AISim
  • 1. Market Background
    • 1.1 Development Prospects
    • 1.2 Potential Challenges
  • 2. AISim: The World’s First Web3 IoE Network
    • 2.1 AIA Protocol (AISim Intelligent Access Protocol)
    • 2.2 Decentralized Identity Authentication (DID) System
    • 2.3 Distributed AI Acceleration Engine
    • 2.4 Intelligent Privacy Computing Module
    • 2.5 DeAI Client (Decentralized AI Client)
    • 2.6 IoE Data Management and Intelligent Caching System
  • 3. Technical Architecture
    • 3.1 AIA Protocol
      • 3.1.1 Protocol Adaptation Layer
      • 3.1.2 Distributed Task Scheduling Engine
      • 3.1.3 Decentralized Communication Network
      • 3.1.4 Cross-layer Data Encryption and Privacy Protection
      • 3.1.5 Dynamic Resource Scheduling and Optimization
    • 3.2 Distributed AI Acceleration
      • 3.2.1 Edge Node Computing Optimization
      • 3.2.2 Multi-Node Distributed Execution
      • 3.2.3 Privacy-Preserving Collaborative Training
    • 3.3 Decentralized Identity and Access Management
      • 3.3.1 Identity Verification and Access Level Grading
      • 3.3.2 Multi-level Data Protection
  • 4. Application Scenario
    • 4.1 Smart Healthcare and Health Management
    • 4.2 Autonomous Driving and Intelligent Transportation
    • 4.3 Agricultural Internet of Things and Precision Agriculture
    • 4.4 Industrial Automation and Intelligent Manufacturing
    • 4.5 Smart City and Public Services
    • 4.6 Edge Computing and Distributed AI
  • 5. IoE Web3 Ecosystem Construction
    • 5.1 DID Physical Nodes
      • 5.1.1 Types of Smart SIM Cards
      • 5.1.2 Rights of DID Physical Nodes
    • 5.2 MVNO Integration
    • 5.3 Ecosystem Incentives
  • 6. Tokenomics
    • 6.1 Token Distribution
    • 6.2 AST Token Use Cases
  • 7.Roadmap
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  1. 3. Technical Architecture
  2. 3.1 AIA Protocol

3.1.2 Distributed Task Scheduling Engine

The task scheduling engine is a key component of the AISim network. It dynamically adjusts computing, storage, and bandwidth resources based on task priority, resource requirements, and node load conditions. The task scheduling engine can efficiently evaluate task priorities and supports task sharding and parallel processing, enhancing the overall system's task execution efficiency. In the IoE environment, tasks are complex and variable, and distributed task scheduling ensures that tasks are quickly and efficiently allocated and executed across nodes.

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Last updated 1 month ago