Managing Computers with Automation

Watch that basket!

The computing industry has lots of trends, numerous buzzwords, and a number of hot topics.  Sometimes these are in conflict with each other, or at least start out that way...  But, in the end, there are often good ways to harmonize all these various things.

Let's wander into virtual machine territory again today.  If you have gone to the trouble to create a bunch of virtual machines, the chances are you hope to do a little server consolidation - because when that's properly done it can save you some money.

This sounds good, and indeed has lots of good things going for it.  It's buzzword compliant, it's green, it saves you green (money).  What's not to like?

To see what you might not like if this is all you do, let's take an example to make it obvious...

If you put all your virtual machines on one physical server, then if that server fails, you lose all your virtual machines.  If you put ten virtual machines on one server, then the impact of that server crashing is roughly ten times as great as if a single server crashed.    If you work at it, you might be able to consolidate the ten most critical virtual machines onto a single server - and bring your entire data center to a halt with just one crash - bringing a suddenly much more personal meaning to the term "shock and awe"

This is not typically what people are looking for in their data center - and could easily be one of those career-limiting mistakes that you'd like to avoid - unless you already have your next job lined up.

This falls under the "putting all your eggs into one basket" way of doing business.  This part of a famous quote - but not the whole quote.  Mark Twain said "Put all your eggs in the one basket and --- WATCH THAT BASKET"[1].  So, to follow Mark Twain's advice, we need to not just put our eggs into one basket, we also need to watch that basket.

As most of you already know, watching servers and services is most commonly done by high-availability software - something like Linux-HA[2].  A properly configured HA system will watch the basket for you, and keep the worst from happening to your basket, your servers or your career.

As you can see, doing virtualization for reasons of consolidation doesn't make much sense unless you also add management software (HA software or otherwise) to watch your basket of virtual machines for you.

In the end, it's easy to see that all these things are connected - virtualization, server consolidation, power savings (green computing), availability management, and you want to manage them all.

[1] http://herbison.com/herbison/broken_eggs_watch.html
[2] http://linux-ha.org/

Virtual machine snapshots considered (nearly) worthless...

With apologies to Edgar Dijkstra...

Usually when people talk about virtual machine snapshotting, they include with it snapshotting both the server and any filesystems its directly connected to.  Although this is more complex than just snapshotting the virtual machine, it isn't that hard.

This works in some very narrow technical sense for some few cases, but it involves loss of data in every case.  If you take a checkpoint every 30 minutes (or every 5 or whatever), then all the updates made during that period of time, are lost when you restore this snapshot and its storage to a consistent (but old) state.  This means that all the checks you deposited during that time, or all the bonuses your boss put you in for during that time, or the books you ordered, or whatever, are lost.  Lost to the point that they probably have to be restored manually - to the tune of great customer dissatisfaction.

In addition, if this application has connections as a client, or as a server to other servers or clients, then although the application and its immediately mounted storage are now consistent, but unless you do simultaneous snapshots between this virtual machines and all the world it is connecting with (some of which may be outside your enterprise), and then restore your entire world to this older state, then there are likely to be many client/server connections which will no longer work - because the client and server are in mutually inconsistent states.

The worst case of this is if you have a Service Oriented Architecture, where any given server is only a small part of the overall service - every service has connections to something else all the time, and to make matters worse, the clients and/or servers are often outside your own enterprise.

And, of course, don't forget that you lost transactions in the process too.  So, a reboot interval of 1 to 3 minutes sounds really good by comparision.  Because all you'll lose in that case is transactions that were not yet committed - which are many fewer than the number of transactions lost by backing up to the previous checkpoint.

As an example of a common special case where this obviously doesn't work, imagine that the server in question is a file server.  So, you restore the virtual machine and all its storage (the file server) to some older state.  Now all the connected applications which _thought_ they had committed some particular piece of work (a spreadsheet, a database transaction) - just had all that work undone.  And, depending on the file server protocol and the application, bad things will happen - certainly loss of data, and probably some of the applications will create corrupt data - since updates they thought they'd made are now gone -  unbeknownst to them.  This corrupt data can cause any number of problems - inability to make further updates, cascading application crashes - these are all possibilities.

Or what if it's a client of a file server?  The file server is a separate machine (possibly virtual, possibly real, possibly an appliance).  Then you can't put its storage state back to a known state - without restoring all its clients back to the same consistent state - and if you somehow did, then _all_ of them now suffer data loss.

Not a very pretty picture.

There are some few cases where you can isolate the application from the "real world" and snapshot the whole "mini-enterprise" in a synchronous way.  Those are mostly limited to large scale scientific applications.  Given how hard it is to make them more available in any other way, this is a good thing.  But, its a practice with narrow applicability.  After reading the paragraphs above, perhaps you can see why...

A Complete Cluster Stack for Linux

Recently, I've had some folks ask me offline what exactly would a “complete” Linux cluster stack look like.  That's a good question, and this posting is intended to address that question.

