Recent advances in cloud computing and data centers have increased the demands for monitoring the network infrastructure and the applications that it hosts. The monitoring processes let network administrators to be aware of the status of the physical and logical units that compose their system. Since the goal of next generation networks is to minimise the administrators’ intervention, the alerting systems should minimize the frequency of notifications, emphasizing on critical scenarios such as when a monitoring metric surpasses a threshold or an anomalous behaviour is detected. However, current monitoring tools flood network administrators with hundreds of notifications every day. In this paper, we propose a binary classification approach, in order to decide if the administrators should be notified through monitoring alerts or not. To do so, our framework is build upon real monitoring logs and alerts, that show how the administrators reacted when receiving an alert. Extensive simulation results assess the performance of various classification approaches and reveal that random forests are great candidates for the binary classification alerting system that we propose, in terms of classification efficiency and computational overhead.
Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy. Mainstream FL approaches require each participant to share a common network architecture and further assume that data are sampled IID across participants. However, in real-world deployments, participants may require heterogeneous network architectures; and the data distribution is almost non-uniform. To address these issues we introduce FedH2L, which is agnostic to the model architecture and robust to different data distributions across participants. In contrast to approaches sharing parameters or gradients, FedH2L relies on mutual distillation, exchanging only posteriors on a shared seed set between participants in a decentralized manner. This makes it extremely bandwidth efficient, model agnostic, and crucially produces models capable of performing well on the whole data distribution when learning from heterogeneous silos.
Federated Learning is a novel distributed machine learning paradigm that leverages the computing power of numerous decentralized data sources for jointly training machine learning models while ensuring user privacy. In the most commonly used cross-device scenarios, the client cluster typically cover a vast number of heterogeneous end devices. Due to physical limitations such as bandwidth, only a few clients can participate in each round of training. The core issue of the client selection is to determine an appropriate client set for each training round. However, existing selection algorithms, especially the widely adopted random selection, suffer from a number of issues that prevent them from achieving a good balance between training efficiency and speed. Therefore, we propose Scout, which utilizes the heterogeneity features of clients' data and resources to jointly model the utility function, and enhances the utilization of correlation among clients and the diversity among selected clients to achieve better training efficiency and speed. Furthermore, Scout maintains the scalability and fairness. Our experiments demonstrate that in large-scale heterogeneous clients scenarios, Scout outperforms three baseline algorithms and the state-of-the-art dual-feature dimension algorithm Oort in evaluation metrics.
Software Defined Network (SDN) enables network operators to achieve the customization of network services, which tends to be more dynamic and fine-grained. However, the distributed nature of rule updating in SDN brings consistency problems, i.e., packets travel according to different versions of rules. It leads to the issues of blackholes, loops, congestion, and deadlock in the data plane, which may further affect the service quality of the application plane. With the emergence of new computing paradigms such as edge computing and fog computing, the heterogeneity of network devices and links, as well as the diversity of network application requirements have become increasingly prominent. Traditional update methods ignore these key factors when modeling, so they cannot cope with the increasingly complex network environment, resulting in delays or packet loss rates that do not meet service requirements. This paper proposes HyCU, which takes device performance as a constraint and optimizes updates based on flow service requirements. We conduct experiments under different scenarios and constraints over two real-world topologies with real-time running flows, demonstrating the effectiveness of HyCU.
With the continuous improvement on model performance, deep learning models have been widely deployed and achieved promising outcomes in various fields in recent years. However, due to the escalating volumes of training data and the complexity of application problems, it becomes more and more challenging to design a neural network with better performance by hand. Analysing the evolution of typical neural network structures has important reference significance for designing a network structure. In this paper, we select the open source models in SAR image processing for an empirical analysis on the evolution of neural network structures. We analyse the evolution of 239 open source deep learning models from the aspects of framework, computing unit, model computation amount and the combined use of various computing units. Results reveal that preference and co-occurrence exist in computing units, while the average number of convolution, activation and normalization layer increases significantly over time. Model complexity shows an overall upward trend, and the characteristics of SAR image are more and more taken into consideration during the model structure design.
