TY - GEN
T1 - EdgeNet
T2 - 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018
AU - Cappos, Justin
AU - Rafetseder, Albert
AU - Hemmings, Matthew
AU - McGeer, Rick
AU - Ricart, Glenn
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/12/6
Y1 - 2018/12/6
N2 - EdgeNet has been informed by the advances of cloud computing and the successes of such distributed systems as PlanetLab, GENI, G-Lab, SAVI, and V-Node: a large number of small points-of-presence, designed for the deployment of highly distributed experiments and applications. EdgeNet differs from its predecessors in two significant areas: first, it is a software-only infrastructure, where each worker node is designed to run part-or full-time on existing hardware at the local site; and, second, it uses modern, industry-standard software both as the node agent and the control framework. The first innovation permits rapid and unlimited scaling: whereas GENI and PlanetLab required the installation and maintenance of dedicated hardware at each site, EdgeNet requires only a software download, and a node can be added to the EdgeNet infrastructure in 15 minutes. The second offers performance, maintenance, and training benefits; rather than maintaining bespoke kernels and control frameworks, and developing training materials on using the latter, we are able to ride the wave of open-source and industry development, and the plethora of industry and community tutorial materials developed for industry standard control frameworks. The result is a global Kubernetes cluster, where pods of Docker containers form the service instances at each point of presence.
AB - EdgeNet has been informed by the advances of cloud computing and the successes of such distributed systems as PlanetLab, GENI, G-Lab, SAVI, and V-Node: a large number of small points-of-presence, designed for the deployment of highly distributed experiments and applications. EdgeNet differs from its predecessors in two significant areas: first, it is a software-only infrastructure, where each worker node is designed to run part-or full-time on existing hardware at the local site; and, second, it uses modern, industry-standard software both as the node agent and the control framework. The first innovation permits rapid and unlimited scaling: whereas GENI and PlanetLab required the installation and maintenance of dedicated hardware at each site, EdgeNet requires only a software download, and a node can be added to the EdgeNet infrastructure in 15 minutes. The second offers performance, maintenance, and training benefits; rather than maintaining bespoke kernels and control frameworks, and developing training materials on using the latter, we are able to ride the wave of open-source and industry development, and the plethora of industry and community tutorial materials developed for industry standard control frameworks. The result is a global Kubernetes cluster, where pods of Docker containers form the service instances at each point of presence.
UR - http://www.scopus.com/inward/record.url?scp=85060240456&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060240456&partnerID=8YFLogxK
U2 - 10.1109/SEC.2018.00045
DO - 10.1109/SEC.2018.00045
M3 - Conference contribution
AN - SCOPUS:85060240456
T3 - Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018
SP - 359
EP - 360
BT - Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 October 2018 through 27 October 2018
ER -