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From Pierre-Yves Ritschard <...@spootnik.org>
Subject Re: Resource Management/Locking [was: Re: What would be your ideal solution?]
Date Tue, 26 Nov 2013 08:52:50 GMT
Hi Everyone,

First off, I'm really excited that there is an undergoing discussion on
these issues.
I agree with john that CAP provides a good "framework" for looking at the
individual properties of the distributed system that cloudstack is, as a
whole. The separation between an orchestration layer and automation layer
is also a valid abstraction of the main roles of the management server.

As far as CAP properties are concerned, I don't think there is much
question that the aim is for:

* a CP orchestration layer (it will continue to rely on a CP system: an
RDBMS)
* an AP automation layer (it is tied to an AP system, a cluster of
hypervisors)

As far as operations are concerned I think the plugin approach in CS is
great, it allows to distribute a very simple system to start with, where a
single management server will most likely run. In largely distributed
systems it is certainly not a crazy requirement to rely on zookeeper, in
many shops using CS, ZK is already used anyhow, operation-wise, it is not
more complex than, say, maintaining a highly available MySQL cluster.

Before I go on, I'll just acknowledge here that I'm not addressing the
issue of compatibility, all approaches discussed so far, except Darren's do
not concern themselves with compatibility and upgrades which will be a
major pain if the persistence layer / data store evolves in any significant
way. I know this is a big concern for CS users and citrix, and will need to
be taken into account, I don't have a clear picture of how this could be
done.

As far as persistence is concerned, there are different things that CS
stores which have different requirements:

* Organizational data needs strong consistency: users, accounts, domains,
projects, configuration (for networks, templates, ...)
* Transient resource data (vm running status) can only have eventual
consistency
* Usage data only requires eventual consistency (and does not need to
clutter the main data store)

I think one of the reasons for the head-scratching around resources right
now is that the persistence layer is right now used both for storing the
expected state of resources and their actual state, maybe their should be a
transient persistence layer used for storing known states.

So to sum up, as far as storage is concerned it might be easier to reason
about CS in terms of three different persistence layer:

* A main layer for organizational data, expected state and last known state
* A layer for storing state as reported by resource owners (hypervisors)
* A mechanism for distributing usage data

With such a system, the mailbox approach is possible. I do think that the
amount of work in CS would be huge and that we would risk ending up with a
franken-erlang type system which java doesn't lend itself well too (surely
scala could but this would imply a total rewrite).

An intermediate step could be to look at resources the same way Apache
Kafka does (or in a way Apache Cassandra). Managers could be seen as a
homogeneous clusters responsible for an nth of the cluster (for a cluster
of n managers). A good mechanism is needed for agreeing on cluster
membership, but there are several proven and valid approaches for this (and
its a problem that lends itself well to the plugin approach in CS).

A typical incoming API request would thus hit any management node, which
could either issue a redirect to the correct node, proxy it to the correct
node or create a jobid and let the client query the jobid for its status.

The upside of this approach is that it still makes it possible for CS to
become the jenkins of cloud controllers (it would need an HSQLDB option for
persistence though !) and rely on proven and well understood projects for
larger deployments (like ZK, or when it stabilizes, an implementation of
raft).

A first step towards this would be to have some sort of agreement on the
different layers of persistence needed throughout CS and try to move
forward. I can get my hands dirty and try to evolve the Dao stuff that is
everywhere in CS, but I'd like to know I'm not going towards a dead-end.








On Mon, Nov 25, 2013 at 10:18 PM, Darren Shepherd <
darren.s.shepherd@gmail.com> wrote:

