What’s actually slowing this PC down?
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You cannot tell which process stopped a YouTube 24/7 stream from the symptom alone. Check the kernel journal for an out-of-memory (OOM) event, identify its named victim and PID, then correlate the timestamp with the systemd unit running the stream. If there is no matching OOM evidence, investigate stream and network logs instead of assuming OBS or FFmpeg was killed.
What can—and cannot—be identified without the VPS records
The title does not establish that RAM exhaustion caused the stream to stop, let alone name a victim process. The operating system, VPS provider, memory limit, streaming program, and incident logs are unknown. A kernel OOM record may name a process the kernel killed; systemd policy or systemd-oomd may also affect a service under memory pressure. A network interruption is another possible explanation.
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For a past incident, start with retained logs. Current files such as /proc/<pid>/oom_score describe a process that still exists; they may be unavailable after it exits. The kernel documentation explains OOM scoring and how the allowed memory pool matters, including when memory or swap limits apply: Linux kernel: Out Of Memory handling.
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Check for an OOM event at the time the stream stopped
- Set the incident window. Note the approximate time the YouTube stream went offline and the VPS time zone. Search a window around that time, not just the latest log entries.
- Read the kernel journal. On a system using systemd’s journal, inspect kernel messages in the window with
journalctl -k --since "2026-10-03 12:00" --until "2026-10-03 12:15", replacing the example dates and times with the incident window. Look for OOM-killer messages and any line that names a process and PID. The command is an example, not a claim that this VPS has those records. See the journalctl manual. - Record exactly what the log says. Preserve the timestamp, process name, PID, and surrounding OOM messages. The process named in a kill record is evidence of the victim; do not substitute the name of the streaming application unless the log identifies it.
- Check whether the records survived. Journal retention and persistence across a reboot depend on system configuration. If the relevant logs are absent, that absence does not prove that no OOM event occurred; it means the available records do not settle the question.
Correlate the victim with the streaming service
- Find the actual unit name. Use the systemd service name configured for the stream, rather than guessing that it is called
obsorffmpeg. - Inspect the unit’s journal for the same interval. For example,
journalctl -u your-stream.service --since "2026-10-03 12:00" --until "2026-10-03 12:15"filters by unit; replace the sample unit and times. The journalctl manual also documents filtering by structured fields, including unit and PID. - Check the unit state and policy. Review the unit’s status, configured
OOMPolicy=andRestart=, and messages about an OOM kill or restart. systemd documents thatOOMPolicy=affects what happens to the unit’s processes after an OOM kill; depending on policy, processes may remain, be stopped, or be killed as a group. A unit can enter an OOM-kill-failed state, after which its restart policy may apply. The actual result depends on the installed systemd version and configuration. See systemd.service. - Look for systemd-oomd evidence too. A service may be terminated under memory pressure by systemd-oomd rather than by the kernel OOM killer. Check available system and unit journal messages; do not treat the lack of a kernel victim line as proof that no memory-pressure policy was involved. The service documentation covers both mechanisms.
Check the VPS’s effective memory limit and trend
A VPS’s usable memory may be limited below the physical memory of the host. Check the limit that applies to the VPS or its service cgroup where configured; the host’s advertised RAM alone does not establish what the streaming process could use. Kernel OOM scoring and victim selection are interpreted in the context of the allowed memory pool, and oom_score_adj modifies a process’s score.
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For a recurring incident, compare memory observations leading up to the stop with the OOM timestamp and service behavior. Tools such as free, top, and vmstat can help show system activity, but a single snapshot taken after recovery cannot establish what happened beforehand. A gradual rise may be consistent with a leak or cache growth; it does not prove either. Interpret memory alongside CPU, disk, and network measures rather than treating one metric as a diagnosis. Microsoft’s guidance is for Azure VMs, so it is general diagnostic guidance, not evidence about this VPS: Troubleshoot performance bottlenecks in Linux VMs.
If no OOM evidence matches, investigate the stream connection
A live stream can disconnect without a process being killed. OBS’s connection guidance says dropped frames can indicate an unstable connection or an inability to sustain the configured bitrate; enough dropped frames may lead to disconnection. Check the OBS log or the streaming application’s own logs for dropped frames, disconnects, reconnect attempts, and their timestamps. Compare these with the kernel and service journals rather than using a connection symptom to dismiss a matching OOM record. See OBS’s Dropped Frames and Network Connection Issues guide, dated 2024-09-30.
How to report the result without overstating it
- Matching kernel OOM record: report the named victim and PID from the record, the timestamp, and whether it corresponds to the streaming service. A victim is not automatically the only process involved in the service stopping.
- Systemd or systemd-oomd evidence: state which policy or log message indicates the service was affected, and include the unit’s observed state and restart behavior.
- Connection evidence but no matching OOM record: describe the stream-side disconnect or dropped-frame evidence; do not say RAM exhaustion killed OBS or FFmpeg.
- Incomplete records: say the process remains unidentified because the relevant logs or incident measurements are unavailable. Do not infer a leak, a specific memory threshold, or a need to upgrade the VPS.
- Both kinds of evidence: report the sequence by timestamp. Memory pressure and connection trouble are not mutually exclusive.
A single OBS GitHub issue describes one user’s Ubuntu 20.04, OBS Studio 27.1.3 server setup with rising RAM and a reported OOM kill. It is an individual report involving plugins, browser sources, and an internal RTMP server—not evidence that OBS is the victim on another VPS: OBS Studio issue #5893.
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