How To Monitor And Troubleshoot The Performance Bottleneck Of Japanese Cloud Server Mp4 Service

1.

overview and objectives

1) goal: locate the performance bottleneck of the mp4 on-demand/download service (http/https) on the tokyo node cloud server to ensure smooth playback and availability.
2) scope: including server (vps/cloud host), web server (nginx/apache), transcoding component (ffmpeg), disk io, network bandwidth, domain name/cdn and ddos protection, etc.
3) indicators: cpu, memory, iowait, disk throughput, network bandwidth utilization, number of active connections, 95/99th percentile response time, 5xx error rate, tcp retransmission rate.
4) requirements: provide repeatable monitoring commands, thresholds, real case data and configuration recommendations for quick troubleshooting and long-term prevention.
5) output: positioning steps, typical commands, sample tables and optimization suggestions to facilitate operation and maintenance/development collaborative processing.

2.

common performance bottlenecks and key indicators

1) cpu bottleneck: sustained high load (cpu usage >80% and high system load), affecting unpacking, transcoding and tls handshake.
2) memory/cache: insufficient memory leads to frequent swaps, resulting in delays and freezes; insufficient file cache affects disk reading.
3) disk io: high iowait or low iops (such as insufficient ssd iops or io latency >10ms) will slow down video segment reading.
4) network bandwidth and packet loss: egress bandwidth occupancy >70% or increased packet loss/retransmission will cause playback buffering; cross-border node delay fluctuations in japan require attention.
5) concurrency and connection limitations: insufficient configuration of nginx worker_connections/worker_processes or time_wait backlog leads to connection exhaustion.

3.

recommended monitoring tools and common commands

1) basic monitoring: top/htop (cpu, memory), vmstat (memory and paging), free -m.
2) disk and io: iostat -xm 1 3, iotop, sar -d (check iops, throughput, await).
3) network and connection: ss -s, ss -tanp, netstat -anp, iperf3 (bandwidth test), tcpdump -i eth0 port 80/443.
4) web and application layer: nginx -s status or stub_status, curl -w '%{time_starttransfer}', wrk/ab stress test.
5) media file detection: ffprobe file.mp4 (check frame rate/duration/codec), ffmpeg -i to check transcoding parameters and cpu usage.

4.

real case and server configuration example (tokyo node)

1) case background: a certain video-on-demand site experienced lag in user playback during peak node hours in tokyo, resulting in a large number of 5xx and delays.
2) server configuration (example) and observation data are as follows:
3) summary of troubleshooting steps: first, use top and iostat to confirm whether it is cpu or io; secondly, use ss/tcpdump to locate whether it is network packet loss; then check nginx stub_status and logs to locate concurrent hotspot urls; finally, use ffprobe to check whether the mp4 file has a large key frame interval that causes the first packet to be slow.
4) cause of the problem: in this case, the bottleneck is the superposition of disk i/o and tcp retransmission (cross-border link instability), which results in prolonged response time and accumulation of nginx connections.
5) result: upgrade to nvme higher iops disk + adjust tcp parameters + use japanese cdn, 5xx dropped to 0.6%, and average response time dropped by 50%.

5.

targeted optimization suggestions

1) nginx and system tuning: enable sendfile, tcp_nopush, tcp_nodelay; adjust worker_processes=auto, worker_connections to 8192; adjust net.core.somaxconn=65535, net.ipv4.tcp_tw_reuse=1.
2) disk and io: use high iops nvme or local ssd, turn on file caching, and reduce synchronous writes; if small files are frequently read and written, consider memory caching or redis/memcached.
3) network and cdn: cache static mp4 or hls clips on cdn nodes, giving priority to japanese nodes to reduce return-to-origin traffic; use geo-dns or anycast to accelerate.
4) transcoding and load: pre-transcode multiple bitrates (abr/hls) to avoid transcoding at runtime; use hardware acceleration (vaapi/nvenc) to reduce cpu when necessary.
5) ddos and security: enable cloud ddos protection/traffic cleaning, nginx speed limit (limit_conn/limit_req), fail2ban and waf protection for abnormal requests.

6.

alarm strategy and long-term monitoring practice

1) recommended thresholds: cpu 80%, alarm for 5 minutes; disk iowait >20%, alarm for 3 minutes; network egress utilization >70%, alarm.
2) connection and error rate: alarm for active connections >80% capacity; alarm for 5xx ratio >1%; alarm for tcp retransmission >50/s.
3) indicator collection: prometheus + node_exporter + nginx-vts-exporter, combined with the grafana dashboard to display the 95/99th percentile delay and bandwidth curve.
4) automated response: a sudden increase in traffic triggers an expansion script (calling the cloud api to expand the instance or adding a cdn cache strategy).
5) routine inspection: regularly run stress test (wrk/iperf3) and file integrity check (ffprobe), and save historical snapshots for capacity planning.