So let's start with – what kind of cluster?  For the purposes of this posting, I'm primarily talking about a full-function high-availability enterprise-style cluster, not primarily a load balancing cluster, and not a high-performance scientific (Beowulf-style) cluster.

A few caveats before proceeding – much of what I'll reference below will be relative to the Linux-HA[1] framework, but the concepts are easily translated to any other clustering framework one might have in mind.

It's also worth noting that not every application, nor every configuration needs every component.   Adding unnecessary components adds complexity, and complexity is the enemy of reliability.

Many of the components (cluster filesystem, DLM) are primarily needed by cluster-aware applications.  Note that at this time (early 2008) very few applications are cluster-aware.

The Full Cluster Stack Exposed!

Below is a picture of the full cluster stack – which I'll describe in more detail later.  For the most part, the components higher up in the picture build on the components lower down in the picture.  To simplify the drawing, I didn't add all the who-uses-whom lines that one might want to make a detailed study of this subject.

 

Cluster Comm - Intracluster  communications

The most basic component any cluster needs is intracluster communications. There are a variety of different possibilities, but guaranteed packet delivery is a requirement.  Linux-HA has its own custom comm layer for doing this.  It's not perfect, but it works.  At one time in the past, we provided support for the AIS cluster APIs, and if you use OpenAIS today, then you can still have a reasonable cluster using Linux-HA and providing compatible support for the AIS protocols.  As will become clear, it's not a perfect configuration, but it's a reasonable one.  (Of course, like everything, it can always be improved even more)

Very large clusters (hundreds to tens of thousands of nodes) will likely require a different communication protocol, since most guaranteed delivery multicast protocols don't scale that high. 

Nevertheless, in an ideal world, all cluster components and cluster-aware applications would sit on top of the same set of communications protocols.

Membership – who's in the cluster?

Looking to the right of the cluster comm box on our architecture chart, you'll see the membership box.  The next basic function that a cluster has to provide is membership services.  Membership closely related to communication – since a simplistic view of membership is just who we can communicate with.  It is highly desirable that everyone in the cluster be able to communicate with everyone else. It's the job of the membership layer to provide this information to the cluster.

When your communication fails in weird ways, it's the job of the membership layer to present a view of the cluster that makes sense – in spite of the weird kinds of failures that might be going on.

If we eventually wind up with multiple kinds of communications methods, then we'll also have multiple ways of becoming a member.

Linux-HA (with or without OpenAIS) supports the AIS membership APIs.

Like I mentioned for communication, in an ideal world,  all cluster components would use exactly the same membership information.  However, it is important to note that the membership one uses must be computed using the communication method being used by the application.  So, unless every cluster-aware application uses both the common communication method and the common membership, it risks getting its membership out of sync with respect to its communication and other components using other communication methods.  In many cases, this can't be avoided.  Methods for coping with such discrepencies are discussed in more detail at the end of this post.

Fencing

Fencing is the ability to “disable” nodes not currently in our membership without their cooperation  Many of you will remember having discussed this in some detail in an earlier post[2]. As I explained in more detail there, fencing  is vital to ensure safe cluster operation

Our current implementation is STONITH[3]-based - STONITH == Shoot The Other Node In The Head

Quorum

Quorum is a topic we've talked about extensively in a couple of earlier posts [2] [4]. Quorum encapsulates the ability to determine whether the cluster can continue to operate safely or not

Quorum is tied closely to both fencing and membership.  In practice, as we discussed before, it is often highly desirable to implement multiple types of quorum.  Linux-HA currently provides multiple implementations and can provide more through plugins.  Like membership and communication, it is desirable for all cluster components to use the same quorum mechanism.  All the interesting and legal ways that quorum can interact with fencing and membership and the communication layer are too detailed for this posting.

Cluster Filesystems

Cluster filesystems allow multiple machines to sanely mount the same filesystem at the same time.  This is a great boon to parallel applications.  Cluster filesystems typically don't use the network or another server involved when doing bulk I/O. Each node mounting the filesystem is normally expected to have access to the data.  This typically requires a SAN.

Typically, this is done to improve performance, but convenience and manageability are common secondary goals  Cluster filesystems are related to, but distinct from, network filesystems like NFS and CIFS. On Linux, there are several cluster filesystems available including

• Red Hat's GFS

• Oracle's OCFS2

• IBM's GPFS

• etc.

Normally, when they're being used for performance reasons, cluster-aware applications are required.  You can't typically just run 'n' copies of your favorite cluster-ignorant application and have it work.  The filesystem won't scramble the data, but your application typically will.  It goes without saying that high-performance cluster filesystems run in the kernel – unlike all the other items we've talked about before.  Because of the high-performance, it's common for a cluster filesystem to have its own communication and membership code – not using the typically userland communications and membership code.  Since membership isn't high bandwidth or really low latency information, it is possible to feed membership from a user-space membership layer into the kernel.  Of course, then the membership and the communications layer are out of sync.  It is arguable whether this is an improvement or not.

Cluster filesystems typically need cluster lock managers – described in the next section.

