TY - GEN
T1 - CLEDGE
T2 - 46th IEEE Conference on Local Computer Networks, LCN 2021
AU - Al Azad, Md Washik
AU - Shannigrahi, Susmit
AU - Stergiou, Nicholas
AU - Ortega, Francisco R.
AU - Mastorakis, Spyridon
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/10/4
Y1 - 2021/10/4
N2 - In today's era of Internet of Things (IoT), where massive amounts of data are produced by IoT and other devices, edge computing has emerged as a prominent paradigm for low-latency data processing. However, applications may have diverse latency requirements: certain latency-sensitive processing operations may need to be performed at the edge, while delay-tolerant operations can be performed on the cloud, without occupying the potentially limited edge computing resources. To achieve that, we envision an environment where computing resources are distributed across edge and cloud offerings. In this paper, we present the design of CLEDGE (CLoud + EDGE), an information-centric hybrid cloud-edge framework, aiming to maximize the on-time completion of computational tasks offloaded by applications with diverse latency requirements. The design of CLEDGE is motivated by the networking challenges that mixed reality researchers face. Our evaluation demonstrates that CLEDGE can complete on-time more than 90% of offloaded tasks with modest overheads.
AB - In today's era of Internet of Things (IoT), where massive amounts of data are produced by IoT and other devices, edge computing has emerged as a prominent paradigm for low-latency data processing. However, applications may have diverse latency requirements: certain latency-sensitive processing operations may need to be performed at the edge, while delay-tolerant operations can be performed on the cloud, without occupying the potentially limited edge computing resources. To achieve that, we envision an environment where computing resources are distributed across edge and cloud offerings. In this paper, we present the design of CLEDGE (CLoud + EDGE), an information-centric hybrid cloud-edge framework, aiming to maximize the on-time completion of computational tasks offloaded by applications with diverse latency requirements. The design of CLEDGE is motivated by the networking challenges that mixed reality researchers face. Our evaluation demonstrates that CLEDGE can complete on-time more than 90% of offloaded tasks with modest overheads.
KW - Hybrid Cloud-Edge Computing
KW - Mixed Reality
KW - Named Data Networking
UR - http://www.scopus.com/inward/record.url?scp=85118430207&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118430207&partnerID=8YFLogxK
U2 - 10.1109/LCN52139.2021.9525006
DO - 10.1109/LCN52139.2021.9525006
M3 - Conference contribution
AN - SCOPUS:85118430207
T3 - Proceedings - Conference on Local Computer Networks, LCN
SP - 589
EP - 596
BT - Proceedings of the IEEE 46th Conference on Local Computer Networks, LCN 2021
A2 - Khoukhi, Lyes
A2 - Oteafy, Sharief
A2 - Bulut, Eyuphan
PB - IEEE Computer Society
Y2 - 4 October 2021 through 7 October 2021
ER -