The Experts below are selected from a list of 675 Experts worldwide ranked by ideXlab platform
Christophe Cerin - One of the best experts on this subject based on the ideXlab platform.
-
IPDPS Workshops - Distributed and in-Situ Machine Learning for Smart-Homes and Buildings: Application to Alarm Sounds Detection
2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017Co-Authors: Amaury Durand, Yanik Ngoko, Christophe CerinAbstract:We consider the implementation of an in-situ machine learning System with the computing model promoted by Qarnot computing. Qarnot introduced an utility computing model in which servers are distributed in homes and offices where they serve as heaters. The Qarnot servers also embed several sensors for temperature, humidity, CO 2 etc. Qarnot offers an adequate platform to develop in-situ workflows for smart-homes problems. To demonstrate this point, we consider a typical problem: the detection of alarm sounds. Our paper introduces a new Orchestration System for in-situ workflows, in the Qarnot platform. We also consider a general parallel framework for training alarm sound classifiers and decline an implementation that makes use of our orchestrator. Finally, we evaluate the implemented framework on different aspects including: the accuracy (of the resulting classifiers) and the runtime gain of the parallelization.
-
Distributed and in-Situ Machine Learning for Smart-Homes and Buildings: Application to Alarm Sounds Detection
2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017Co-Authors: Amaury Durand, Yanik Ngoko, Christophe CerinAbstract:We consider the implementation of an in-situ machine learning System with the computing model promoted by Qarnot computing. Qarnot introduced an utility computing model in which servers are distributed in homes and offices where they serve as heaters. The Qarnot servers also embed several sensors for temperature, humidity, CO 2 etc. Qarnot offers an adequate platform to develop in-situ workflows for smart-homes problems. To demonstrate this point, we consider a typical problem: the detection of alarm sounds. Our paper introduces a new Orchestration System for in-situ workflows, in the Qarnot platform. We also consider a general parallel framework for training alarm sound classifiers and decline an implementation that makes use of our orchestrator. Finally, we evaluate the implemented framework on different aspects including: the accuracy (of the resulting classifiers) and the runtime gain of the parallelization.
Piero Castoldi - One of the best experts on this subject based on the ideXlab platform.
-
Latency-aware resource Orchestration in SDN-based packet over optical flexi-grid transport networks
IEEE OSA Journal of Optical Communications and Networking, 2019Co-Authors: S. Fichera, Molka Gharbaoui, B. Martini, R. Martínez, R. Casellas, D R. Vilalta, R. Muñoz, Piero CastoldiAbstract:In the upcoming 5G networks and following the emerging software-defined networking/network function virtualization (SDN/NFV) paradigm, demanded services will be composed of a number of virtual network functions that may be spread across the whole transport infrastructure and allocated in distributed data centers (DCs). These services will impose stringent requirements such as bandwidth and end-to-end latency that the transport network will need to fulfill. In this paper, we present an Orchestration System devised to select and allocate virtual resources in distributed DCs connected through a multi-layer (packet over flexi-grid optical) network. Three different on-line Orchestration algorithms are conceived to accommodate the incoming requests by satisfying computing, bandwidth, and end-to-end latency constraints, setting up multi-layer connections. We addressed end-to-end latency requirements by considering both network (due to propagation delay) and processing delay components. The proposed algorithms have been extensively evaluated and assessed (via a number of figures of merit) through experimental tests carried out in a packet over optical flexi-grid network available in the ADRENALINE testbed with emulated DCs connected to it.
-
NFV-SDN - Demonstration of Latency-Aware and Self-Adaptive Service Chaining in 5G/SDN/NFV infrastructures
2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2018Co-Authors: Molka Gharbaoui, Barbara Martini, C. Contoli, G. Davoli, G. Cuffaro, F. Paganelli, W. Cerroni, P. Cappanera, Piero CastoldiAbstract:This paper will demonstrate an Orchestration System for 5G infrastructure supporting latency-minimized and self-adaptive service chaining over geographically distributed edge clouds interconnected through SDN. The demo will show an Orchestration System that comprises dynamic virtual function selection and intent-based traffic steering control functionalities to provide optimized service chains in terms of end-to-end latency and adjustable with respect to the context (e.g., service dynamics, network status).