DLM - Distributed Lock Manager

A DLM (Distributed Lock Manager) provides locking services across the cluster, and it's an interesting piece of code to implement them – particularly the error recovery. 

To some degree, DLMs are analogous to System V semaphores but – cluster-aware.  In addition, they provide much more sophisticated API and semantics.  Although DLM APIs are fairly well understood, there is no formal standard, so switching from one to another can be annoying.  Red Hat has a reasonable kernel-based DLM which they use with GFS.  DLMs commonly have their own separate communications and membership code.  The comments about getting membership from user-space and having them be potentially different from cluster filesytems also apply here.

Cluster Volume Managers

You might think that you really don't need a cluster-aware volume manager.  Sometimes you might be right.  More often, if you thought that, you'd be wrong.   A cluster volume manager is just like a regular volume manager – only cluster aware.  This is to keep different nodes from getting inconsistent views of the layout of a set of disks or volumes.   The current cluster-aware volume managers are EVMS and CLVM.  Only CLVM is expected to survive into the long term.

The big challenges for cluster volume managers are high-performance mirroring and snapshots.  These operations are potentially very difficult to implement right and fast.  Cluster-aware volume managers often have both kernel and user-space components.  The membership inconsistency issues here are similar to those for cluster filesystems and the DLM.

CRM - Cluster Resource Manager

Every HA cluster has something like a CRM, but they may divide up these functions differently.  Our CRM is a policy-based decision maker for what should run where – handling failed services and failed cluster nodes.

The CRM is similar to UNIX/Linux startup init scripts – it starts everything up – but across a cluster following some policies, and managing failures.

The Linux-HA CRM is arguably the best cluster resource manager around today – at least in terms of flexibility and power.  It has usability issues, and can be extended, but those are solvable.

The Linux-HA CRM function is largely divided between the PE and TE – which are described below.

PE - Policy Engine

The Policy Engine is a key component of the CRM and does two distinct things.

• It determines what should run where (cluster layout)

• It creates a graph of actions of how to get from the current state of affairs to the new desired state

This graph of actions is then given to the TE (described below).

The system would have more flexibility if he PE were split into two parts for these two functions, and supported plugins for the cluster layout function. 

It currently isn't aware of resource cost, nor of absolute resource limits and load balancing considerations, which complicate optimal placement.   Those would be good things to add to it in the future.  Having plugins for doing resource placement would also be a highly useful and desirable thing.

TE - Transition Engine

Receives a graph of actions to perform from the policy engine, then uses the LRM proxy to communicate with the LRMs to carry out the actions

Its main jobs are action sequencing, error detection and reporting

CIB - Cluster Information Base

The CIB manages information on cluster configuration and current status.  The cluster configuration includes the configuration and policies as defined by the system administrator.

Its key difficulty is to keep a consistent copy replicated across the cluster, resolving potential version differences.

All the data it manages is XML, and the CIB has a minimal knowledge of the structure of this XML.

LRM - Local Resource Manager

In the Linux-HA architecture, a local resource manager runs on every machine and carries out the tasks given to it. Everything that gets done gets carried out by the LRM.  Examples are:

• start this resource

• stop this resource

• monitor this resource

• migrate this resource

• etc.

The LRM provides interface matching to the various kinds of resources through Resource Agents.  The Linux-HA LRM supports several classes of Resource Agents.

The LRM is not at all cluster-aware.  It can support an arbitrary number of clients, one of which is the LRM communications proxy (below).

LRM Communications Proxy

The LRM proxy communicates between the CRM and the LRMs on all the various machines.  This function is currently built into the CRM.  This architectural decision was based on expedience more than anything else.

To support larger clusters this needs to be separated out, made more scalable, and more flexible.  This would allow a large number of LRMs to be supported by a small number of LRM proxies.   In large systems, this would probably use the ClusterIP capability to provide load distribution (leveling) across multiple LRM proxies.

Init - Initialization and recovery

This code does really three things:

• Sequences the startup of the cluster components

• Recovers from component failures (restart or reboot)

• Sequences the shutdown of all the various cluster components

This is currently provided by Linux-HA and bundled with the Linux-HA communications code.  This likely needs to be separated out to a separate proxy function (process) in the future.

Infrastructure

The Linux-HA infrastructure libraries (“clplumbing”) does a wide variety of things.  A few samples include:

• Inter-Process Communication

• Process management

• Event management and scheduling

• Many other miscellaneous functions

Surprisingly, these libraries amount to about 20K lines of code.

Quorum Daemon

The quorum daemon is an unusual daemon, because it's the only daemon we have that's intended to run outside the cluster proper.  It is instrumental in solving certain knotty quorum problems – especially for:

• 2-node clusters (very common)

• Split-site (disaster recovery) clusters

This was discussed extensively in previous postings [4] and [5].

Management Daemon

Provides Complete Authenticated Configuration and Status API.  This includes both information contained in the CIB, and also information about the communications configuration and so on.

