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MaGe Linux Operations
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MaGe Linux Operations

Founded in 2009, MaGe Education is a top Chinese high‑end IT training brand. Its graduates earn 12K+ RMB salaries, and the school has trained tens of thousands of students. It offers high‑pay courses in Linux cloud operations, Python full‑stack, automation, data analysis, AI, and Go high‑concurrency architecture. Thanks to quality courses and a solid reputation, it has talent partnerships with numerous internet firms.

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Recent Articles

Latest from MaGe Linux Operations

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MaGe Linux Operations
MaGe Linux Operations
Jul 26, 2026 · Operations

10 Best Practices for Reliable Declarative Jenkins Pipelines

This guide explains why declarative Jenkins pipelines fail—due to environment drift, workspace leftovers, capacity limits, credential exposure, uncontrolled concurrency, and lack of observability—and provides a step‑by‑step diagnostic workflow and ten concrete practices to harden Jenkinsfiles, enforce timeouts, manage agents, handle credentials, archive artifacts, and safely release and roll back applications.

AutomationCI/CDDeclarative
0 likes · 13 min read
10 Best Practices for Reliable Declarative Jenkins Pipelines
MaGe Linux Operations
MaGe Linux Operations
Jul 25, 2026 · Operations

How to Diagnose and Automate Cleanup for Server Disk‑Full Alerts

The guide explains why a full disk is more than just deleting a large file, walks through preserving evidence, checking capacity, inode and storage health, pinpointing the root cause on Linux hosts with systemd and Docker, and implementing safe automated cleanup with monitoring and rollback.

AutomationDisk ManagementDocker
0 likes · 22 min read
How to Diagnose and Automate Cleanup for Server Disk‑Full Alerts
MaGe Linux Operations
MaGe Linux Operations
Jul 23, 2026 · Operations

When Linux CPU spikes to 99%, these 6 commands saved me three times

If a Linux server shows 99% CPU usage, don’t restart immediately; instead use a systematic chain of six diagnostic commands—uptime, top, ps, pidstat, mpstat, and sar—combined with cgroup, thread, and log analysis to pinpoint the true cause, whether user‑space computation, I/O wait, soft‑interrupts, virtualization steal, or container limits, and then apply targeted remediation.

CPULinuxMonitoring
0 likes · 24 min read
When Linux CPU spikes to 99%, these 6 commands saved me three times
MaGe Linux Operations
MaGe Linux Operations
Jul 22, 2026 · Backend Development

How to Safely Distribute Traffic with Nginx upstream Load Balancing

This guide walks through verifying the Nginx environment, configuring a reliable upstream block, setting proper proxy headers and timeouts, validating backend health, handling failures, exposing observability, and performing controlled rollouts to ensure traffic is correctly balanced without service disruption.

LinuxNginxSystemd
0 likes · 28 min read
How to Safely Distribute Traffic with Nginx upstream Load Balancing
MaGe Linux Operations
MaGe Linux Operations
Jul 21, 2026 · Cloud Native

How to Use Kubernetes Node Affinity to Schedule Large Models on Specific GPU Nodes

This guide explains how to schedule large‑model inference pods onto GPU nodes that meet exact hardware requirements—such as A100 80 GB cards, specific node pools, and zones—by converting those needs into Kubernetes node‑affinity, taint, and topology constraints, verifying the deployment, monitoring its health, and safely rolling out or rolling back changes.

GPU SchedulingKubernetesNode Affinity
0 likes · 23 min read
How to Use Kubernetes Node Affinity to Schedule Large Models on Specific GPU Nodes
MaGe Linux Operations
MaGe Linux Operations
Jul 21, 2026 · Cloud Native

Auto‑Scaling LLM Inference with Kubernetes HPA Based on Request Queue Depth

The article explains how to replace CPU‑only autoscaling for large‑model inference services with a Kubernetes HPA that scales pods according to a custom queue‑depth metric exported to Prometheus, covering metric definition, deployment configuration, Prometheus‑Adapter setup, HPA creation, capacity calculation, validation, troubleshooting, and rollback procedures.

HPAKubernetesPrometheus
0 likes · 21 min read
Auto‑Scaling LLM Inference with Kubernetes HPA Based on Request Queue Depth
MaGe Linux Operations
MaGe Linux Operations
Jul 20, 2026 · Operations

How to Quickly Spot Anomalous Requests and Attack Sources Using Nginx Logs

This article presents a step‑by‑step Nginx log‑analysis workflow that helps operators identify slow requests, 5xx spikes, CC attacks, scanners and SQL‑injection attempts by parsing access_log and error_log fields, aggregating by IP, URL, UA and time windows, and then applying rate‑limiting, map‑based blocking, geo‑blocking and firewall rules to mitigate the threats while ensuring proper log rotation and verification.

Log AnalysisNginxPerformance
0 likes · 40 min read
How to Quickly Spot Anomalous Requests and Attack Sources Using Nginx Logs
MaGe Linux Operations
MaGe Linux Operations
Jul 19, 2026 · Operations

Hands‑On nvidia‑smi Guide: Diagnosing GPU Utilization and Memory Usage Anomalies

This article provides a step‑by‑step, Linux‑focused workflow for recording driver and GPU versions, interpreting utilization versus memory metrics, locating memory‑consuming processes, handling container and Kubernetes mappings, checking temperature, power, ECC, MIG, driver health, OOM conditions, and setting up reliable monitoring and alert thresholds for data‑center GPUs.

CUDAGPU monitoringKubernetes
0 likes · 28 min read
Hands‑On nvidia‑smi Guide: Diagnosing GPU Utilization and Memory Usage Anomalies