Distributed Computing and Storage Allocation by Geographic Proximity
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Solution Overview
Problem
Existing computing and storage resources in devices such as cell sites, data centers, and IoT devices often have idle capacity that is not utilized efficiently, leading to underutilization and increased demand for additional resources.
Innovation Solution
A system that intelligently selects and allocates available computing and storage resources from geographically separated nodes based on demand and availability, using artificial intelligence to predict usage patterns and adjust resource allocation dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If idle computing resources are allocated to remote devices, then resource utilization increases, but system complexity increases
Solution Approach 1:
A resource scheduler acts as an intermediary between computing nodes and remote devices. The scheduler receives resource requests, identifies available idle resources across the network, matches requests with suitable nodes, and manages the allocation dynamically. This mediator approach enables resource sharing without requiring direct complex interactions between all nodes and devices, thus increasing utilization while managing system complexity.
2Loss of time
If computing nodes are selected based on geographic proximity, then latency is reduced, but the quantity of available resources decreases
Solution Approach 1:
The system segments the resource pool into geographic regions or zones. Within each zone, the scheduler prioritizes allocating resources from nearby computing nodes to minimize latency. When local resources are insufficient, the scheduler can then allocate resources from other zones. This segmentation enables the system to optimize for both proximity and resource availability by handling allocations hierarchically.
3Adaptability or versatility
If resources are allocated dynamically based on demand, then adaptability increases, but control difficulty increases
Solution Approach 1:
The resource scheduler implements continuous feedback mechanisms by monitoring the status of computing nodes (idle, busy, load levels) and resource requests (pending, active, completed). Based on this real-time feedback, the scheduler dynamically adjusts resource allocation decisions. When nodes become idle, they are added to the available pool; when they become busy, allocations are adjusted. This feedback-driven approach enables adaptability while maintaining manageable control through automated decision-making based on current system state.
Data Source
AI summary
Systems and methods for utilizing distributed computing and storage resources intelligently select processing and/or storage resources of cell sites, data centers and/or other computing nodes and tap into these available processing and data storage resources to harness these resources on behalf of other remote devices to increase the amount of computing and storage data resources to perform tasks and store data for various services. The system predicts demand and usage and dynamically selects and adjusts utilization of computing resources to meet the demand and utilize otherwise idle systems. To improve latency and reduce network congestion by avoiding adding network traffic over longer and more complex routes, the system provides resources to particular remote devices based on the geographic proximity of the computing node to the particular remote device requesting the resources.