The management daemon is used by the:

• GUI

• SNMP agent

• CIM agent

Clients are authenticated using PAM, and all communications is via SSL, so its clients can safely be outside cluster, or even outside a firewall.  This daemon should provide different levels of authorization depending on the authenticated user, and should log its actions in a format suitable for Sarbanes-Oxley (SOX) auditing purposes.

GUI

Provides a Graphical User Interface providing configuration and status information.  It also supports creating and configuring the cluster.  Note that at the present time, there are a number of useful cluster configurations it cannot create.

CIM and SNMP agents

The CIM and SNMP agents provide CIM and SNMP management interfaces for systems management tools.  The CIM interface supports status updates and configuration changes, whereas the SNMP interfaces only report status.

Disadvantages of this architecture

For a variety of reasons, kernel space doesn't have access to user-space cluster communications or membership.

As a result, both the DLM and most cluster filesytems implements their own membership and communications.

This is in contradiction to the “ideal world” statements earlier.  This can result in some odd cases where one communication method is working in a particular case, but another method is not.  This results in differences in membership – which can have bad effects.

Why this might not be quite as bad as it seems

One reason why one might not worry about this as much as one might, is because it's a problem which one can't make go away.  A cluster system will always have to interface with software packages which do their own communication, and compute their own membership for a variety of usually good reasons.  As a result, this is a problem which we can't make go away.  Instead we have to deal with it effectively.  There are basically two cases to consider:

1. The “Main” membership thinks that node X should not be in the cluster, whereas the “Other” membership thinks it should be.

2. The “Other” membership thinks that node X should not be in the cluster, whereas the “Main” membership thinks it should be.

Let's take these two cases one at a time:

Case 1:

If the main membership thinks node X is not in the cluster, then it will simply not start any resources on node X.  This takes care of the problem.

Case 2:

If the “Other” membership discovers that a particular node should be dropped from its view of membership, and it can inform the CRM not to start its resources on that machine, then the local view of this membership from the perspective of the resources it deals with is effectively made to exclude these Other-errant nodes.  In the Linux-HA CRM this is easily done having the Other-resources write node attributes to cause those nodes to be excluded, and the rules would then be written to exclude those nodes from consideration for running Other-related resources.

Although Case 2 isn't pretty, it works, and no amount of wishing and hoping is likely to ever make this kind of problem go away in the general case - particularly when one involves proprietary applications  So, even if there is some membership discrepancy, it can is always possible to manage it appropriately assuming you can get a tiny bit of cooperation from the application.

References

[1] http://linux-ha.org/
[2] http://techthoughts.typepad.com/managing_computers/2007/10/split-brain-quo.html
[3] http://linux-ha.org/STONITH
[4] http://techthoughts.typepad.com/managing_computers/2007/10/more-about-quor.html
[5] http://techthoughts.typepad.com/managing_computers/2007/11/quorum-server-i.html

How Managed Virtualization (including HA) conflicts with System Management

Managed Virtualization Versus System Management

In an earlier post[1], I talked about a couple of kinds of virtualization, comparing two of them and highlighting their strengths.  This posting discusses how virtualization can confuse and confound conventional systems management - both automated and manual, and gives some thoughts on how to deal with it.

We all know that virtualization is a GoodThing(TM).  Therefore, it can't really have any disadvantages, can it?  <tongue-in-cheek-off> Unfortunately, it does have disadvantages.  The great strength of virtualization is its ability to break the ties between a service or operating system and the server which implements its service.  Many software systems and a good number of human beings find this confusing.  If I want to reboot a physical server, what services or operating systems will be disrupted by the reboot?

Conversely, if I want to do something to the machine that's running a particular service, which machine do I have to log into?  If you're running both service virtualization (conventional HA like Linux-HA[2]) on top of server virtualization (ala Xen or VMware), then you have a doubly difficult task - first you have to figure out which virtual machine is running a service, then you have to figure out which physical machine is running that particular virtual machine.

This can be really annoying and can easily result in system administrators[3] making mistakes either in the middle of the night, or when under pressure (which all sysadmins know is pretty much all the time).

Remember - Complexity is the Enemy of Reliability.   This is just another example of my favorite phrase at work.

And, if you want to have server monitoring software which tries to figure out whether a service is stopped and have it restart it, then it can also get confused by the fact that all these stupid servers and services are always moving around.  They just won't stay put!  Back in the olden days, you logged into a server and you edited the inittab, and you always knew what hardware it was running on and what server it was.  Now, with virtualization, and especially with virtualization management software, you never know what's where.

A Recipe for Chaos and Conflict

Your HA software and/or your virtualization management software can move things around on you.  Imagine that you have these four kinds of things in your data center:

• High-Availability (HA/service-virtualization) management software

• Virtualization management software

• System management monitoring software

• Human system administrators

This is a recipe for chaos, interspersed with the occasional career-limiting disaster. It's this kind of thing that leads system administrators to pull their hair out, and keep their resumes up to date.  None of these is bad by itself, in fact, each is a GoodThing(TM).  But they don't normally play well with each other. In typical myopic software design fashion, each of these layers is usually unaware of the other layers (except, of course for the last (human) layer - who has to make up for all the poor integration).

