Trace Analysis via Instruction-to-Address Mapping
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Solution Overview
Problem
Modern hyper-scale datacenters face challenges with existing tracing tools causing significant slowdown and inability to replay traces for performance analysis, leading to inefficient resource management and scheduling.
Innovation Solution
A lightweight tracing tool combined with offline trace analysis using synthetic data to reconstruct workload characteristics and behaviors from instruction traces, allowing for low overhead and efficient resource allocation without the need for a dedicated offline cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing tracing tools are used to monitor workloads, then workload information can be obtained, but significant tracing overhead and unacceptable slowdown occur
Solution Approach 1:
The patent segments the tracing process into two independent parts: (1) lightweight online instruction trace collection that runs with minimal overhead, and (2) offline analysis using synthetic data that does not affect online performance. This segmentation allows precise workload monitoring without impacting application productivity.
Solution Approach 2:
The patent introduces an intermediary mechanism - synthetic data generation - that bridges the gap between instruction traces and meaningful workload analysis. By generating synthetic data that mimics actual data access patterns, the system enables comprehensive offline analysis without requiring access to sensitive real data or impacting online performance.
2Loss of information
If existing tracing tools are used, then some trace information can be collected, but the ability to replay trace for performance analysis is lost
Solution Approach 1:
The patent performs preliminary action by collecting and storing instruction traces online with minimal overhead before offline analysis is needed. This pre-collection of structured instruction trace data enables comprehensive replay and analysis offline without requiring the original execution context or impacting online systems.
Solution Approach 2:
The patent creates a copy of the essential trace information through synthetic data generation. Instead of relying on the original execution environment for analysis, the system generates synthetic data that replicates the behavioral characteristics of the workload, enabling flexible replay and analysis offline.
3Ease of operation
If coarse-grain resource management is used, then resource allocation can be performed, but fine-grain resource allocation and performance isolation are not achieved
Solution Approach 1:
The patent enables dynamic resource allocation by continuously analyzing instruction traces and generating updated workload models. The system can adapt resource allocation decisions based on actual observed behavior rather than static assumptions, achieving fine-grain control while maintaining operational simplicity through automated analysis.
Solution Approach 2:
The patent replaces manual or coarse-grain resource management mechanisms with an automated system that uses instruction trace analysis and synthetic data generation to dynamically determine resource allocation. This substitution enables precise, fine-grain resource management without increasing operational complexity.
4Measurement precision
If offline profiling is used to perform scheduling, then workload characteristics can be analyzed, but expensive and inefficient processes are required
Solution Approach 1:
The patent uses synthetic data as a cheap substitute for expensive real-data profiling. The synthetic data can be generated quickly and discarded after analysis, replacing the need for costly, time-consuming offline profiling that requires dedicated clusters and extensive computational resources.
Solution Approach 2:
The patent changes the fundamental parameter of how workload characteristics are obtained - instead of expensive offline profiling with real data, the system uses lightweight online instruction trace collection combined with synthetic data generation. This parameter change dramatically reduces computational cost while maintaining analysis accuracy.
Data Source
AI summary
The present disclosure provides an apparatus for analyzing trace information. The apparatus includes one or more storage devices that store a set of instructions, and one or more processors. The one or more processors are configured to execute the set of instructions to cause the apparatus to: obtain, from servers, processor instruction traces corresponding to workloads performed by the servers; generate address traces based on the processor instruction traces; and perform a workload analysis in accordance with the address traces to model resource demands of the workloads.