TY - GEN
T1 - Edge computing resource procurement
T2 - 4th IEEE World Forum on Internet of Things, WF-IoT 2018
AU - Nguyen, Duong Tung
AU - Le, Long Bao
AU - Bhargava, Vijay
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/4
Y1 - 2018/5/4
N2 - Edge computing (EC) has emerged as a key technology in future communication networks to enhance user experience and enable various Internet of Things (IoT) applications. In this paper, we propose an online framework for edge computing resource procurement in which a marketplace (platform) is established between sellers (i.e., resource contributors) and buyers (i.e., resource purchasers). Each buyer has a certain budget for his procurement campaign. The sellers arrive to the platform in an online fashion and offer their computing capacities along with prices that they want to be compensated for their services. Upon the arrival of every new offer, the platform has to make an irrevocable decision, without knowing future information, to accept the offer or not and to allocate the accepted resource to which buyer. We present an efficient online optimization method that helps the platform maximize the total system utility with guaranteed performance. Indeed, the developed model can be applied to other interesting settings such as edge caching and content delivery with slight modifications. Finally, numerical studies are conducted to illustrate the effectiveness of the proposed solution approach.
AB - Edge computing (EC) has emerged as a key technology in future communication networks to enhance user experience and enable various Internet of Things (IoT) applications. In this paper, we propose an online framework for edge computing resource procurement in which a marketplace (platform) is established between sellers (i.e., resource contributors) and buyers (i.e., resource purchasers). Each buyer has a certain budget for his procurement campaign. The sellers arrive to the platform in an online fashion and offer their computing capacities along with prices that they want to be compensated for their services. Upon the arrival of every new offer, the platform has to make an irrevocable decision, without knowing future information, to accept the offer or not and to allocate the accepted resource to which buyer. We present an efficient online optimization method that helps the platform maximize the total system utility with guaranteed performance. Indeed, the developed model can be applied to other interesting settings such as edge caching and content delivery with slight modifications. Finally, numerical studies are conducted to illustrate the effectiveness of the proposed solution approach.
KW - crowd-sourcing
KW - Edge computing
KW - online optimization
KW - resource procurement
UR - http://www.scopus.com/inward/record.url?scp=85050391576&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050391576&partnerID=8YFLogxK
U2 - 10.1109/WF-IoT.2018.8355134
DO - 10.1109/WF-IoT.2018.8355134
M3 - Conference contribution
AN - SCOPUS:85050391576
T3 - IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings
SP - 807
EP - 812
BT - IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 February 2018 through 8 February 2018
ER -