End-to-End Hardware Tracing for Edge Performance Analysis
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Solution Overview
Problem
Current scale-out computing systems lack hardware support for tracking and analyzing Edge applications as they propagate through multiple Edge nodes, leading to performance analysis gaps and challenges in debugging and resource management in Edge computing environments.
Innovation Solution
A scale-out tracing system is introduced that integrates hardware hooks into Edge devices for end-to-end tracing across heterogeneous environments, enabling fine-grained billing, resource alignment, and context abstraction for comprehensive analysis of Edge applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware tracing is implemented across multiple Edge nodes, then measurement precision and debugging capability are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The tracing system is segmented into distributed tracing clients deployed on individual Edge nodes and a centralized tracing server. Each node independently collects and processes tracing data locally, then aggregates results centrally. This segmentation allows precise local measurements while distributing system complexity across multiple manageable components rather than requiring a monolithic complex system.
Solution Approach 2:
The patent introduces hardware performance counters and tracing agents as intermediary components between the Edge application workloads and the analysis tools. These intermediaries capture detailed performance data at the hardware level and translate it into standardized tracing formats, enabling precise measurement without requiring direct complex integration between all system components.
2Ease of operation
If end-to-end tracing is implemented across distributed Edge infrastructure, then debugging capability and performance analysis are improved, but ease of operation and implementation difficulty worsen
Solution Approach 1:
The tracing system implements universal tracing interfaces and standardized data formats that work across heterogeneous Edge nodes, cloud infrastructure, and different workload types. The same tracing mechanism can track various application protocols and service patterns uniformly, simplifying operation across diverse environments while abstracting away the underlying infrastructure complexity through consistent APIs and trace aggregation.
3Measurement precision
If hardware performance counters are utilized for detailed tracing, then measurement precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The system implements selective tracing that activates hardware performance counters only for specific Edge nodes, time periods, or workload types that require detailed analysis. Rather than continuously monitoring all nodes at maximum precision, the system applies partial tracing action only where needed, achieving sufficient measurement precision for critical paths while reducing overall energy consumption and computational overhead on less critical infrastructure.
Data Source
AI summary
Systems, methods, articles of manufacture, and apparatus for end-to-end hardware tracing in an Edge network are disclosed. An example compute device includes at least one memory, instructions in the compute device, and processing circuitry to execute the instructions to, in response to receiving detecting an object having a global group identifier, generate monitoring data corresponding to a respective process executing on the compute device, the monitoring data including a process identifier, index the monitoring data having the process identifier to the corresponding global group identifier, synchronize a time stamp of the monitoring data to a network time protocol corresponding to the global group identifier, and transmit the indexed and synchronized monitoring data as tracing data in to the a tracing datastore.