eBPF Kernel Probes • OpenTelemetry MELT • Autonomous Self-Healing

AI-Based Telemetry for Zero-Downtime Infrastructure

Legacy SNMP polling and static threshold alerts drown NOC engineers in thousands of correlated false positives while missing silent grey failures. Nisarga’s AI Telemetry & Autonomous Observability Platform unifies sub-second eBPF kernel telemetry, OpenTelemetry (OTel) distributed traces, and Time-Series Foundation Models—compressing alert noise by 97%, pinpointing root-cause topology in seconds, and triggering closed-loop remediation before SLAs breach.

97.4%

Alert Noise Reduction

Graph-Causal Deduplication

< 1% CPU

Zero-Code eBPF Probes

Linux Kernel & K8s CNI

45 Min

Early Failure Horizon

Predictive SLO Burn-Rate

84% Lower

MTTR Resolution

Agentic Runbook Execution

Inspect eBPF + AIOps Architecture

LIVE eBPF + OTEL CAUSAL TELEMETRY RADAR

MTTR: 18s

MULTIVARIATE LATENCY SPIKE → TOPOLOGY RCA → AUTO-HEAL 1.4M SPANS/SEC

eBPF + AIOPS CORRELATION ENGINE ZERO HUMAN INTERVENTION

eBPF_PROBE: tcp_retransmit_skb & blk_mq_complete_request

RCA_VERDICT: NVMe SMART wear_level=98% → Pod Evicted

Beyond Static Threshold Monitoring

How AI Telemetry Transforms Infrastructure Operations

Static rules (CPU > 85%) fire either too late or constantly during normal batch workloads. Nisarga’s AI Telemetry engine learns dynamic seasonal baselines, correlates cross-layer dependencies, and acts autonomously.

PILLAR 01 • KERNEL-TO-CLOUD SENSING

Zero-Instrumentation eBPF & Unified OpenTelemetry (MELT)

Eliminates heavyweight proprietary agents that consume 10%–15% of production CPU. We deploy sandboxed Linux eBPF (Extended Berkeley Packet Filter) programs at the kernel, socket, and filesystem layers alongside OpenTelemetry (OTel) collectors.

Sub-Millisecond Kernel Visibility: Captures TCP retransmissions, DNS lookup stalls, block-storage I/O queue depth, syscall latency, and TLS handshakes without modifying application source code.
Hardware & DCIM Telemetry: Ingests bare-metal Redfish/IPMI out-of-band sensor streams, SMART disk wear metrics, SAN fabric FC switch counters, and GPU DCGM telemetry (NVLink errors, VRAM temperature, Xid faults).
W3C Trace Context Propagation: Links every frontend HTTP/gRPC request span directly to Kubernetes pod scheduling events, PostgreSQL/Oracle lock waits, and storage IOPS.

Cilium Tetragon / Beyla eBPF • OpenTelemetry • NVIDIA DCGM • Redfish IPMI

PILLAR 02 • NEURAL ANOMALY DETECTION

Unstructured Log Mining (Drain3) & Multivariate Time-Series AI

Converts terabytes of noisy syslog, systemd, Kubernetes, and firewall logs into structured templates in real time while modeling seasonal metric periodicity across thousands of co-varying infrastructure signals.

Streaming Log Template Tree (Drain3 + LogBERT): Automatically clusters millions of raw log lines into canonical event templates and flags rare semantic sequence transitions or unseen stack-trace signatures.
Seasonal-Trend & Foundation Forecasting: Applies STL Decomposition, Isolation Forests, and zero-shot time-series models (Chronos-T5 / TimesFM) to distinguish legitimate Monday-morning traffic surges from genuine memory leaks.
Silent Grey-Failure Detection: Identifies degraded nodes where health-check pings return HTTP 200 OK but downstream transaction throughput silently drops by 40%.

Drain3 Log Parser • LogBERT • Chronos-T5 •VAE Multivariate Reconstruction

PILLAR 03 • TOPOLOGICAL CAUSAL RCA

97% Alert Storm Suppression & Graph-Based Root-Cause Isolation

When a core Top-of-Rack switch or database primary degrades, 500+ downstream microservices fire simultaneous alerts. Our Graph Neural Network (GNN) and Causal Discovery engine collapses the entire storm into a single root-cause ticket.

Live Dependency Topology Graph: Continuously maps real-time service-to-service TCP flows, Kubernetes node-to-pod placement, VMware ESXi/KVM host-to-VM lineage, and SAN LUN attachments.
Granger Causality & PC-Algorithm Traversal: Traces temporal fault propagation backward along the dependency DAG to separate upstream root causes from downstream cascading symptoms.
Change-Event Correlation: Automatically cross-references anomalies against recent ArgoCD GitOps commits, Terraform state applies, firewall rule pushes, and kernel patch reboots.

Spatiotemporal GNN • Granger Causality DAG • GitOps Change Correlation

PILLAR 04 • AUTONOMOUS SELF-HEALING

Predictive Hardware Failure & Closed-Loop Agentic Remediation

Connected directly to the Tier-05 Cognitive Agent plane described on our AI-Based Analytics page, our platform forecasts exhaustion horizons and executes pre-approved self-healing runbooks.

Remaining Useful Life (RUL) Forecasting: Predicts NVMe/SSD wear-out, filesystem inode exhaustion, TLS certificate expiry, and memory fragmentation hours or days before an outage occurs.
Deterministic Runbook Automation: Triggers idempotent Ansible playbooks, Kubernetes Operators, or Argo Rollouts to drain degraded nodes, scale Horizontal Pod Autoscalers (KEDA), flush deadlocked DB connections, or revert faulty canary builds.
Human-Gated Blast-Radius Controls: Low-risk self-healing executes autonomously in milliseconds; high-impact failovers generate a one-click Slack/Teams approval card with full diagnostic evidence.

