Proposed Decoupled Edge Computing Architecture: Zenith 😄


In the Internet of Things(IoT) era, the demands for low-latency computing for time-sensitive applications (e.g., location-based augmented reality games, real-time smart grid management, real-time navigation using wearables) has been growing rapidly. Edge Computing provides an additional layer of infrastructure to fill latency gaps between IoT devices and the backend computing infrastructure.

Zenith decouples resource allocation and service provisioning and management at the edge and provides a novel model to allocate resources at the edge to maximize utility. Zenith aims at increasing the overall resource allocation efficiency by allowing resources to be allocated and shared in a latency-aware manner.

What is the challenge?

  • Fair and Efficient Resource Allocation across Geographically Distributed Edge Resources
  • Fast Service Discovery on Highly Distributed Micro Data Centers

How Zenith Works?

  • Geographic Division usingWeighted Vonoroi Diagram (WVD)
    • Service Discovery with Constant Time
    • Naturally Satisfies Latency-Sensitive Workloads with Nearest Service Discovery
    • Dynamically Balances the Workloads Between Micro Data Centers
    • Easy and Fast to Update
  • Auction-based Resource Allocation
    • Truthfulness: No Incentives for Cheating.
    • Budget Balance: Keeps the Allocation Process more Sustainable
    • Naturally Finds the Equilibrium Between Supply and Demand
  • Resource Sharing Contracts
    • Longer duration of Contracts Avoid Frequent Migrations
    • Stable Running Environment Suits the Requirements for Latency-Sensitive Workloads



Jinlai Xu (徐锦来)
Jinlai Xu (徐锦来)

My research interests include Big Model Training/Inference/Finetune Framework, Serverless Computing, Distributed Systems, Fog/Edge and Cloud Computing, Stream Processing Optimization and Blockchain-based Techniques