Related papers: From Small to Large: Clos Network for Scaling All-…
To accommodate various use cases with differing characteristics, the Fifth Generation (5G) mobile communications system intends to utilize network slicing. Network slicing enables the creation of multiple logical networks over a shared…
Recent progress in speech separation has been largely driven by advances in deep neural networks, yet their high computational and memory requirements hinder deployment on resource-constrained devices. A significant inefficiency in…
Probabilistic message-passing algorithms are developed for routing transmissions in multi-wavelength optical communication networks, under node and edge-disjoint routing constraints and for various objective functions. Global routing…
In order to deal with the recent rapid increase in Internet traffic, a transmission technology is required to enable the efficient use of the optical fiber spectrum while offering flexibility in network bandwidth. To meet these challenges,…
In this paper the focus is on a family of Interconnection Networks (INs) known as Multistage Interconnection Networks (MINs). When it is exploited in Network-on-Chip (NoC) architecture designs, smaller circuit area, lower power consumption,…
One of the most demanding challenges for the designers of parallel computing architectures is to deliver an efficient network infrastructure providing low latency, high bandwidth communications while preserving scalability. Besides off-chip…
We describe two applications of machine learning in the context of IP/Optical networks. The first one allows agile management of resources at a core IP/Optical network by using machine learning for short-term and long-term prediction of…
This paper studies the design of self-adjusting networks whose topology dynamically adapts to the workload, in an online and demand-aware manner. This problem is motivated by emerging optical technologies which allow to reconfigure the…
The highly dynamic nature of the current network traffics, makes the network managers to exploit the flexibility of the state-of-the-art paradigm called SDN. In this way, there has been an increasing interest in hybrid networks of SDN-MPLS.…
SDN and NFV have recently changed the way we operate networks. By decoupling control and data plane operations and virtualising their components, they have opened up new frontiers towards reducing network ownership costs and improving…
Network slicing over space division multiplexed elastic optical networks (SDM EONs) enables efficient multiservice provisioning on a shared optical substrate. However, embedding such slices requires coordinated spectrum and compute resource…
The next generation wireless communication networks are required to support high-mobility scenarios, such as reliable data transmission for high-speed railways. Nevertheless, widely utilized multi-carrier modulation, the orthogonal…
Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing network embedding algorithms are mostly developed for a single…
In this study, we propose a novel data-driven reduced-order model for complex dynamics, including nonlinear, multi-attractor, multi-frequency, and multiscale behaviours. The starting point is a fully automatable cluster-based network model…
To ensure high availability, datacenter networks must rely on local fast rerouting mechanisms that allow routers to quickly react to link failures, in a fully decentralized manner. However, configuring these mechanisms to provide a high…
The operation of large-scale infrastructure networks requires scalable optimization schemes. To guarantee safe system operation, a high degree of feasibility in a small number of iterations is important. Decomposition schemes can help to…
Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. To overcome this limitation, numerous gradient compression techniques have been proposed and…
The exponential increase in Machine Learning (ML) model size and complexity has driven unprecedented demand for high-performance acceleration systems. As technology scaling enables the integration of thousands of computing elements onto a…
Rail-optimized network fabrics have become the de facto datacenter scale-out fabric for large-scale ML training. However, the use of high-radix electrical switches to provide all-to-all connectivity in rails imposes massive power, cost, and…
This paper considers the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. This…