Disaggregated Data Center Sleds for Edge Resource Coordination
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Solution Overview
Problem
In edge computing, the coordination of resources among multiple providers with varying service level agreements and ownerships is challenging due to the dispersed nature of edge clouds, leading to inefficiencies in resource utilization and latency in delivering services.
Innovation Solution
A data center architecture with disaggregated resources, where sleds of specific types (compute, memory, storage, etc.) are connected via switches, allowing for flexible allocation and deallocation of resources to form managed nodes, enabling independent upgrading and improved resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If resources are dispersed across multiple edge locations, then service delivery latency is reduced, but resource coordination complexity increases
Solution Approach 1:
The system segments resources into discrete, independently manageable units that can be allocated and de-allocated dynamically. Each edge location operates as an independent resource pool that can be managed separately, reducing coordination complexity while maintaining low latency delivery.
Solution Approach 2:
The resource allocation system is made dynamic, allowing real-time allocation and de-allocation of resources based on demand. This enables flexible coordination across dispersed edge locations without requiring static, complex pre-arranged claims, thereby reducing coordination overhead.
2Productivity
If resources are allocated under pre-arranged service level agreements, then resource utilization efficiency decreases, but service level guarantees are maintained
Solution Approach 1:
The system transitions from static pre-arranged allocations to dynamic real-time allocation. Resources are allocated based on actual demand and current system state, maximizing utilization efficiency while maintaining service level guarantees through continuous monitoring and adjustment.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor resource usage and service level performance. This feedback enables real-time adjustments to allocations, ensuring service level guarantees are maintained while optimizing resource utilization dynamically.
3Productivity
If resources are consolidated in centralized data centers, then resource utilization improves, but service delivery latency increases
Solution Approach 1:
The system segments the centralized resource pool into distributed edge locations, allowing resources to be physically closer to data sources and consumers. This segmentation maintains high utilization through shared resource pools while reducing latency through geographic distribution.
Solution Approach 2:
The system adds a geographic dimension to resource deployment by distributing resources across multiple edge locations rather than consolidating them in a single centralized data center. This dimensional change enables simultaneous achievement of low latency and high utilization.
Data Source
AI summary
Technologies for providing certified telemetry data indicative of resource utilizations include a device with circuitry configured to obtain telemetry data indicative of a utilization of one or more device resources over a time period. The circuitry is additionally configured to sign the obtained telemetry data with a private key associated with the present device. Further, the circuitry is configured to send the signed telemetry data to a telemetry service for analysis.