Siri Health Monitoring & Analyzer Tool

 

Real-time observability, safe tuning, and automated guidance for Linux and Android

Siri Health Monitoring & Analyzer gives engineers accurate visibility into device behavior and safe, auditable controls to tune it. It runs on Linux and Android, reads authoritative kernel data, and performs deterministic actions such as CPU affinity and control-group configuration. Advanced analysis is built in: CPU flame graphs and system call reports. The tool is integrated with a local Falcon LLM for automated log/telemetry analysis and recommendations but it can also connect to online LLM services when desired.

What it is

• On-device service: Samples kernel/system state, computes deltas, executes tuning actions, and streams structured metrics.
• Desktop client: Renders live dashboards and timelines, triggers analyses, and provides control panels with full audit trails.
• No kernel rebuilds: Uses standard kernel facilities (procfs/sysfs, perf events/ftrace) for lab, rack, vehicle, chamber, and field deployments.

Platforms & connectivity

• Targets: Linux and Android on modern kernels.
• Transport: TCP; for Android, also via ADB port forwarding.
• Modes: Connected or air-gapped (fully offline supported).

Telemetry sources (low overhead, high fidelity)

Collected from standard kernel interfaces to minimize overhead and preserve correctness:

• CPU: per-core utilization, run-queue depth, context switches, per-CPU counters.
• Processes/threads: scheduler state, CPU time.
• Memory: usage/pressure/reclaim/swap.
• Storage: I/O counts and latency indicators.
• Network: throughput/error counters.
• Interrupts/softirqs: counts and rates.
• System: uptime, load averages, scheduler summaries.
• Control groups: per-group metrics for cgroups v1/v2.

Sampling rates are configurable per signal family.

Built-in advanced analysis

CPU flame graphs

Generate interactive flame graphs from periodic samples to identify hot call paths and quantify time in user vs kernel space.

System call analysis
Generate per-process system-call histograms, latency percentiles, and blocking indicators for automatically detected resource-hungry processes. Highlight dominant calls (e.g., futex, read, write) and flag abnormal storage or network service times.

 

Focused capture sessions

Run short or extended captures with elevated sampling, derived metrics, and marker events; correlate CPU, memory, storage, and network timelines in one view.

Comparative runs (A/B)

Compare code or configuration changes with side-by-side metrics, flame graphs, and system-call deltas.

Interactive real-time controls (authenticated, validated, logged)

Admin privileges on the device are required for tuning operations. The tool enforces prerequisite checks and refuses unsafe actions.

• CPU affinity management
  Set/clear process and thread affinity masks using standard system calls. Pin time-critical workloads, isolate background tasks, and verify impact live.
• Control-group configuration

Create/apply cgroups for CPU, cpuset, memory, I/O, and pids (where supported). Assign processes to groups, set quotas/shares, and persist profiles for repeatable tests.

• Profiles & rollback

Save tuned combinations as named profiles; apply across devices; roll back instantly if acceptance checks fail.

Integration & automated guidance

• Log server integration
  Stream or export metrics, events, and analysis artifacts to local or online servers (including direct file export). Fully offline workflows are supported.
• Local Falcon LLM (POC)
  A locally hosted Falcon LLM can analyze telemetry and logs to propose targeted fixes with links to evidence. No external data leaves the site.
• Online LLM option

When permitted, connect to online LLM services for at-scale analysis. Data-handling policies are configurable.

• APIs & automation
  Trigger captures, export artifacts, apply profiles, and fetch reports programmatically—enable workflows that react to detected resource-hungry processes/threads.

Typical workflows

• Reproduce a field issue → capture a focused session → generate a flame graph → apply an affinity profile → validate the fix.
• During a high-latency event → run a system call report → confirm dominant futex waits → apply a cpuset profile to reduce cross-core contention.
• Pre-release → run a scripted acceptance suite → check timing budgets → record headroom → export artifacts to a log server for audit.

Outcomes

• Faster isolation of CPU, memory, I/O, and scheduling bottlenecks.
• Verified timing budgets with measurable headroom before release gates.
• Repeatable tuning via versioned profiles and scripted captures.

Why this tool

• Reads system metrics with low overhead using standard kernel interfaces on Linux and Android.
• Combines deep analysis artifacts (flame graphs, system call reports) with safe, immediate tuning controls.
• Works offline; integrates with local/online LLMs; enforces safety via validation and audit logging.

Where this tool will be used

In engineering labs, validation environments, production systems, and field deployments running Linux or Android, by technical teams that need real-time system health monitoring, performance optimization, and AI-guided tuning—all with low overhead, auditability, and offline capability.

Request a live demonstration or a pilot on your target hardware. Our Sales & Marketing team will schedule a session and provide a focused plan aligned with your acceptance criteria and test environment.