In addition, since the software layers typically aren't aware of all this wonderful virtualization going on, they can't really deal with the picture reliably.  They don't know what should be happening where, because it isn't fixed.  The various virtualization management packages keep changing things!

So, what's a body to do?  As far as I know, there are two basic options.

1. Integrate the four layers of management with each other using things like CIM[4] and SNMP[5]

2. Empower your HA software to also manage the server virtualization of your data center

Integration of Layers

Virtually every data center (sadly, pun intended) has a variety of server types and a variety of operating systems, and a variety of management software.  They mostly don't play well with each other.  Almost the only way to get them to play together - even if imperfectly - is to have them talk together using industry standard protocols.

Today, that means using SNMP or CIM.   Here is my personal view on the characteristics of these two protocols for your consideration.

• SNMP - widely deployed - implemented in a truly compatible way, but far too weak for a job this hard.  SNMP is great for grabbing statistics, checking whether a server or router is up and what kind of load it is seeing in great detail.  Anything much beyond this, and the MIBs become 100% vendor-specific - meaning that cross-vendor integration breaks down - basically completely.  For HA clustering or virtualization management or worse yet the combination of the two - forget it.

• CIM - widely deployed in expensive disk subsystems - but rarely deployed outside that.  It has newly developed models for virtualization and clustering, but like most standards they're mostly lowest-common-denominator standards, and unfortunately not widely deployed.  For example, Linux-HA[2] implements CIM, but unfortunately Linux-HA has tremendous power and capability which CIM can't begin to model.  So, this winds up being only possible to model using vendor-specific extensions - greatly weakening the possible integrations.

Now, I'm not saying that these two protocols are useless - far from it. Without open standards like CIM and SNMP, the prospect truly is hopeless.   But I am saying that integrating them in the typical-for-the-industry highly-heterogeneous data center is a challenge, and the more layers there are to integrate, the bigger the challenge.  Since standards necessarily trail industry practice, the more "bleeding edge" the topic (i.e., HA clustering or virtualization) and the more powerful the underlying tool (like Linux-HA), the greater the mismatch.

Here we have two bleeding edge topics and four layers.  Yikes!  Surely there must be some kind of alternative to this somewhat-unattractive mess.

Decrease The Layers and Let Them Manage Themselves

As I mentioned in my earlier virtualization posting, some HA packages (like Linux-HA) can also manage virtualization simultaneously.  So, one way of dealing with this is to let (or extend) your service virtualization product also manage your server virtualization.  One advantage of this approach is that service virtualization software (HA software) is comparatively mature technology, minimizing the risk.

Unfortunately, this doesn't yet go all the way in solving the problem either.  There are a few things that should change to make this really work well. These include

• Support much larger HA clusters - hundreds to thousands of nodes.  In an ideal world, you'd really like fewer of these HA/virtualization clusters as you can get.  Today you'd typically have to have one of these clusters for every 8-32 physical servers - which makes an awfully lot of these things to manage in a data center containing hundreds or thousands of servers.

• Integrate with many virtualization layers - Such a product would need to integrate with Xen, IBM System Z, IBM System P, Linux KVM, VMware, and future virtualization layers like the one promised by Microsoft.   This isn't rocket science, but by the time you're done, it will be some work.

• Support monitoring and controlling services inside the virtual machine - Otherwise you haven't really integrated the two layers - and you wind up running some HA software inside some of the virtual machines.  Again, this isn't rocket science, but it will require some work[1] for each operating system you want to manage services for.

• Integrate with provisioning systems - so that you can add and delete virtual machines and allocate disk to them and their applications with fewer possibilities for error, and more automation.

None of these items are technically difficult, and none of them are prohibitively expensive to implement.  Given that I'm the project leader for Linux-HA, and Linux-HA is one of the most capable HA products around, you might imagine that some of these thoughts are on my mind for our future  ;-).  Of course, that doesn't eliminate the necessity for integration with the remaining layers above, which is why Linux-HA implements both CIM and SNMP.  This allows the virtualization management infrastructure to actively and autonomically manage  servers and services, while letting it bubble up events (especially those it can't automatically recover from) to the management consoles and humans via protocols like SNMP and/or CIM.

Conclusions

Virtualization technologies add complexity to the data center along with the benefits they bring, and in the process may render the existing management facilities less than useful.  However, if HA and Virtualization management are performed by a single entity, and open standards like CIM and SNMP are used, systems can be active the problems can be minimized.

