| authors | state |
|---|---|
Robert Mustacchi <[email protected]> |
publish |
This RFD covers the background of what exists in the operating system for reliability and serviceability (RAS) and then goes on from there to cover what it might look like to extend this to software components such as Triton (formerly SmartDataCenter or SDC) and Manta. It also looks at hooking into existing RAS artifacts in the system such as the Fault Management Architecture (FMA) in illumos.
This RFD serves as an introduction and a high level direction that we would like to proceed down. Future RFDs will cover more specific component interfaces and explicit APIs. Currently this is focused on operators of Triton and Manta; however, there's no reason that it could not be extended to users in the future.
The following is the guiding principle for all of this work:
Triton and Manta operators need to be able to quickly assess the status of the entire system and of individual components. They also need to be notified with actionable messages whenever problems develop.
Before we get into too many details of what we need to build, we'd first like to go through and talk about how this might be used.
As an operator of Triton or Manta who is currently doing something else, I need to be notified when problems occur that affect service (e.g., a critical service has gone offline) or that compromise the system's tolerance to future failures (e.g., a disk fails and a hot spare is activated). These notifications should be specific and include the severity of the problem, a summary of what happened, what the known impact is, what automated response the system already took, and what the operator should do about it. For example:
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Severity: medium
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Summary: A Manta storage node has become unresponsive.
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Impact: Some in-flight data plane requests affecting objects on that node may have failed. Until the storage node is back in service, subsequent requests to fetch objects with durability 1 that are stored on this node will fail. Compute tasks that operate on such objects will also fail.
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Automated response: requests to write new objects will be directed away from this node. Requests to fetch or compute on objects stored on this node will be redirected to nodes with other copies, if any are available.
When the node becomes responsive again, the system will automatically start directing read, write, and compute requests to this node again.
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Suggested action: Determine why the system has become unresponsive. Likely reasons include: the system has been powered off or rebooted or the system has panicked. If the system has been powered off, power it back on. If the system has been rebooted or has panicked, wait for the reboot to complete. If you cannot reach the system's service processor, check for network problems.
As an operator of Triton or Manta, I have reason to believe the system is not working correctly. (This may be because I was just notified or because a user has reported a problem.) I want to walk up to the system and quickly assess the status of all components, highlighting those which are believed to be not working. If any components have been removed from service because they were deemed faulty, those should be highlighted as well. I should also be able to explicitly bring such components back into service if I determine that they're not faulty.
In the same situation as 'System Status', or after having finished restoring service after an incident, I want to review the actionable messages that were emitted by 'Actionable Notifications of Developing Problems' to fully understand what happened across the whole system.
Another way to phrase the previous three narratives is as the following points:
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Operator Notification of all knowledge article's emitted (mail, jabber, record, etc).
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List retired Components
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List degraded Components
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"Clear" retired components
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List all knowledge articles that have been emitted
Above, we have both a notion of degraded and retired components. It's worth taking some time to explain the differences. A retired component, is one that has been taken out of service. For example, a disk that has failed and removed from a zpool has been retired.
There are a few important points to emphasize about a device being retired:
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The system retires components
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Retired components do not return to service without explicit operator intervention
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Operators are required to notify the system that the retired component has been replaced.
In general, components that can be retired are hardware components and not software components. We generally retire things due to hardware faults and not software defects.
On the other hand, we have degraded components. A degraded component, whether software or hardware, can be diagnosed from both outside of the process and inside of it. For example, if a service cannot connect to a downstream component, it has the knowledge that it is degraded, even if it doesn't know the reason as to why it is in this state. For example, if NAPI cannot connect to moray, it doesn't know if that's because of a network partition, moray is down, or some other failure; however, it knows that its functionality is impaired.
Today FMA (Fault Management Architecture) provides information about hardware faults, software defects, diagnoses them, and automatically performs actions based on the generated events. For example, the operating system reports events called ereports which are facts. These include, but are not limited to, correctable ECC DRAM errors, ZFS checksum errors, and I/O errors.
The system processes these and determines if there is a hardware fault, generally based on SERD, and if so, then takes action such as off-lining a component, notifying administrators, and on-lining a new one, if appropriate. For example, if a disk has reached a fatal number of errors and FMA decides to off-line it, it will swap in a hot-spare, if available.
For each of these faults or defects, there's a corresponding knowledge article which informs the administrator what has happened, what the impact is, what actions have been taken, and what the operator's next steps are.
However, while this exists on a per-CN basis, Triton is entirely unaware of it. It has no integration into any facilities to inform operators, be collected, logged, etc.
In addition, at the moment there's no way to leverage the ideas of FMA inside of Triton. Components can't emit the equivalent of events like ereports that can be transformed by something into knowledge articles which detail information that an operator can use.
