Edge Scalable Adaptive-Grained Telemetry Processing for Multi-QoS Services
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Solution Overview
Problem
Edge computing environments face challenges in providing rich, accurate, and granular monitoring for service level agreements (SLAs) due to resource constraints and the need for efficient telemetry processing, especially in decentralized setups where real-time and low-latency requirements are critical, but resources are scarce.
Innovation Solution
Implementing edge-scalable adaptive-grained monitoring and telemetry processing that prioritizes processing where needed, using a layered architecture with specialized circuitry for SLA monitoring, telemetry function execution, and quality of service enforcement, allowing for dynamic resource allocation and fine-grained control of monitoring functions based on application criticality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive telemetry collection and processing is implemented at edge computing nodes, then monitoring precision and SLA accuracy are improved, but resource consumption and system complexity increase
Solution Approach 1:
The patent segments the telemetry processing system into multiple specialized components: infrastructure processing units (IPUs) dedicated to telemetry collection and processing, separate from general-purpose compute units. This segmentation allows comprehensive monitoring to be implemented without overwhelming the entire edge computing node, as only specific segments handle telemetry functions. The IPU is further divided into specialized circuits for different telemetry types (network, storage, compute, I/O), enabling precise monitoring through modular organization.
Solution Approach 2:
The patent introduces infrastructure processing units as intermediary components between the edge computing node's various resources and the central management system. These IPUs act as dedicated mediators that collect, process, and prioritize telemetry data from multiple sources (network interfaces, storage devices, compute units, I/O devices) before transmitting to remote systems. This intermediary layer simplifies the overall system architecture by centralizing telemetry functions in specialized units rather than distributing complex monitoring logic across all components.
2Loss of time
If real-time telemetry processing is implemented, then response time to SLA violations is reduced, but processing overhead and resource usage increase
Solution Approach 1:
The patent implements preliminary action by having infrastructure processing units continuously collect and pre-process telemetry data in real-time before SLA violations occur. The IPU maintains running counts and statistics of resource usage, establishing baselines and detecting anomalies proactively. This preliminary processing ensures that when SLA thresholds are approached or violated, the system can immediately respond with pre-computed information, reducing the time needed for analysis and response while distributing the processing workload over time rather than concentrating it at violation moments.
Solution Approach 2:
The patent applies local quality by implementing specialized processing circuits within the IPU for different types of telemetry data (network traffic, storage I/O, compute utilization, memory usage). Each specialized circuit is optimized for its specific data type, processing it with appropriate algorithms and priority levels. This localized optimization reduces overall processing overhead by avoiding generic processing approaches, as each telemetry stream is handled by circuitry designed specifically for its characteristics and SLA requirements.
3Measurement precision
If granular monitoring of multiple QoS parameters is implemented, then service level agreement accuracy is improved, but data processing volume and complexity increase
Solution Approach 1:
The patent extracts and isolates telemetry processing functions into dedicated infrastructure processing units, separating them from general-purpose compute operations. The IPU extracts only the necessary QoS parameters (network throughput, latency, jitter, storage I/O rates, compute utilization, memory usage) from the vast amount of available system data, focusing processing on these specific metrics rather than analyzing all possible system variables. This extraction approach maintains high SLA accuracy by monitoring relevant parameters while reducing overall data processing volume by ignoring extraneous information.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting which QoS parameters are monitored and their measurement granularities based on service priorities and current system conditions. The IPU can change monitoring parameters in real-time, intensifying observation of critical resources during high-demand periods while reducing measurement frequency for non-critical parameters during low-utilization periods. This dynamic parameter adjustment maintains accurate SLA monitoring for essential services while reducing data processing volume overall by adapting monitoring intensity to actual needs.
Data Source
AI summary
Methods and apparatus to implement edge scalable adaptive-grained monitoring and telemetry processing for multi-quality of service (QoS) services are disclosed. In one example, the apparatus includes platform compute circuitry. The apparatus also includes both a monitoring function registry interface data structure to list a set of monitoring functions that provide at least a unique universal function identifier and a function descriptor and an application assignment interface data structure that provide at least an application identifier, an application service level agreement (SLA) definition, and the function descriptor to enable a link to a first monitoring function. Additionally, the apparatus includes an SLA monitoring circuitry that instantiates the first monitoring function for an application instance in a logic stack, causes the first monitoring function to execute in the hardware and software monitoring logic stack, and generates a QoS enforcement callback in response to a violation of the SLA definition.