See Also

Preparing for Virtual Management http://www.itbusinessedge.com/blogs/dcc/?p=276

References

[1] http://techthoughts.typepad.com/managing_computers/2007/09/virtualization-.html
[2] http://linux-ha.org/
[3] http://linux-ha.org/SysAdmin
[4] http://www.dmtf.org/standards/cim/
[4] http://en.wikipedia.org/wiki/Common_Information_Model_%28computing%29
[5] http://en.wikipedia.org/wiki/Simple_Network_Management_Protocol

A brief overview of load balancing techniques

Something that people commonly do which involves a form of automation is load balancing.  Load balancing is the idea that incoming network requests are distributed across a set of servers which then each provide the same service.  If you spread the load across "n" servers, then in an ideal world what you get is "n" times the throughput.  And, since you have redundant servers, with the right kind of automation software, you can also get a degree of high-availability.   This is way cool!  This article will talk about load balancing as a general technique, and specifically about ways to do it on Linux using free or open source software.  In particular we'll talk about the Linux Virtual Server project[1], (LVS, ipvs) and the Cluster IP[2] as load balancing techniques.

Meanwhile back in the real world, we see some slight differences from this ideal view of things.  We see that load balancers often introduce single points of failure, and that that the load balancer or some kind of back end servers typically introduce scalability limitations.  To really understand these  problems, we need to look at specific load balancing techniques in a little more detail.  Please understand that I'm not an in-depth expert on any of these techniques, but I do have basic familiarity with the methods described here.

Linux Virtual Server
The first technique we'll cover is the Linux Virtual Server[1] (LVS) - which is implemented by the ipvs kernel module.  Much of what I have to say about LVS also applies to the most load balancers - hardware or software, since they typically work roughly the same way as LVS.

I usually describe  LVS clusters as being similar to a baseball[3] diamond - with the load balancer on third base, web (or other) "real servers" stretched from home plate to second base, and the back end database on first base.  In this image, requests flow from the left to right starting from the users in the dugout to the left of second base foul line,  and responses flow from right to left from the database or file server on first base back to the users in the dugout.  [This imagery works great when talking to Americans or Japanese on the phone, but often fails for people from other cultures].

The first thing to notice is that the only inherently scalable portion of this arrangement is the web servers in the middle.  The load balancer (on third base) and the database server (on first base) are each potentially performance bottlenecks and potentially single points of failure.

If you make each of them redundant to eliminate single points of failure, the picture looks something like this:

There are a number of variations on this basic theme:

• Failover vs load sharing load balancers

• Different applications on the "real servers" instead of WAS / Web servers.

• Different routing techniques for the load balancer

• Different data sources instead of a DB2 database

In the end, however, they look a lot the same, and work very similarly.

In a NAT[5] arrangement, both incoming and outgoing packets flow through the LVS director.  In a direct routing arrangement, only incoming packets flow through the LVS director, and outgoing packets bypass the director, and go directly to the clients.

LVS monitoring
Although you could set this all up by hand and start all the services by hand, if anything failed, then you'd have to reconfigure things by hand.  Since the theme of this blog is automation, obviously, the right answer is to automate this setup and reconfiguration on failure.   A common way to do this is to use the Linux-HA software[6], which includes the LVS tool ldirectord[4]. Ldirectord will look at your real servers and see if they and the services they're running are operating correctly.  It will then take corrective action if it sees problems.  The Linux-HA software will watch the directors (sitting on third base), and fail things over and back if problems come up, to eliminate the single point of failure on third base.  As of now, the most common configurations of real servers have them be part of an LVS cluster, but not part of a Linux-HA cluster.  For historical reasons, the load balancers (directors) on third base are in one cluster, and the database server(s) on first base are commonly in separate clusters.  However, with release 2.x versions of Linux-HA it is perfectly sensible to include the both in the same cluster, perhaps in an n+1 sparing arrangement.  If you have fewer than 10-12 real servers, then it might also make sense to let Linux-HA manage those real servers as well.  The reason for the upper limit is to ensure that the total cluster isn't larger than the current Linux-HA limitations on cluster size (approximately 16 nodes).  Another  possible configuration is to use Linux-HA to monitor your real servers.  This would involve writing a clone resource agent for configuring LVS to point at the various real servers.  This might result in a more scalable monitoring arrangement than the current ldirectord monitoring arrangement, since the monitoring is done on each real server, and only errors are reported back to Linux-HA. 

This is a very brief overview of LVS, which perhaps we can expand on in a future posting.  For a thorough treatment of LVS,  I recommend The Linux Enterprise Cluster[7] by Karl Kopper.

Performance characteristics
Clearly every inbound packet has to go through the load balancer (director) - so it has to receive, look at, and forward each inbound packet.  It may also have to rewrite headers and recompute checksums on each packet.  If it configured with NAT, then it also has to read and rewrite all outbound packets as well. In addition, with ldirectord and similar software, the director also has the job of monitoring the all the real server processes on all the real servers.  Eventually, this node (or these nodes) will become a bottleneck.  When this happens depends on the nature of the workload, the complexity of monitoring, and the director configuration chosen.

Cluster IP
Although LVS doesn't require a master's degree to configure, some features of it do have a reasonably steep learning curve.  For a very easy-to-configure, albeit less scalable load distribution method on Linux, you might consider using ClusterIP addresses[2].

