N-tier Edge Workload Orchestration via Dynamic Placement
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Solution Overview
Problem
Current edge computing configurations, such as the 3-tier datacenter-edge-end-device design, are not well-suited for many use cases and lack a common platform for developers to implement effective alternatives without significant engineering and infrastructural effort, leading to inefficiencies in workload and data orchestration across edge stations.
Innovation Solution
An N-tier edge design is implemented, allowing for dynamic workload and data placement based on network performance, using a developer-defined manifest to optimize placement across edge stations, with optional network telemetry data inclusion and support for multiple cloud/infrastructure providers, enabling developers to specify startup strategies, network requirements, and hardware specifications for optimized user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a 3-tier datacenter-edge-end-device design is used, then the system provides a known fixed architecture, but it is not well-suited for many use cases and lacks flexibility for developers to implement alternatives
Solution Approach 1:
The patent implements an N-tier architecture that serves as a universal platform capable of adapting to multiple different use cases and deployment scenarios. The system can dynamically configure different numbers of tiers (N) between datacenter and end-device, allowing it to function as a multi-functional framework that accommodates various application requirements without requiring separate fixed architectures for each use case.
Solution Approach 2:
The system employs dynamic workload and data placement capabilities that allow the architecture to adapt its configuration in real-time based on network conditions and performance requirements. The N-tier design enables flexible adjustment of the number and positioning of edge stations, transforming the static 3-tier model into a dynamic system that can be optimized for different scenarios.
2Reliability
If dynamic workload placement based on network performance is implemented, then user experience is optimized, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring network performance metrics and using this information to dynamically adjust workload and data placement decisions. The orchestration system receives feedback about network conditions, latency, and performance, then automatically repositions workloads to optimize user experience, creating a closed-loop control system that balances complexity with performance benefits.
Solution Approach 2:
The orchestration system performs self-service by automatically making placement decisions based on monitored network conditions without requiring manual intervention. The system autonomously evaluates performance metrics and redistributes workloads across the N-tier architecture to maintain optimal user experience, reducing the operational complexity burden on users while preserving system intelligence.
3Productivity
If network telemetry data is collected for orchestration optimization, then placement efficiency improves, but user privacy concerns increase
Solution Approach 1:
The system introduces privacy-preserving mechanisms as intermediaries between telemetry data collection and orchestration processing. Anonymous identifiers and aggregated statistics serve as mediators that enable performance optimization through network telemetry while protecting user privacy by removing personally identifiable information before data is used for placement decisions.
Solution Approach 2:
The system applies different quality levels of data processing to different portions of the system. Network telemetry data is processed locally at edge stations with privacy-sensitive information stripped or anonymized before being used for orchestration decisions. This local quality approach allows productivity benefits from telemetry while containing privacy risks at the data collection point.
Data Source
AI summary
One example method includes creating a manifest that specifies one or more requirements concerning execution of an application that resides at an end device in an N-tier configuration, identifying a workload that is associated with the application and executable at one or more edge stations of the N-tier configuration, gathering and evaluating network telemetry, orchestrating the workload based on the network telemetry and the manifest, scheduling performance of the workload at the one or more edge stations, and performing the workload at the one or more edge stations in accordance with the scheduling.