-
NFV-SDN - Experimenting latency-aware and reliable service chaining in Next Generation Internet testbed facility
2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2018Co-Authors: Molka Gharbaoui, Barbara Martini, C. Contoli, G. Davoli, G. Cuffaro, F. Paganelli, W. Cerroni, P. Cappanera, Piero CastoldiAbstract:In this paper we report experimental validation of an Orchestration System for geographically distributed Edge/NFV clouds, supporting end-to-end latency-aware and reliable network service chaining. The Orchestration System includes advanced features such as dynamic virtual function selection and intent-based traffic steering control across heterogeneous SDN infrastructures. The experiment run under the 1st Open Call of the Fed4FIRE+ EU H2020 Project and took advantage of bare metal servers provided by the Fed4FIRE platform to set up a distributed SDN/NFV deployment. We provide details on how this Orchestration System has been deployed on top of Fed4FIRE facility and present experimental results assessing the effectiveness of the proposed Orchestration approach.
-
Experimenting latency-aware and reliable service chaining in Next Generation Internet testbed facility
2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2018Co-Authors: Molka Gharbaoui, C. Contoli, G. Davoli, G. Cuffaro, B. Martini, F. Paganelli, W. Cerroni, P. Cappanera, Piero CastoldiAbstract:In this paper we report experimental validation of an Orchestration System for geographically distributed Edge/NFV clouds, supporting end-to-end latency-aware and reliable network service chaining. The Orchestration System includes advanced features such as dynamic virtual function selection and intent-based traffic steering control across heterogeneous SDN infrastructures. The experiment run under the 1st Open Call of the Fed4FIRE+ EU H2020 Project and took advantage of bare metal servers provided by the Fed4FIRE platform to set up a distributed SDN/NFV deployment. We provide details on how this Orchestration System has been deployed on top of Fed4FIRE facility and present experimental results assessing the effectiveness of the proposed Orchestration approach.
-
Demonstration of Latency-Aware and Self-Adaptive Service Chaining in 5G/SDN/NFV infrastructures
2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2018Co-Authors: Molka Gharbaoui, B. Martini, C. Contoli, G. Davoli, G. Cuffaro, F. Paganelli, W. Cerroni, P. Cappanera, Piero CastoldiAbstract:This paper will demonstrate an Orchestration System for 5G infrastructure supporting latency-minimized and self-adaptive service chaining over geographically distributed edge clouds interconnected through SDN. The demo will show an Orchestration System that comprises dynamic virtual function selection and intent-based traffic steering control functionalities to provide optimized service chains in terms of end-to-end latency and adjustable with respect to the context (e.g., service dynamics, network status).
Amaury Durand - One of the best experts on this subject based on the ideXlab platform.
-
IPDPS Workshops - Distributed and in-Situ Machine Learning for Smart-Homes and Buildings: Application to Alarm Sounds Detection
2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017Co-Authors: Amaury Durand, Yanik Ngoko, Christophe CerinAbstract:We consider the implementation of an in-situ machine learning System with the computing model promoted by Qarnot computing. Qarnot introduced an utility computing model in which servers are distributed in homes and offices where they serve as heaters. The Qarnot servers also embed several sensors for temperature, humidity, CO 2 etc. Qarnot offers an adequate platform to develop in-situ workflows for smart-homes problems. To demonstrate this point, we consider a typical problem: the detection of alarm sounds. Our paper introduces a new Orchestration System for in-situ workflows, in the Qarnot platform. We also consider a general parallel framework for training alarm sound classifiers and decline an implementation that makes use of our orchestrator. Finally, we evaluate the implemented framework on different aspects including: the accuracy (of the resulting classifiers) and the runtime gain of the parallelization.
