Multistage Packet-Switching Fabrics for Data Center Networks

Recent applications have imposed stringent requirements within the Data Center Network (DCN) switches in terms of scalability, throughput and latency. In this thesis, the architectural design of the packet-switches is tackled in different ways to enable the expansion in both the number of connected endpoints and traffic volume. A cost-effective Clos-network switch with partially buffered units is proposed and two packet scheduling algorithms are described. The first algorithm adopts many simple and distributed arbiters, while the second approach relies on a central arbiter to guarantee an ordered packet delivery. For an improved scalability, the Clos switch is build using a Network-on-Chip (NoC) fabric instead of the common crossbar units. The Clos-UDN architecture made with Input-Queued (IQ) Uni-Directional NoC modules (UDNs) simplifies the input line cards and obviates the need for the costly Virtual Output Queues (VOQs). It also avoids the need for complex, and synchronized scheduling processes, and offers speedup, load balancing, and good path diversity. Under skewed traffic, a reliable micro load-balancing contributes to boosting the overall network performance. Taking advantage of the NoC paradigm, a wrapped-around multistage switch with fully interconnected Central Modules (CMs) is proposed. The architecture operates with a congestion-aware routing algorithm that proactively distributes the traffic load across the switching modules, and enhances the switch performance under critical packet arrivals. The implementation of small on-chip buffers has been made perfectly feasible using the current technology. This motivated the implementation of a large switching architecture with an Output-Queued (OQ) NoC fabric. The design merges assets of the output queuing, and NoCs to provide high throughput, and smooth latency variations. An approximate analytical model of the switch performance is also proposed. To further exploit the potential of the NoC fabrics and their modularity features, a high capacity Clos switch with Multi-Directional NoC (MDN) modules is presented. The Clos-MDN switching architecture exhibits a more compact layout than the Clos-UDN switch. It scales better and faster in port count and traffic load. Results achieved in this thesis demonstrate the high performance, expandability and programmability features of the proposed packet-switches which makes them promising candidates for the next-generation data center networking infrastructure

Multistage Packet-Switching Fabrics for Data Center Networks

Recent applications have imposed stringent requirements within the Data Center Network (DCN) switches in terms of scalability, throughput and latency. In this thesis, the architectural design of the packet-switches is tackled in different ways to enable the expansion in both the number of connected endpoints and traffic volume. A cost-effective Clos-network switch with partially buffered units is proposed and two packet scheduling algorithms are described. The first algorithm adopts many simple and distributed arbiters, while the second approach relies on a central arbiter to guarantee an ordered packet delivery. For an improved scalability, the Clos switch is build using a Network-on-Chip (NoC) fabric instead of the common crossbar units. The Clos-UDN architecture made with Input-Queued (IQ) Uni-Directional NoC modules (UDNs) simplifies the input line cards and obviates the need for the costly Virtual Output Queues (VOQs). It also avoids the need for complex, and synchronized scheduling processes, and offers speedup, load balancing, and good path diversity. Under skewed traffic, a reliable micro load-balancing contributes to boosting the overall network performance. Taking advantage of the NoC paradigm, a wrapped-around multistage switch with fully interconnected Central Modules (CMs) is proposed. The architecture operates with a congestion-aware routing algorithm that proactively distributes the traffic load across the switching modules, and enhances the switch performance under critical packet arrivals. The implementation of small on-chip buffers has been made perfectly feasible using the current technology. This motivated the implementation of a large switching architecture with an Output-Queued (OQ) NoC fabric. The design merges assets of the output queuing, and NoCs to provide high throughput, and smooth latency variations. An approximate analytical model of the switch performance is also proposed. To further exploit the potential of the NoC fabrics and their modularity features, a high capacity Clos switch with Multi-Directional NoC (MDN) modules is presented. The Clos-MDN switching architecture exhibits a more compact layout than the Clos-UDN switch. It scales better and faster in port count and traffic load. Results achieved in this thesis demonstrate the high performance, expandability and programmability features of the proposed packet-switches which makes them promising candidates for the next-generation data center networking infrastructure

Kick-scooters detection in sensor-based transportation mode classification methods

International audienceIn this work we present a novel classification model that can detect kick-scooters from inertial and pressure sensors. The detection is performed with kick-scooters being trained with other activities and transportation modes including still, walking, biking, taking bus and tramway. Results show that kick-scooters can be precisely detected up to 99% for three different sensor placements: on-foot, waist-attached and in the trouser's pocket. Thus, this paper provides a first contribution where kick-scooters can be classified and studied for further applications such as mobility behavior analysis and navigation

Kick-scooters identification in the context of transportation mode detection using inertial sensors: Methods and accuracy

International audienceThis work presents a novel transportation mode detection algorithm that handles the recognition of kick-scooters. In 2015, 10 minutes of data from a kick-scooter were considered in a transportation mode detection study, yielding a 56% F1-score. Since then, kick-scooters were not given much attention. Yet, kick-scooters are now very present in the urban transportation ecosystem, and their consideration in transportation studies has become a must. To fill this gap, 4 hours of kick-scooter signals were collected by 18 participants, with a set of 6 different kick-scooters, using 3 body-worn inertial measurement units. Obviously, kick-scooter patterns are classified in contrast with other modes of transportation. Two classification scenarios are considered in order to gradually increase the classification model complexity. The first scenario includes walking, biking, and kick-scooter, while the second considers public transport (tramway and bus) in addition to the former transportation modes. Results show that kick-scooters can be detected with an F1-score of 80% in the first scenario. Walking and public transport samples were still accurately classified in the second scenario, with an F1-score above 80% for both classes. However, bike and kick-scooter samples were both classified with lower F1-scores, equal to 59% and 64% respectively. Therefore, the main focus of future works should be directed toward the separability of kick-scooters and bikes when public transport is considered. The findings also suggest to place preferably the sensors in the trouser’s pocket, allowing for leg motion to be finely captured

Kick-scooters detection in sensor-based transportation mode classification methods

International audienceIn this work we present a novel classification model that can detect kick-scooters from inertial and pressure sensors. The detection is performed with kick-scooters being trained with other activities and transportation modes including still, walking, biking, taking bus and tramway. Results show that kick-scooters can be precisely detected up to 99% for three different sensor placements: on-foot, waist-attached and in the trouser's pocket. Thus, this paper provides a first contribution where kick-scooters can be classified and studied for further applications such as mobility behavior analysis and navigation