TY - GEN
T1 - Performance evaluations of multimedia service function chaining in edge clouds
AU - Imagane, Kentaro
AU - Kanai, Kenji
AU - Katto, Jiro
AU - Tsuda, Toshitaka
AU - Nakazato, Hidenori
N1 - Funding Information:
ACKNOWLEDGMENT This research is supported by “Research and Development of Fundamental and Utilization Technologies for Social Big Data,” NICT, Japan, “Grant-in-Aid for Scientific Research (A) (15H01684),” JSPS, Japan and The R&D contract "Wired-and-Wireless Converged Radio Access Network for Massive IoT Traffic" with the Ministry of Internal Affairs and Communications, Japan, for radio resource enhancement.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/3/16
Y1 - 2018/3/16
N2 - As mobile multimedia services have significantly evolved and diversified with the spread of smartphones and Internet of Things (IoT) devices, low-delay multimedia cloud computing is the need of the hour. To address this demand, in this study, we introduce an edge cloud system that equips a multimedia service function chaining capability. A prototype implementation of the proposed edge cloud system has three main features: 1) edge computing deployment by using OpenStack, 2) multimedia service slicing and chaining, and 3) efficient resource management in edge networks. Based on these features, the proposed system achieves lower multimedia processing delay compared to a conventional cloud computing platform. We deploy the proposed system in our laboratory and validate the system performance by using typical multimedia application, such as human detection in video surveillance.
AB - As mobile multimedia services have significantly evolved and diversified with the spread of smartphones and Internet of Things (IoT) devices, low-delay multimedia cloud computing is the need of the hour. To address this demand, in this study, we introduce an edge cloud system that equips a multimedia service function chaining capability. A prototype implementation of the proposed edge cloud system has three main features: 1) edge computing deployment by using OpenStack, 2) multimedia service slicing and chaining, and 3) efficient resource management in edge networks. Based on these features, the proposed system achieves lower multimedia processing delay compared to a conventional cloud computing platform. We deploy the proposed system in our laboratory and validate the system performance by using typical multimedia application, such as human detection in video surveillance.
KW - Edge computing
KW - Multimedia processing
KW - Multimedia service slicing
KW - OpenStack
KW - Service function chaining
UR - http://www.scopus.com/inward/record.url?scp=85046654099&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046654099&partnerID=8YFLogxK
U2 - 10.1109/CCNC.2018.8319249
DO - 10.1109/CCNC.2018.8319249
M3 - Conference contribution
AN - SCOPUS:85046654099
T3 - CCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
SP - 1
EP - 4
BT - CCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE Annual Consumer Communications and Networking Conference, CCNC 2018
Y2 - 12 January 2018 through 15 January 2018
ER -