-
Distributed and in-Situ Machine Learning for Smart-Homes and Buildings: Application to Alarm Sounds Detection
2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017Co-Authors: Amaury Durand, Yanik Ngoko, Christophe CerinAbstract:We consider the implementation of an in-situ machine learning System with the computing model promoted by Qarnot computing. Qarnot introduced an utility computing model in which servers are distributed in homes and offices where they serve as heaters. The Qarnot servers also embed several sensors for temperature, humidity, CO 2 etc. Qarnot offers an adequate platform to develop in-situ workflows for smart-homes problems. To demonstrate this point, we consider a typical problem: the detection of alarm sounds. Our paper introduces a new Orchestration System for in-situ workflows, in the Qarnot platform. We also consider a general parallel framework for training alarm sound classifiers and decline an implementation that makes use of our orchestrator. Finally, we evaluate the implemented framework on different aspects including: the accuracy (of the resulting classifiers) and the runtime gain of the parallelization.
Molka Gharbaoui - One of the best experts on this subject based on the ideXlab platform.
-
Latency-aware resource Orchestration in SDN-based packet over optical flexi-grid transport networks
IEEE OSA Journal of Optical Communications and Networking, 2019Co-Authors: S. Fichera, Molka Gharbaoui, B. Martini, R. Martínez, R. Casellas, D R. Vilalta, R. Muñoz, Piero CastoldiAbstract:In the upcoming 5G networks and following the emerging software-defined networking/network function virtualization (SDN/NFV) paradigm, demanded services will be composed of a number of virtual network functions that may be spread across the whole transport infrastructure and allocated in distributed data centers (DCs). These services will impose stringent requirements such as bandwidth and end-to-end latency that the transport network will need to fulfill. In this paper, we present an Orchestration System devised to select and allocate virtual resources in distributed DCs connected through a multi-layer (packet over flexi-grid optical) network. Three different on-line Orchestration algorithms are conceived to accommodate the incoming requests by satisfying computing, bandwidth, and end-to-end latency constraints, setting up multi-layer connections. We addressed end-to-end latency requirements by considering both network (due to propagation delay) and processing delay components. The proposed algorithms have been extensively evaluated and assessed (via a number of figures of merit) through experimental tests carried out in a packet over optical flexi-grid network available in the ADRENALINE testbed with emulated DCs connected to it.
-
NFV-SDN - Demonstration of Latency-Aware and Self-Adaptive Service Chaining in 5G/SDN/NFV infrastructures
2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2018Co-Authors: Molka Gharbaoui, Barbara Martini, C. Contoli, G. Davoli, G. Cuffaro, F. Paganelli, W. Cerroni, P. Cappanera, Piero CastoldiAbstract:This paper will demonstrate an Orchestration System for 5G infrastructure supporting latency-minimized and self-adaptive service chaining over geographically distributed edge clouds interconnected through SDN. The demo will show an Orchestration System that comprises dynamic virtual function selection and intent-based traffic steering control functionalities to provide optimized service chains in terms of end-to-end latency and adjustable with respect to the context (e.g., service dynamics, network status).
-
NFV-SDN - Experimenting latency-aware and reliable service chaining in Next Generation Internet testbed facility
2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2018Co-Authors: Molka Gharbaoui, Barbara Martini, C. Contoli, G. Davoli, G. Cuffaro, F. Paganelli, W. Cerroni, P. Cappanera, Piero CastoldiAbstract:In this paper we report experimental validation of an Orchestration System for geographically distributed Edge/NFV clouds, supporting end-to-end latency-aware and reliable network service chaining. The Orchestration System includes advanced features such as dynamic virtual function selection and intent-based traffic steering control across heterogeneous SDN infrastructures. The experiment run under the 1st Open Call of the Fed4FIRE+ EU H2020 Project and took advantage of bare metal servers provided by the Fed4FIRE platform to set up a distributed SDN/NFV deployment. We provide details on how this Orchestration System has been deployed on top of Fed4FIRE facility and present experimental results assessing the effectiveness of the proposed Orchestration approach.
