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Nothing is 100% reliable. When designing application architecture, you have to assume there will be failures. Historically, this has meant deploying across racks, rooms and data centres to ensure that local switch, power and geographic incidents do not affect your entire infrastructure.
When deploying on the cloud, this translates to deploying across zones and regions. A zone is supposed to be isolated from other zones but close enough to allow low latency and low cost networking. However, since zones are close by, they are still susceptible to geographic events e.g. Storms. That’s why you deploy across multiple regions. For complete redundancy, provider diversity is the ultimate goal.
Zone and regional failures are bad but with proper design, you can handle them transparently to your users. This is why it’s a big deal when a cloud provider suffers a global outage, as Google did this week.
The April 2016 Google Cloud outage #
Google posted a very detailed post mortem of the 18 minute global networking outage on April 11 in which all Compute Engine IPs were withdrawn from routing. There were failures in multiple levels of “defence in depth” where a cascade of software bugs resulted in bad configs being rolled out.
The post mortem shows how, as with modern aircraft, it is never a single component failure but a combination of many complex factors. Our systems are now sufficiently complex that simple failure cases are rare. Redundancy is built in.
Although you might point out that there was a failure with testing and ask why nobody was monitoring the rollout stages after it was triggered, this post mortem demonstrates the systems in place that make up a modern, well engineered cloud provider.
Humans can’t be relied upon to monitor every change, especially if they’re happening regularly, so the real improvement to follow this outage is with the QA and test coverage for the automated systems.
It also highlights the excellent response time for their reliability engineers and how training was an important factor in reducing the length of the outage.
Historical Google Cloud outages #
This is not the first time Google has had a global outage. Indeed, in the past they happened disappointingly often.
- August 2015: A global networking outage caused by rolling out a software bug. A failure in testing and an implied global rollout without canaries.
- March 2015: A networking outage with variable impact across zones/regions causing packet loss. A failure in testing and a global rollout without canaries.
- September 2014: Rolling reboot of large numbers of GCE instances in all regions. Global rollout without canaries.
There have also been previous region level outages caused by similar circumstances, for example in October 2014 the Asia East region suffered a networking outage due to a bug in a networking upgrade procedure. And again more recently in February 2016.
This is important because Google claims:
Because each zone is an independent entity, zone failures do not affect other zones.
Is Google Cloud getting more reliable? #
From the latest post mortem, we can see that Google has added multiple layers of protection that were not present in previous outages. These include:
- Automation. Using tools to apply configuration changes makes it easy to do them consistently and repeatedly. You mitigate human errors such as typos or incorrect configurations.
- Verification. Google has software which not only generates the config but also verifies it. The problem is the most recent outage was that although the verification step discovered the problems, the rollout was not aborted due to bugs in the rollout process.
- Staged rollouts. The incident actually began several hours before the global impact was seen. Past outages have seen changes applied globally at the same time but now changes are rolled out gradually. Unfortunately this didn’t help because although alerts were generated, their investigation took longer than it took to complete the global rollout.
- Canaries. Rolling out changes to a small set of the entire infrastructure allows you to observe the impact in production. This step is absent from previous Google outages but is now a specific step. Unfortunately, although the canary deploy did show the problem, the rollout was not aborted due to a separate software bug.
The latest post mortem is the most detailed of any previous public root cause analysis. It goes into detail about the multiple layers of protection which the previous post-mortems do not, so it’s difficult to say whether these were just not reported or if they have since been added. However, since Google is a serious engineering organisation, you have to assume they have been working on improving things since the last global outage of August 2015 and at least some of the above improvements were introduced since then.
How reliable is Google Cloud? #
Google Compute Engine guarantees 99.95% uptime which equates to 4 hours and 23 minutes of downtime per year.
Historically, I have had concerns about Google Cloud’s reliability because as you can see from the above incident reports, the postmortems all look to have similar root causes: software rollouts without sufficient staging, canaries and testing.
However, anecdotally over the last ~12 months I have seen significant improvement in reliability from the workloads we run on App Engine, Compute Engine, BigQuery and Google Cloud Storage at Server Density. This is now backed up by the detail in the recent post-mortem explaining the layers of defence in depth.
Indeed, the post mortem write up has received generally good comments and only serves to improve confidence in the service. When you have technical buyers, they want to know the details of what went wrong because. Hiding behind a layer of secrecy does not help.
Although a global outage is never good, I am more confident in Google Cloud Platform than before this outage. And, contrary to the final statement at the end of the post mortem about it being unusually detailed, I hope they continue to publish full root cause analysis for the inevitable future outages!