> Okay, I'll have to stew over this for a bit.  My one general comment is
> that it seems complicated.  Such a system seems like it would take a good
> amount of effort to construct properly and as such it's a risky endeavour.
>
> Darren
>
>
> On Mon, Nov 25, 2013 at 12:10 PM, John Burwell <jburwell@basho.com> wrote:
>
> > Darren,
> >
> > In a peer-to-peer model such as I describe, active-active is and is not a
> > concept.  The supervision tree is responsible for identifying failure,
> and
> > initiating process re-allocation for failed resources.  For example, if a
> > pod’s management process crashed, it would also crash all of the
> processes
> > managing the hosts in that pod.  The zone would then attempt to restart
> the
> > pod’s management process (either local to the zone supervisor or on a
> > remote instance which could be configurable) until it was able to start
> > “ready” process for the child resource.
> >
> > This model requires a “special” root supervisor that is controlled by the
> > orchestration tier which can identify when a zone supervisor becomes
> > unavailable, and attempts to restart it.  The ownership of this “special”
> > supervisor will require a consensus mechanism amongst the orchestration
> > tier processes to elect an owner of the process and determine when a new
> > owner needs to be elected (e.g. a Raft implementation such as barge [1]).
> >  Given the orchestration tier is designed as an AP system, an
> orchestration
> > tier process should be able to be an owner (i.e. the operator is not
> > required to identify a “master” node).  There are likely other potential
> > topologies (e.g. a root supervisor per zone rather than one for all
> zones),
> > but in all cases ownership election would be the same.  Most importantly,
> > there are no data durability requirements in this claim model.  When an
> > orchestration process becomes unable to continue owning a root
> supervisor,
> > the other orchestration processes recognize the missing owner and
> initiate
> > ownership claim the process for the partition.
> >
> > In all failure scenarios, the supervision tree must be rebuilt from the
> > point of failure downward using the process allocation process I
> previously
> > described.  For an initial implementation, I would recommend taking
> simply
> > throwing any parts of the supervision tree that are already running in
> the
> > event of a widespread failure (e.g. a zone with many pods).  Dependent on
> > the recovery time and SLAs, a future optimization may be to re-attach
> > “orphaned” branches of the previous tree to the tree being built as part
> of
> > the recovery process (e.g. loss a zone supervisor due to a switch
> failure).
> >  Additionally, the system would also need a mechanism to hand-off
> ownership
> > of the root supervisor for planned outages (hardware
> > upgrades/decommissioning, maintenance windows, etc).
> >
> > Again, caveated with a a few hand waves, the idea is to build up a
> > peer-to-peer management model that provides strict serialization
> > guarantees.  Fundamentally, it utilizes a tree of processes to provide
> > exclusive access, distribute work, and ensure availability requirements
> > when partitions occur.  Details would need to be worked out for the best
> > application to CloudStack (e.g root node ownership and orchestration tier
> > gossip), but we would be implementing well-trod distributed systems
> > concepts in the context cloud orchestration (sounds like a fun thing to
> do
> > …).
> >
> > Thanks,
> > -John
> >
> > [1]: https://github.com/mgodave/barge
> >
> > P.S. I see the libraries/frameworks referenced as the building blocks to
> a
> > solution, but none of them (in whole or combination) solves the problem
> > completely.
> >
> > On Nov 25, 2013, at 12:29 PM, Darren Shepherd <
> darren.s.shepherd@gmail.com>
> > wrote:
> >
> > I will ask one basic question.  How do you forsee managing one mailbox
> per
> > resource.  If I have multiple servers running in an active-active mode,
> how
> > do you determine which server has the mailbox?  Do you create actors on
> > demand?  How do you synchronize that operation?
> >
> > Darren
> >
> >
> > On Mon, Nov 25, 2013 at 10:16 AM, Darren Shepherd <
> > darren.s.shepherd@gmail.com> wrote:
> >
> >> You bring up some interesting points.  I really need to digest this
> >> further.  From a high level I think I agree, but there are a lot of
> implied
> >> details of what you've said.
> >>
> >> Darren
> >>
> >>
> >> On Mon, Nov 25, 2013 at 8:39 AM, John Burwell <jburwell@basho.com>
> wrote:
> >>
> >>> Darren,
> >>>
> >>> I originally presented my thoughts on this subject at CCC13 [1].
> >>>  Fundamentally, I see CloudStack as having two distinct tiers —
> >>> orchestration management and automation control.  The orchestration
> tier
> >>> coordinates the automation control layer to fulfill user goals (e.g.
> create
> >>> a VM instance, alter a network route, snapshot a volume, etc)
> constrained
> >>> by policies defined by the operator (e.g. multi-tenacy boundaries,
> ACLs,
> >>> quotas, etc).  This layer must always be available to take new
> requests,
> >>> and to report the best available infrastructure state information.
>  Since
> >>> execution of work is guaranteed on completion of a request, this layer
> may
> >>> pend work to be completed when the appropriate devices become
> available.
> >>>