It's worth going into a bit more detail about what FMA consists of today and how they fit together:
We've mentioned events, it's worth going into more detail about they include. In FMA, events fall into the following categories:
- error reports (ereports)
- errors
- defects
- faults
- upsets
Each of these has a class, which is a dot delineated ASCII name and it has a payload, which has a structured body. The dot delineated scheme describes a hierarchical relationship. An event will have all of the payload properties of its parents. Each of these events cover different information.
faults indicate that something is broken. Generally, this only ever refers to hardware. Fixing a fault usually involves replacing it and telling the system that the fault has gone away. For example, a hard drive that needs to be replaced is faulty or has suffered from a fault.
defects indicate a design defect. Generally this only ever refers to software and firmware. For example, when a service enters maintenance, that usually indicates a bug in the software. It also covers things like the service processor.
upsets are used to indicate the cause of a soft error. This often is used to describe various transport related issues, for example, an I/O that gets retried.
errors are events that indicate some kind of signal or datum was incorrect. Generally they are referred to by error reports.
error reports are reports of errors that have occurred and often referred to as ereports. These are reports of errors and have additional report information inside of them to allow for components to better understand what's going on. These reports are generated by error detectors which may be the kernel or the hardware. For example, when a CPU generates an ECC error, the OS is notified and the kernel generates an error report.
Traditionally the payload has been defined in XML; however, we recommend that we move instead towards a JSON based schema to describe these in the future. We'll talk more about structuring this in the section 'Events Gate'.
The role of a diagnosis engine is to take a list of ereports and errors and transform them into something actionable. The purpose of the diagnosis engine is to take some set of ereports, and transform them into specific problems, or additional ereports. For example, there's a diagnosis engine that watches the rate of ECC errors that a CPU generates, and if it exceeds that rate, it then will generate additional events that will result in the CPU being faulted.
For every problem in the system that gets reported to the operator, (faults, defects, etc), there is a corresponding knowledge article that gets emitted. In many ways, these knowledge articles are the most important part of FMA for the operator. They tell an operator several facts:
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The severity of the issue.
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What the impact of the issue is.
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What action has been taken by the system, if any.
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What to do next.
The information contained within is designed to be used by both programs and software. In addition, various fields may be localized and therefore translated. Those that are localized are not used for programmatic consumption.
Importantly, a knowledge article also has a list of events that map to it. This is how the system knows what knowledge article to display for a given event.
The following are examples of the articles generated by the system:
The event registry represents a collection of events and knowledge articles. All of the events and knowledge articles consumed by a part of the system are contained inside a single registry. Today the registry is a series of XML files, though we'd like to change that longer term. In addition, while today there is a single registry that exists for the OS, there's no reason that various components couldn't have their own registries and therefore overlapping IDs and the like.
fmd is the fault management daemon. It's in charge of listening for
various reports, diagnosing them, and handles the logic around faulting
and marking devices repaired. The domain of the daemon is the single
compute node that it's on. It doesn't have a real sense of remote
systems. But it watches over both the global zones and non-global zones.
Earlier we talked about three different examples and phrased those as five different use cases. It's worth taking each of them apart and talking about them in more detail. Let's also take a little bit of time to describe the application workflow. There are basically three different ways that data transmits through the system, we'll letter these A, B, and C, while the use cases are numbered 1-5.
A) Software ------> Event -------> Knowledge Article
| ^
| |
| |
v |
Diagnosis -------> Event
Engine
B) Software -----> Event ------> Diagnosis ----> Event
Engine ||
||
vv
Component
Retired
C) Software ----------> event =====+
^ || Indicates
| || degraded
+------------> polled =====+
These three entries correspond to different flows. It's worth noting that B is a subset of A.
Looking back at our use cases, today FMA handles the logic of operations A, B, and use cases 2 and 4 within the context of a single system, not across the cloud.
Use case 3, on the other hand, listing degraded components, in Triton and Manta today. While the information can be both polled and pushed, today we poll it in the form of things such as sdc-healthcheck and madtom. While these may not be the final forms that we want things to take, they at least form the starting points.
If we now want to look at what we'd need to do, we'd want to create some new interface that helps provide a way for our new software to have these events noticed and emitted and have a new Triton component that is in charge of helping deal with these events and potentially perform diagnosis. That API would help facilitate the use cases of 1, 2, 4, and 5 across the cloud and interact with the local fmd as appropriate.
By integrating with the local fmd, we can provide operators a centralized view of the state of a given compute node's health. This will also allow the new Triton component to integrate with the current notion of cases and retirement that fmd has today.
We may also want to create a new fmd-like daemon which knows how to do simpler mappings of events to knowledge articles and tracking that which doesn't relate to the current fmd implementation, but leverages the same metadata. This would allow an Triton or Manta component to emit an event that can be noticed, transformed into a knowledge article, and alert an operator.
At this point in time we want to take a gradual approach and not start to go down doing software retirement due to defects and faults, but this allows us to still have components emit events that can be used to notify operators and have the needed documentation to help operators end up in one place.
