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+# Memory Collector Telemetry Strategy
+
+## Overview
+
+Our telemetry strategy prioritizes high-resolution, low-level data collection to build a foundation for understanding memory subsystem interference. By focusing on simplicity and data quality in the initial collector, we can enable rapid iteration and validation of detection algorithms.
+
+**The key aspects of our approach are:**
+
+- Collect per-process, per-core metrics at 1 millisecond granularity to capture interference at a meaningful timescale
+- Collect per-process cache occupancy metrics at 1 millisecond granularity
+- Generate synchronized datasets for joint analysis
+- Implement in stages to manage complexity
+
+**This "firehose" telemetry will enable us to build a dataset for offline analysis, allowing us to identify patterns and develop algorithms for real-time interference detection.**
+
+## Telemetry Collection
+
+The collector will monitor and record the following metrics for each process at 1 millisecond granularity:
+
+- Process ID
+- Core ID 
+- Core frequency during the measured interval
+- Cycles 
+- Instructions
+- Last level cache misses
+
+Modern cloud environments routinely run dozens or even hundreds of applications on a single server, each with its own dynamic memory usage patterns. In an extreme case, with 100 applications changing phase every second on average, there would be a phase change every 10 milliseconds in aggregate.
+
+**The 1 millisecond telemetry granularity enables us to detect this behavior and characterize interference at a meaningful timescale.**
+
+In addition to these per-process metrics, we will also collect cache occupancy measurements using Intel RDT's Cache Monitoring Technology (CMT) or an equivalent mechanism. This data will be collected per process at the same 1 millisecond granularity.
+
+**Monitoring cache usage per process is necessary because caches maintain state across context switches and are shared by all threads of a process.**
+
+## Data Format
+
+For the initial version, telemetry will be written to CSV files to simplify data collection and analysis. Each row will represent a single measurement interval for a specific process.
+
+**We will generate two datasets:**
+
+1. Per-process, per-core measurements (process ID, core ID, frequency, cycles, instructions, LLC misses)
+2. Per-process cache occupancy measurements
+
+**While these datasets will be separate, they will be synchronized and aligned by timestamp to enable joint analysis.**
+
+## Implementation Stages
+
+To manage complexity, we will implement telemetry collection in two stages:
+
+1. Collect per-process, per-core measurements (process ID, core ID, frequency, cycles, instructions, LLC misses)
+2. Add per-process cache occupancy measurements using Intel RDT or an equivalent mechanism
+
+**This staged approach allows us to validate the core telemetry pipeline before adding the complexity of cache monitoring.**
+
+For the cache monitoring stage, we will need to assign each process a unique identifier (e.g., CLOS for Intel RDT) to track its cache usage. This will require additional system-level coordination and metadata management.
+
+## Analysis and Algorithm Development
+
+By collecting high-resolution telemetry from multiple clusters, both real-world deployments and benchmark environments, we aim to build a representative dataset capturing a wide range of interference scenarios.
+
+**Analyzing this data offline using big data techniques will help us identify common interference patterns, resource usage signatures, and relevant metrics for detecting contention.**
+
+These insights will inform the development of algorithms for real-time interference detection in future collector versions. Starting with a thorough understanding of low-level behavior is key to building effective higher-level detection and mitigation strategies.
+