-
Experimenting latency-aware and reliable service chaining in Next Generation Internet testbed facility
2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2018Co-Authors: Molka Gharbaoui, C. Contoli, G. Davoli, G. Cuffaro, B. Martini, F. Paganelli, W. Cerroni, P. Cappanera, Piero CastoldiAbstract:In this paper we report experimental validation of an Orchestration System for geographically distributed Edge/NFV clouds, supporting end-to-end latency-aware and reliable network service chaining. The Orchestration System includes advanced features such as dynamic virtual function selection and intent-based traffic steering control across heterogeneous SDN infrastructures. The experiment run under the 1st Open Call of the Fed4FIRE+ EU H2020 Project and took advantage of bare metal servers provided by the Fed4FIRE platform to set up a distributed SDN/NFV deployment. We provide details on how this Orchestration System has been deployed on top of Fed4FIRE facility and present experimental results assessing the effectiveness of the proposed Orchestration approach.
-
Demonstration of Latency-Aware and Self-Adaptive Service Chaining in 5G/SDN/NFV infrastructures
2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2018Co-Authors: Molka Gharbaoui, B. Martini, C. Contoli, G. Davoli, G. Cuffaro, F. Paganelli, W. Cerroni, P. Cappanera, Piero CastoldiAbstract:This paper will demonstrate an Orchestration System for 5G infrastructure supporting latency-minimized and self-adaptive service chaining over geographically distributed edge clouds interconnected through SDN. The demo will show an Orchestration System that comprises dynamic virtual function selection and intent-based traffic steering control functionalities to provide optimized service chains in terms of end-to-end latency and adjustable with respect to the context (e.g., service dynamics, network status).
Yanik Ngoko - One of the best experts on this subject based on the ideXlab platform.
-
IPDPS Workshops - Distributed and in-Situ Machine Learning for Smart-Homes and Buildings: Application to Alarm Sounds Detection
2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017Co-Authors: Amaury Durand, Yanik Ngoko, Christophe CerinAbstract:We consider the implementation of an in-situ machine learning System with the computing model promoted by Qarnot computing. Qarnot introduced an utility computing model in which servers are distributed in homes and offices where they serve as heaters. The Qarnot servers also embed several sensors for temperature, humidity, CO 2 etc. Qarnot offers an adequate platform to develop in-situ workflows for smart-homes problems. To demonstrate this point, we consider a typical problem: the detection of alarm sounds. Our paper introduces a new Orchestration System for in-situ workflows, in the Qarnot platform. We also consider a general parallel framework for training alarm sound classifiers and decline an implementation that makes use of our orchestrator. Finally, we evaluate the implemented framework on different aspects including: the accuracy (of the resulting classifiers) and the runtime gain of the parallelization.
-
Distributed and in-Situ Machine Learning for Smart-Homes and Buildings: Application to Alarm Sounds Detection
2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017Co-Authors: Amaury Durand, Yanik Ngoko, Christophe CerinAbstract:We consider the implementation of an in-situ machine learning System with the computing model promoted by Qarnot computing. Qarnot introduced an utility computing model in which servers are distributed in homes and offices where they serve as heaters. The Qarnot servers also embed several sensors for temperature, humidity, CO 2 etc. Qarnot offers an adequate platform to develop in-situ workflows for smart-homes problems. To demonstrate this point, we consider a typical problem: the detection of alarm sounds. Our paper introduces a new Orchestration System for in-situ workflows, in the Qarnot platform. We also consider a general parallel framework for training alarm sound classifiers and decline an implementation that makes use of our orchestrator. Finally, we evaluate the implemented framework on different aspects including: the accuracy (of the resulting classifiers) and the runtime gain of the parallelization.