At Sun, the set of events and the knowledge articles about them were stored in a single place. The gate itself was a morass of XML and tooling which relied on the sccs history. There are a couple things that we'd like to make sure are changed about this:
- Simplify adding of events and knowledge articles
- Allow there to be multiple repositories
To that end we have several goals. The biggest is getting tooling to make it easy to add non-conflicting events and to have this tooling live separate from the event repositories. This allows different event repositories, illumos, Triton, Manta, and anything else in the future to have different sets of events, but share the same tooling.
One of the challenges we have here is that the system has codes for every knowledge article that can be used to determine what they refer to. No two articles can have the same code. If they do, then that will cause a collision. Traditionally this was solved by just always using the next available ID and having the single repository of record be serialized. Unfortunately, this isn't always a desirable property for some repositories as it makes it harder for downstream forks to develop. There are many different considerations here. It's useful to consult the birthday problem table for a look at various probabilities so we can determine what makes sense here.
We still need to work out what the best scheme is here. Unfortunately, it seems like there may be some need for a project to coordinate at the end of the day; however, the strategy of improving the tooling will allow different repositories to co-exist.
We recommend that the tooling be such that event payloads are now defined using JSON schema and that knowledge articles are defined in a markdown-like format that allows variables from the payload to be inserted. The tooling should likely be set up in such a way as to randomly choose an ID and to properly initialize the markdown file.
While the operating system itself will likely only use a single repository, we'll want to make sure that whatever we're working with has the ability to use multiple event sources each with their own overlapping events and registries.
One of the other major things that we'd like to do is to talk about how we enhance the topology of the system. Today, most FMRIs are referring to specific chassis; however, there are other components in the data center that we could want to talk about or other logical aspects that we'd want to consider.
One of the things that operators would like to be able to do, and often Triton would like to better leverage, is to paint a picture of the data center so we know what's in what rack, where. This covers more than just compute nodes, but also other things in the data center that operations staff have to manage, such as switches. Ideally, we could leverage things like LLDP to put together a picture of how the data center itself is connected and allow operations staff to note various things like EOL time frames, leases, etc.
To that end, there are a bunch of different things that we should explore:
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Creating a new set of APIs that exposes the topology of the data center, whether it's a part of some existing API or something new can be hashed out later.
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Expanding the current chassis topology to have logical nodes for datalinks that can include information about their state and what they're connected to, if it's knowable. For this we can leverage lldp.
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Capturing information about service processors and associating that with their chassis, regardless if we want to provide numbers for them.
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Making sure that we have a concrete way of describing service topology in terms of FMRIs. This is really more of a mapping of SAPI than anything else.
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Better representing and gathering service processor information in the data center.
Many on-premise customers have many existing monitoring and alerting solutions that run the gamut from home-grown systems, systems built around tools like zabbix and nagions, and traditional use of SNMP.
As part of this, we should work with the JPC operations staff and with sevearl of our on-premise customers to determine the best way to provide this information in ways that can hook into their existing systems. This may be a simple as providing a means that these services can poll the general set of alerts; however, with others it may be more challenging because many of the events that we announce will be single alerts that fire, but do not come back up again. It also may not be practical as part of the broad set of existing systems to fit in all of the event payload.
The exact form of this integration will be the subject of a future RFD.
From here, we have several more specific directions that we can do on which will want to be the subject of future RFDs that further explore how we want to build these up and how this interacts with the rest of the system.
A lot of the things we've discussed have overlaps with existing APIs such as amon, cnapi, etc. At this time, this RFD isn't trying to suggest where something should or shouldn't be built. It's important that we co-exist well with amon's existing uses and make sure that this isn't a parallel world, but rather something we can move all of Triton and Manta to. The specific mechanics of that will be left to future RFDs and research.
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Capturing compute node and service processor topology
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Datalink FMA topology and RAS
This would also play into various other things we've had bugs on such as better handling Ethernet card replacements and handling things that rely on datalink state (such as sysinfo and provisioning).
- New Events Gate Tooling and Automation
Note that the following three points need to integrate in some form with amon.
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Existing FMA event centralization, notification, and fault database persistence
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Component knowledge articles and events
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Alert integration with existing Operator Systems
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Data center Topology, APIs, and Management
One thing that might be interesting is to rig up the status of various docker containers and SMF services to events and knowledge articles, giving customers a way that they could see these kinds of events for their architecture or even plug into it themselves, though that seems a bit more far reaching and less practical. Folks aren't generally going to rewrite their app to take advantage of our features.
Another aspect of the RAS services that we might want to explore in the future is the idea of threshold alerting based on various Cloud Analytics features or dragnet tables. A lot of what we might do here depends on how future developments of Cloud Analytics might develop.
This lists other RFDs that go into more details on what it is we'd like to accomplish:
| RFD |
|---|
| RFD 7 Datalink LLDP and State Tracking |
| RFD 8 Datalink Fault Management Topology |