Dynamic Server Resource Allocation for Online Education
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Solution Overview
Problem
Existing online education systems face challenges in providing individualized learning and managing resources efficiently, as students learn solo and providers must over-provision hardware and software to accommodate worst-case scenarios.
Innovation Solution
A method and system utilizing a blockchain configured on a public ledger to secure user information, combined with data capturing devices equipped with artificial intelligence techniques, to manage computing resources and generate crypto-coins through a learning module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If online education systems purchase enough hardware or software services to serve the worst-case scenarios, then system reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements dynamic resource allocation where computing resources are adjusted in real-time based on actual student engagement and learning patterns detected by AI algorithms. Instead of static over-provisioning, the system dynamically scales resources to match actual demand, maintaining reliability while reducing complexity.
Solution Approach 2:
The system employs AI-driven automated monitoring and management of computing resources that self-adjust based on detected student needs and system performance metrics. This self-service approach eliminates manual intervention for resource provisioning while maintaining system reliability through continuous automated optimization.
2Productivity
If data capturing devices with AI techniques are deployed to manage computing resources, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent extracts complex AI processing functions from individual student devices and consolidates them on centralized server infrastructure. This allows productivity improvements through advanced AI analytics while keeping student devices relatively simple, as the computational burden is taken out and handled centrally.
Solution Approach 2:
The system introduces an intermediary AI layer that sits between data capturing devices and resource management functions. This intermediary handles the complex AI processing and resource allocation decisions, allowing simple devices to achieve high productivity through the mediating intelligence of the AI system.
Data Source
AI summary
A method for dynamically allocating server resources includes receiving a request from a client system, wherein the request comprises a request for a first set of streaming data, providing from the server to the client system a first portion of streaming data from the first set of streaming data, wherein the first portion is associated with a first quality of service level, receiving user activity data from the client system for the first portion of the streaming data, determining a second quality of service level for a second portion of the streaming data from the first set of streaming data, providing from the server to the client system the second portion of streaming data from the first set of streaming data, wherein the second portion provided with the second quality of service level, and wherein the first quality of service level is different from the second quality of service level.