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Edge computing has become a popular paradigm where services and applicationsare deployed at the network edge closer to the data sources. It providesapplications with outstanding benefits, including reduced response latency andenhanced privacy protection. For emerging advanced applications, such asautonomous 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 areinadequate to address the new challenges. Hence, we envision that the futureedge computing is moving towards distributed intelligence, where heterogeneousedge nodes collaborate to provide services in large-scale and geo-distributededge infrastructure. We thereby propose Edge-as-a-Service (EaaS) to enabledistributed intelligence. EaaS jointly manages large-scale cross-node edgeresources and facilitates edge autonomy, edge-to-edge collaboration, andresource elasticity. These features enable flexible deployment of services andubiquitous computation and intelligence. We first give an overview of existingedge computing studies and discuss their limitations to articulate themotivation for proposing EaaS. Then, we describe the details of EaaS, includingthe physical architecture, proposed software framework, and benefits of EaaS.Various application scenarios, such as real-time video surveillance, smartbuilding, and metaverse, are presented to illustrate the significance andpotential of EaaS. Finally, we discuss several challenging issues of EaaS toinspire more research towards this new edge computing framework.

EaaS: A Service-Oriented Edge Computing Framework Towards Distributed Intelligence