TY - GEN
T1 - EaaS: A Service-Oriented Edge Computing Framework Towards Distributed Intelligence
AU - Zhang, Mingjin
AU - Cao, Jiannong
AU - Sahni, Yuvraj
AU - Chen, Qianyi
AU - Jiang, Shan
N1 - Funding Information:
VII. ACKNOWLEDGEMENT This work is supported by the Hong Kong RGC General Research Fund under Grant PolyU 15204921 and PolyU 15220922.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/10
Y1 - 2022/10
N2 - Edge computing has become a popular paradigm where services and applications are deployed at the network edge closer to the data sources. It provides applications with outstanding benefits, including reduced response latency and enhanced privacy protection. For emerging advanced applications, such as autonomous vehicles, industrial IoT, and metaverse, further research is needed. This is because such applications demand ultra-low latency, hyper-connectivity, and dynamic and reliable service provision, while existing approaches are inadequate to address the new challenges. Hence, we envision that the future edge computing is moving towards distributed intelligence, where heterogeneous edge nodes collaborate to provide services in large-scale and geo-distributed edge infrastructure. We thereby propose Edge-as-a-Service (EaaS) to enable distributed intelligence. EaaS jointly manages large-scale cross-node edge resources and facilitates edge autonomy, edge-to-edge collaboration, and resource elasticity. These features enable flexible deployment of services and ubiquitous computation and intelligence. We first give an overview of existing edge computing studies and discuss their limitations to articulate the motivation for proposing EaaS. Then, we describe the details of EaaS, including the physical architecture, proposed software framework, and benefits of EaaS. Various application scenarios, such as real-time video surveillance, smart building, and metaverse, are presented to illustrate the significance and potential of EaaS. Finally, we discuss several challenging issues of EaaS to inspire more research towards this new edge computing framework.
AB - Edge computing has become a popular paradigm where services and applications are deployed at the network edge closer to the data sources. It provides applications with outstanding benefits, including reduced response latency and enhanced privacy protection. For emerging advanced applications, such as autonomous vehicles, industrial IoT, and metaverse, further research is needed. This is because such applications demand ultra-low latency, hyper-connectivity, and dynamic and reliable service provision, while existing approaches are inadequate to address the new challenges. Hence, we envision that the future edge computing is moving towards distributed intelligence, where heterogeneous edge nodes collaborate to provide services in large-scale and geo-distributed edge infrastructure. We thereby propose Edge-as-a-Service (EaaS) to enable distributed intelligence. EaaS jointly manages large-scale cross-node edge resources and facilitates edge autonomy, edge-to-edge collaboration, and resource elasticity. These features enable flexible deployment of services and ubiquitous computation and intelligence. We first give an overview of existing edge computing studies and discuss their limitations to articulate the motivation for proposing EaaS. Then, we describe the details of EaaS, including the physical architecture, proposed software framework, and benefits of EaaS. Various application scenarios, such as real-time video surveillance, smart building, and metaverse, are presented to illustrate the significance and potential of EaaS. Finally, we discuss several challenging issues of EaaS to inspire more research towards this new edge computing framework.
KW - edge as a service
KW - Edge computing
KW - edge intelligence
KW - edge-native applications
KW - service-oriented architecture
UR - http://www.scopus.com/inward/record.url?scp=85141370088&partnerID=8YFLogxK
U2 - 10.1109/SOSE55356.2022.00026
DO - 10.1109/SOSE55356.2022.00026
M3 - Conference article published in proceeding or book
AN - SCOPUS:85141370088
T3 - Proceedings - 16th IEEE International Conference on Service-Oriented System Engineering, SOSE 2022
SP - 165
EP - 175
BT - Proceedings - 16th IEEE International Conference on Service-Oriented System Engineering, SOSE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Service-Oriented System Engineering, SOSE 2022
Y2 - 15 August 2022 through 18 August 2022
ER -