What is a Cluster IP?
The unique feature of a cluster IP is that it has no load balancer, hence no single point of failure.  Wow! That seems weird!  What does the picture look like?  If you move the users out of the dugout onto third base, you'll get the basic idea.  But that picture brings lots of questions to mind - like how do packets get routed?

The answer is simple - each machine in the cluster has the same IP address.  Say what?  The same IP address? Yes.  I mean the same IP address.   How can this work?  This sounds like it flies in the face of usual teaching about networking.  Which it does.

Enter the Multicast MAC address
The trick to making this work is to have each machine have an ARP table entry with the same MAC address in it - a multicast MAC address.  So when an ARP request is given, all nodes in the cluster respond, but they all give the same answer"I have IP address XXX with MAC address YYY".  So, in effect, there is no confusion - because it doesn't matter which ARP reply is listened to, they all say the same thing.  Therefore at the IP level everyone is happy.

So far, this is a reasonably satisfying answer, but not quite omplete.  What about addressing at the MAC level, and at the TCP or UDP level?

At the MAC level, multicast MAC addresses are recognized by switches, and is routed to all the switch ports, since everyone has presented that MAC address as "theirs". So, it copies all the packets to all the servers.

What happens at the TCP or UDP level?
This is where things get a little more interesting.  Now, it's more obvious how each machine gets the packets - because every machine gets them.  But, now what?  We clearly don't want every machine to respond to a given TCP packet. That would totally confuse everything, as would giving every packet to all the applications.  To solve this problem, Linux has added a hashing feature which allows the source address, source and destination port number to be used in a hashing function to allow it to decide which machine will respond to any given request.  So, if you have three hash buckets and three servers, the packet header information (source IP and port numbers) can be hashed into three buckets with one bucket assigned to each server.   If the packet hashes to the hash bucket assigned to this server, then it is kept, and passed along to the UDP or TCP layers.  If it doesn't hash to the bucket assigned to this server, then it's just dropped (ignored).

So, this hashing method determines which host serves the requests.  Although the ethernet driver in every machine sees each packet, each packet is only processed by one machine each.  Now you know how it works.

It also turns out to be very easy to configure using Linux-HA, as you can see on our ClusterIP web page[8].  In the process, Linux-HA also handles all the redundancy and failover of cluster IP buckets for you automatically.  Very cool indeed.

If you only configure one bucket per node, then when a node fails, all of its traffic has to get assigned to one machine.  If you start out with 3 nodes in your ClusterIP group, and one node dies, then that means that one node gets all the additional traffic - effectively doubling its workload.  So, a better idea for "n" nodes, to have n*(n-1) cluster IP buckets.  That way when any given machine fails, its workload is split evenly across the remaining nodes.  In Linux-HA terminology, the ClusterIP address is called a clone resource, and what you want is to configure clone_max to n*(n-1)and clone_node_max also to n*(n-1).  Although clone_node_max probably doesn't have to be this large, it would allow a single node to handle all the traffic, if a sufficient number of ClusterIP peers die.

Performance characteristics
Every node in the cluster will see all incoming IP packets.  As I understand it, many/all switches will also send every packet to every switch port in the subnet (or vlan).  This argues for a small subnet for this function.  But, the packets are discarded at a very early stage - minimizing the overhead on the host.  Outbound packets are not affected by this arrangement.  This kind of arrangement works well for these kinds of cases:

• long processing time per packet (complex J2EE applications, for example)

• small incoming packets with large outgoing packets

• smaller number of processing nodes
  

It probably works less well with the opposite kinds of configurations:

• high number of incoming packets with trivial processing per pacekt

• large incoming packets (uploading DVD images, for example)

• large number of processing nodes

Note that in this case, since there is no head-end processor like an LVS director that can be a single point of failure, so no special provisions are needed for high-availability when used with Linux-HA.  It is typically not as scalable as LVS load balancer, but it is trivial to set up and use.

[1] http://www.linuxvirtualserver.org/
[2] http://flaviostechnotalk.com/wordpress/index.php/2005/06/12/loadbalancer-less-clusters-on-linux/
[3] http://en.wikipedia.org/wiki/Baseball
[4] http://www.vergenet.net/linux/ldirectord/
[5] http://en.wikipedia.org/wiki/Network_address_translation
[6] http://linux-ha.org/
[7] http://www.nostarch.com/frameset.php?startat=cluster
[8] http://www.linux-ha.org/ClusterIP

Alan eats his own cl_respawn dog food. Yum!!

In this posting, I show how to use cl_respawn[1] to monitor my system logging and help keep it running, and along the way, I improved cl_respawn a little as well.  In addition, I explain why I couldn't just use the respawn directive in /etc/inittab[5] (and why you probably can't either).   I first talked about cl_respawn in one of my first blog posts[6].

The problem

When we run our automated CTS[2] tests for Linux-HA[3] we rely on the guaranteed log entry delivery provided by syslog-ng[4].  Basically, we redirect all our logs in a test cluster to a test overseer machine, and then CTS watches this consolidated log for errors and correct behavior.

