3,712 research outputs found
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Demonstration of latency-aware 5G network slicing on optical metro networks
The H2020 METRO-HAUL European project has architected a latency-aware, cost-effective, agile, and programmable optical metro network. This includes the design of semi-disaggregated metro nodes with compute and storage capabilities, which interface effectively with both 5G access and multi-Tbit/s elastic optical networks in the core. In this paper, we report the automated deployment of 5G services, in particular, a public safety video surveillance use case employing low-latency object detection and tracking using on-camera and on-the-edge analytics. The demonstration features flexible deployment of network slice instances, implemented in terms of ETSI NFV Network Services. We summarize the key findings in a detailed analysis of end-to-end quality of service, service setup time, and soft-failure detection time. The results show that the round-trip-time over an 80 km link is under 800 µs and the service deployment time under 180 seconds.Horizon 2020 Framework Programme (761727); Bundesministerium für Bildung und Forschung (16KIS0979K).Peer ReviewedArticle signat per 25 autors/es:
B. Shariati, Fraunhofer HHI, Berlin, Germany / L. Velasco, Universitat Politècnica de Catalunya, Barcelona, Spain / J.-J. Pedreno-Manresa, ADVA, Munich, Germany / A. Dochhan, ADVA, Munich, Germany / R. Casellas, Centre Tecnològic Telecomunicacions Catalunya, Castelldefels, Spain / A. Muqaddas, University of Bristol, Bristol, UK / O. Gonzalez de Dios, Telefónica, Madrid, Spain / L. Luque Canto, Telefónica, Madrid, Spain / B. Lent, Qognify GmbH, Bruchsal, Germany / J. E. Lopez de Vergara, Naudit HPCN, Madrid, Spain / S. Lopez-Buedo, Naudit HPCN, Madrid, Spain / F. Moreno, Universidad Politécnica de Cartagena, Cartagena, Spain / P. Pavon, Universidad Politécnica de Cartagena, Cartagena, Spain / M. Ruiz, Universitat Politècnica de Catalunya, Barcelona, Spain / S. K. Patri, ADVA, Munich, Germany / A. Giorgetti, CNIT, Pisa, Italy / F. Cugini, CNIT, Pisa, Italy / A. Sgambelluri, CNIT, Pisa, Italy / R. Nejabati, University of Bristol, Bristol, UK / D. Simeonidou, University of Bristol, Bristol, UK / R.-P. Braun, Deutsche Telekom, Germany / A. Autenrieth, ADVA, Munich, Germany / J.-P. Elbers, ADVA, Munich, Germany / J. K. Fischer, Fraunhofer HHI, Berlin, Germany / R. Freund, Fraunhofer HHI, Berlin, GermanyPostprint (author's final draft
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