluate our interface using an electrocardiogram beat classification case study. Compared to a baseline feature importance interface, we find that 14 physicians are better able to align the model's uncertainty with domain-relevant factors and build intuition about its capabilities and limitations

Mecanismos dinâmicos de segurança para redes softwarizadas e virtualizadas

The relationship between attackers and defenders has traditionally been asymmetric, with attackers having time as an upper hand to devise an exploit that compromises the defender. The push towards the Cloudification of the world makes matters more challenging, as it lowers the cost of an attack, with a de facto standardization on a set of protocols. The discovery of a vulnerability now has a broader impact on various verticals (business use cases), while previously, some were in a segregated protocol stack requiring independent vulnerability research. Furthermore, defining a perimeter within a cloudified system is non-trivial, whereas before, the dedicated equipment already created a perimeter. This proposal takes the newer technologies of network softwarization and virtualization, both Cloud-enablers, to create new dynamic security mechanisms that address this asymmetric relationship using novel Moving Target Defense (MTD) approaches. The effective use of the exploration space, combined with the reconfiguration capabilities of frameworks like Network Function Virtualization (NFV) and Management and Orchestration (MANO), should allow for adjusting defense levels dynamically to achieve the required security as defined by the currently acceptable risk. The optimization tasks and integration tasks of this thesis explore these concepts. Furthermore, the proposed novel mechanisms were evaluated in real-world use cases, such as 5G networks or other Network Slicing enabled infrastructures.A relação entre atacantes e defensores tem sido tradicionalmente assimétrica, com os atacantes a terem o tempo como vantagem para conceberem uma exploração que comprometa o defensor. O impulso para a Cloudificação do mundo torna a situação mais desafiante, pois reduz o custo de um ataque, com uma padronização de facto sobre um conjunto de protocolos. A descoberta de uma vulnerabilidade tem agora um impacto mais amplo em várias verticais (casos de uso empresarial), enquanto anteriormente, alguns estavam numa pilha de protocolos segregados que exigiam uma investigação independente das suas vulnerabilidades. Além disso, a definição de um perímetro dentro de um sistema Cloud não é trivial, enquanto antes, o equipamento dedicado já criava um perímetro. Esta proposta toma as mais recentes tecnologias de softwarização e virtualização da rede, ambas facilitadoras da Cloud, para criar novos mecanismos dinâmicos de segurança que incidem sobre esta relação assimétrica utilizando novas abordagens de Moving Target Defense (MTD). A utilização eficaz do espaço de exploração, combinada com as capacidades de reconfiguração de frameworks como Network Function Virtualization (NFV) e Management and Orchestration (MANO), deverá permitir ajustar dinamicamente os níveis de defesa para alcançar a segurança necessária, tal como definida pelo risco actualmente aceitável. As tarefas de optimização e de integração desta tese exploram estes conceitos. Além disso, os novos mecanismos propostos foram avaliados em casos de utilização no mundo real, tais como redes 5G ou outras infraestruturas de Network Slicing.Programa Doutoral em Engenharia Informátic