This is a nice system and it works pretty well, but it relies on the reliability of syslog-ng.  For the most part, that's just fine.  But, sometimes syslog-ng just stops running.  Then the tests show that Heartbeat has failed, but it's really just syslog-ng that's crashed on me.  So, in the past I added some code to CTS to make it test the logging after every error, and then hit the machines over the head with a hammer and restart logging if logging wasn't working.

This was sort-of OK, because it meant subsequent tests would run fine, but the one test would show failed - even though it probably succeeded.  This would be fine, except that one of my machines (my oldest and slowest) had syslog-ng die on it a few times a day.  I don't know why, and as long as I can live with it for my testing, I don't much care.  I just want it to work.  (I know, it's a lousy attitude, but I have way more to do than I can possibly do).

The solution

Then it I had this revolutionary thought - I could use HA software to make my logging highly available!!

Hold the presses, folks, new headline reads
   "HA guru realizes he can use HA software just like he tells everyone else to do!"

To fix this problem all I had to do was change the init script for syslog to use our cool little cl_respawn tool to babysit the syslog-ng service.  Although I could have used Heartbeat to monitor this service, it seemed like overkill and would have conflicted with CTS.

So, I set out to use cl_respawn to restart syslog-ng quickly - minimizing but not eliminating the possibliity of losing important log messages.

When I looked at the init scripts (they're from SUSE Linux), they had these statements in them:

• For starting
  startproc -p ${syslog_pid} ${BINDIR}/${syslog} $params
• For stopping
  killproc -p ${syslog_pid} -TERM ${BINDIR}/${syslog} ; rc_status -v
• For status
  checkproc -p ${syslog_pid}      ${BINDIR}/${syslog{; rc_status -v
• checkproc -p ${syslog_pid}      /usr/bin/cl_respawn; rc_status -v

My first thought was ll I had to do was insert cl_respawn ahead of the ${BINDIR}/syslog and I'd be done.  Well.... not quite...

If I had done that, then the pid file for the service ${syslog_pid} would have pointed not to cl_respawn, but to syslog-ng.  So, when I tried to shut down syslog, cl_respawn would have just respawned it.  OOPS.  Not quite the right effect.

What was necessary was for the syslog pid file to contain the pid of cl_respawn, not the pid of syslog-ng.  One minor problem - the author of cl_respawn didn't deal with pidfiles.  To fix that, I added support for a -p option to tell it the name of the pid file to use.

Now I try it.  Uh-oh... It didn't work.  The logs are quickly filled with attempts to start  ${syslog} and having it fail continually with  socket in use.  What was all that about?

By default, syslog-ng forks itself into the background,  and its parent process exits.  That makes cl_respawn think it's died - so it restarts it - and it fails ad infinitum.  So, I read the man page for syslog-ng and discover the -F option to keep it from forking.  Without that, cl_respawn can't tell when it dies.

Along the way, I read the code, find a couple of other minor bugs and fix them.  I update my init script and now it looks like this:

• For starting
  startproc -p ${syslog_pid} /usr/bin/cl_respawn -p ${syslog_pid} ${BINDIR}/${syslog} -F $params
• For stopping
  killproc -p ${syslog_pid} -TERM /usr/bin/cl_respawn ; rc_status -v
• For status
• checkproc -p ${syslog_pid}      /usr/bin/cl_respawn; rc_status -v

Of course, if you don't run SUSE Linux, then your init scripts will look somewhat different, but I'm sure you'll figure it out.

Why not just use respawn in inittab?

Those of you who know UNIX administration to any degree realize that /etc/inittab[5] has a respawn directive you can give it.  Why wouldn't that do the trick?  The short answer is service dependencies.   The longer answer is below:

• Logging depends on other /etc/init.d services, so you don't want it to start until after those other services (like the network) are started.  The LSB init script system supports these dependencies and starts things in the right order.
• Other services depend on logging.  A number of other services can't start until after logging starts.  If you try and disable the /etc/init.d/syslog service on your machine so you can start it with respawn from /etc/inittab, havoc ensues - because these other services won't start until the /etc/init.d/syslog service is started.  If you disable it, they won't start.
• What fun would that be?  I mean, if we wrote this cl_respawn tool, we probably ought to use it ;-).

What did I learn?

• How to use cl_respawn in real life
• Some missing requirements for cl_respawn
• I was reminded of the advantages of using our own software
• How handy simple little tools like cl_respawn can be

[1]  http://hg.linux-ha.org/dev/file/tip/tools/cl_respawn.c
[2]  http://linux-ha.org/CTS
[3]  http://linux-ha.org/
[4]  http://www.balabit.com/network-security/syslog-ng/opensource-logging-system/"
[5]  http://www.freebsd.org/cgi/man.cgi?query=inittab&manpath=Red+Hat+Linux%2Fi386+9&format=html
[6]  http://techthoughts.typepad.com/managing_computers/2007/09/tools-for-servi.html