> >>> The automation control tier translates logical units of work to
> >>> underlying infrastructure component APIs.  Upon completion of unit of
> >>> work’s execution, the state of a device (e.g. hypervisor, storage
> device,
> >>> network switch, router, etc) matches the state managed by the
> orchestration
> >>> tier at the time unit of work was created.  In order to ensure that the
> >>> state of the underlying devices remains consistent, these units of work
> >>> must be executed serially.  Permitting concurrent changes to resources
> >>> creates race conditions that lead to resource overcommitment and state
> >>> divergence.   A symptom of this phenomenon are the myriad of scripts
> >>> operators write to “synchronize” state between the CloudStack database
> and
> >>> their hypervisors.  Another is the example provided below is the rapid
> >>> create-destroy which can (and often does) leave dangling resources due
> to
> >>> race conditions between the two operations.
> >>>
> >>> In order to provide reliability, CloudStack vertically partitions the
> >>> infrastructure into zones (independent power source/network uplink
> >>> combination) sub-divided into pods (racks).  At this time, regions are
> >>> largely notional, as such, as are not partitions at this time.
>  Between the
> >>> user’s zone selection and our allocators distribution of resources
> across
> >>> pods, the system attempts to distribute resources widely as possible
> across
> >>> these partitions to provide resilience against a variety infrastructure
> >>> failures (e.g. power loss, network uplink disruption, switch failures,
> >>> etc).  In order maximize this resilience, the control plane
> (orchestration
> >>> + automation tiers) must be to operate on all available partitions.
>  For
> >>> example, if we have two (2) zones (A & B) and twenty (20) pods per
> zone, we
> >>> should be able to take and execute work in Zone A when one or more
> pods is
> >>> lost, as well as, when taking and executing work in Zone B when Zone B
> has
> >>> failed.
> >>>
> >>> CloudStack is an eventually consistent system in that the state
> >>> reflected in the orchestration tier will (optimistically) differ from
> the
> >>> state of the underlying infrastructure (managed by the automation
> tier).
> >>>  Furthermore, the system has a partitioning model to provide
> resilience in
> >>> the face of a variety of logical and physical failures.  However, the
> >>> automation control tier requires strictly consistent operations.
>  Based on
> >>> these definitions, the system appears to violate the CAP theorem [2]
> >>> (Brewer!).  The separation of the system into two distinct tiers
> isolates
> >>> these characteristics, but the boundary between them must be carefully
> >>> implemented to ensure that the consistency requirements of the
> automation
> >>> tier are not leaked to the orchestration tier.
> >>>
> >>> To properly implement this boundary, I think we should split the
> >>> orchestration and automation control tiers into separate physical
> processes
> >>> communicating via an RPC mechanism — allowing the automation control
> tier
> >>> to completely encapsulate its work distribution model.  In my mind, the
> >>> tricky wicket is providing serialization and partition tolerance in the
> >>> automation control tier.  Realistically, there two options — explicit
> and
> >>> implicit locking models.  Explicit locking models employ an external
> >>> coordination mechanism to coordinate exclusive access to resources
> (e.g.
> >>> RDBMS lock pattern, ZooKeeper, Hazelcast, etc).  The challenge with
> this
> >>> model is ensuring the availability of the locking mechanism in the
> face of
> >>> partition — forcing CloudStack operators to ensure that they have
> deployed
> >>> the underlying mechanism in a partition tolerant manner (e.g. don’t
> locate
> >>> all of the replicas in the same pod, deploy a cluster per zone, etc).
> >>>  Additionally, the durability introduced by these mechanisms inhibits
> the
> >>> self-healing due to lock staleness.
> >>>
> >>> In contrast, an implicit lock model structures the runtime execution
> >>> model to provide exclusive access to a resource and model the
> partitioning
> >>> scheme.  One such model is to provide a single work queue (mailbox) and
> >>> consuming process (actor) per resource.  The orchestration tier
> provides a
> >>> description of the partition and resource definitions to the automation
> >>> control tier.  The automation control tier creates a supervisor per
> >>> partition which in turn manage process creation per resource.
>  Therefore,
> >>> process creation and destruction creates an implicit lock.  Since
> >>> automation control tier does not persist data in this model,  The
> crash of
> >>> a supervisor and/or process (supervisors are simply specialized
> processes)
> >>> releases the implicit lock, and signals a re-execution of the
> >>> supervisor/process allocation process.  The following high-level
> process
> >>> describes creation allocation (hand waves certain details such as back
> >>> pressure and throttling):
> >>>
> >>>
> >>>    1. The automation control layer receives a resource definition (e.g.
> >>>    zone description, VM definition, volume information, etc).  These
> requests
> >>>    are processed by the owning partition supervisor exclusively in
> order of
> >>>    receipt.  Therefore, the automation control tier views the world as