Ansible Runner • KEDA / Argo Rollouts • Policy-Gated SRE Copilot

Reference Observability Blueprint

Logical Architecture: Open-Source AI Telemetry & AIOps Stack

Engineered on high-performance open-source collectors and columnar storage—delivering petabyte-scale MELT (Metrics, Events, Logs, Traces, Profiles) retention without punitive per-host or per-gigabyte SaaS ingestion fees.

AIOps & TELEMETRY TOPOLOGY v4.8

Nisarga Sovereign eBPF, OpenTelemetry & Self-Healing Control Fabric

VictoriaMetrics + ClickHouse Cilium eBPF + OTel Zero Per-GB SaaS Tax

LAYER 01 • MONITORED ESTATE

Hybrid Infrastructure, K8s, DBs & GPU Clusters

Compute & Virt: RHEL/Ubuntu Linux, Windows Server, VMware vSphere, KVM/Proxmox
Cloud-Native: Kubernetes (EKS, AKS, OpenShift), Docker, Envoy Service Mesh
Data & AI Hardware: Oracle RAC, PostgreSQL, SAN/NAS, NVIDIA GPU Clusters

On-Premises Data Centers + Multi-Cloud VPCs

LAYER 02 • ZERO-CODE SENSORS

eBPF Kernel Probes & OpenTelemetry Collectors

eBPF Instrumentation: Cilium Hubble, Grafana Beyla, Pixie, Tetragon security probes
Unified MELT Agent: OpenTelemetry Collector + Vector.dev high-speed log router
Hardware & Network: Prometheus Exporters, gNMI/sFlow, Redfish IPMI, NVIDIA DCGM

< 1% CPU Overhead • Wire-Speed Packet & Syscall Capture

LAYER 03 • STREAM & TSDB STORAGE

High-Compression Columnar MELT Lakehouse

Ingestion Bus: Apache Kafka / Redpanda backpressure buffer & tail-based trace sampling
Metrics TSDB: VictoriaMetrics / Prometheus Mimir (10x ZSTD compression)
Logs, Traces & Profiles: ClickHouse + Grafana Loki + Tempo + Pyroscope

S3 Object Tiering • Sub-Second PromQL / LogQL / SQL

LAYER 04 • AI TELEMETRY ENGINE

Multivariate Anomaly, LogBERT & RUL Models

Time-Series AI: Chronos-T5 / TimesFM dynamic seasonal baseline bands
Log Intelligence: Drain3 online template mining + LogBERT embedding clustering
Predictive RUL: Weibull survival & LightGBM disk/memory exhaustion models

Zero Static Thresholds • Grey-Failure Detection

LAYER 05 • CAUSAL RCA & DEDUP

Topological Graph Correlation & SRE Agent

Alert Deduplication: KeepHQ / Temporal-Topological GNN grouping (97% noise cut)
Causal Discovery: DoWhy / Granger causality traversal over eBPF service map
SRE LLM Copilot: Private Qwen2.5 / Llama-3.3 summarizing incident blast radius

Pinpoints Exact Commit, Pod, Switch Port or Disk

LAYER 06 • AUTO-REMEDIATION

Self-Healing Runbooks & 24/7 NOC Glass

Autonomous Actuators: Ansible Automation, StackStorm, KEDA, Argo Rollouts
Unified Dashboards: Grafana SLO Burn-Rate & Topology Map + ITSM (ServiceNow/Jira)
Audit & Postmortem: Automated timeline reconstruction & SLA compliance ledger

99.99% Uptime SLA • 84% Faster MTTR

Observability Generation Comparison

Legacy Monitoring vs. Nisarga AI-Based Telemetry

How shifting from reactive SNMP/threshold polling to kernel-level eBPF and causal AI eliminates downtime and commercial SaaS overages.

Observability Dimension	Legacy Monitoring (Nagios / Zabbix / Static Thresholds)	Commercial Cloud APM SaaS	Nisarga AI Telemetry (eBPF + OTel + AIOps)
Data Collection & Agent Overhead	1–5 minute SNMP/ICMP polling intervals; misses sub-second micro-bursts and kernel stalls.	Proprietary language agents requiring code changes and consuming 5%–12% host CPU/RAM.	Zero-code Linux eBPF kernel probes + OpenTelemetry with <1% CPU overhead and sub-ms resolution.
Alerting & Noise Suppression	Static thresholds trigger massive alert storms (1,000+ emails/pages per switch or DB flap).	Basic tag-based grouping; still requires heavy manual rule tuning by SRE teams.	97.4% alert compression via Spatiotemporal Graph Neural Networks & Drain3 log clustering.
Root-Cause Analysis (RCA)	Manual war-room log grepping across siloed server, network, and DB consoles (hours of MTTR).	Shows correlated graphs, but leaves causal isolation to human operators.	Automated Granger-causal DAG traversal + GitOps change correlation pinpoints exact fault node in <20s.
Remediation & Self-Healing	100% manual human intervention after outage has already impacted end users.	Webhook notifications only; no native closed-loop infrastructure actuation.	Predictive RUL warning (-45 min) + policy-gated Ansible / Kubernetes Operator auto-remediation.
Data Sovereignty & Cost Model	Low license cost, but high operational toil and outage penalty costs.	Punitive per-host, per-GB log ingestion, and per-custom-metric overage bills; telemetry leaves VPC.	100% sovereign on-prem/VPC deployment (VictoriaMetrics + ClickHouse) at 70% lower TCO.