> a tree
> >>>    of partitions and resources.
> >>>    2. The partition supervisor creates the process (and the associated
> >>>    mailbox) — providing it with the initial state.  The process state
> is
> >>>    Initialized.
> >>>    3. The process synchronizes the state of the underlying resource
> >>>    with the state provided.  Upon successful completion of state
> >>>    synchronization, the state of the process becomes Ready.  Only Ready
> >>>    processes can consume units of work from their mailboxes.  The
> processes
> >>>    crashes.  All state transitions and crashes are reported to
> interested
> >>>    parties through an asynchronous event reporting mechanism including
> the id
> >>>    of the unit of work the device represents.
> >>>
> >>>
> >>> The Ready state means that the underlying device is in a useable state
> >>> consistent with the last unit of work executed.  A process crashes
> when it
> >>> is unable to bring the device into a state consistent with the unit of
> work
> >>> being executed (a process crash also destroys the associated mailbox —
> >>> flushing pending work).  This event initiates execution of allocation
> >>> process (above) until the process can be re-allocated in a Ready state
> >>> (again throttling is hand waved for the purposes of brevity).  The
> state
> >>> synchronization step converges the actual state of the device with
> changes
> >>> that occurred during unavailability.  When a unit of work fails to be
> >>> executed, the orchestration tier determines the appropriate recovery
> >>> strategy (e.g. re-allocate work to another resource, wait for the
> >>> availability of the resource, fail the operation, etc).
> >>>
> >>> The association of one process per resource provides exclusive access
> to
> >>> the resource without the requirement of an external locking mechanism.
>  A
> >>> mailbox per process provides orders pending units of work.  Together,
> they
> >>> provide serialization of operation execution.  In the example
> provided, a
> >>> unit of work would be submitted to create a VM and a second unit of
> work
> >>> would be submitted to destroy it.  The creation would be completely
> >>> executed followed by the destruction (assuming no failures).
>  Therefore,
> >>> the VM will briefly exist before being destroyed.  In conduction with a
> >>> process location mechanism, the system can place the processes
> associated
> >>> with resources in the appropriate partition allowing the system
> properly
> >>> self heal, manage its own scalability (thinking lightweight system
> VMs),
> >>> and systematically enforce partition tolerance (the operator was nice
> >>> enough to describe their infrastructure — we should use it to ensure
> >>> resilience of CloudStack and their infrastructure).
> >>>
> >>> Until relatively recently, the implicit locking model described was
> >>> infeasible on the JVM.  Using native Java threads, a server would be
> >>> limited to controlling (at best) a few hundred resources.  However,
> >>> lightweight threading models implemented by libraries/frameworks such
> as
> >>> Akka [3], Quasar [4], and Erjang [5] can scale to millions of
> “threads” on
> >>> reasonability sized servers and provide the supervisor/actor/mailbox
> >>> abstractions described above.  Most importantly, this approach does not
> >>> require operators to become operationally knowledgeable of yet another
> >>> platform/component.  In short, I believe we can encapsulate these
> >>> requirements in the management server (orchestration + automation
> control
> >>> tiers) — keeping the operational footprint of the system proportional
> to
> >>> the deployment without sacrificing resilience.  Finally, it provides
> the
> >>> foundation for proper collection of instrumentation information and
> process
> >>> control/monitoring across data centers.
> >>>
> >>> Admittedly, I have hand waved some significant issues that would beed
> to
> >>> be resolved.  I believe they are all resolvable, but it would take
> >>> discussion to determine the best approach to them.  Transforming
> CloudStack
> >>> to such a model would not be trivial, but I believe it would be worth
> the
> >>> (significant) effort as it would make CloudStack one of the most
> scalable
> >>> and resilient cloud orchestration/management platforms available.
> >>>
> >>> Thanks,
> >>> -John
> >>>
> >>> [1]:
> >>>
> http://www.slideshare.net/JohnBurwell1/how-to-run-from-a-zombie-cloud-stack-distributed-process-management
> >>> [2]: http://lpd.epfl.ch/sgilbert/pubs/BrewersConjecture-SigAct.pdf
> >>> [3]: http://akka.io
> >>> [4]: https://github.com/puniverse/quasar
> >>> [5]: https://github.com/trifork/erjang/wiki
> >>>
> >>> P.S.  I have CC’ed the developer mailing list.  All conversations at
> >>> this level of detail should be initiated and occur on the mailing list
> to
> >>> ensure transparency with the community.
> >>>
> >>> On Nov 22, 2013, at 3:49 PM, Darren Shepherd <
> >>> darren.s.shepherd@gmail.com> wrote:
> >>>
> >>> 140 characters are not productive.
> >>>
> >>> What would be your idea way to do distributed concurrency control?
> >>>  Simple use case.  Server 1 receives a request to start a VM 1.
>  Server 2
> >>> receives a request to delete VM 1.  What do you do?
> >>>
> >>> Darren
> >>>
> >>>
> >>>
> >>
> >
> >
>

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