Hello Travis,
I am just a short-time member of this list but I can definitely see the
benefit of using built-in OS resource management facilities to
dynamically manage cluster resources on the node level in this manner.
At our company we often fight for resources on our development cluster
as well as sometimes cancel running jobs in production to free up
immediately needed resources. If I understand it correctly, this would
solve a lot of our problems.
The only downside I see with this is that it is Linux-specific.
Michal
On 5.12.2016 16:36, Hegner, Travis wrote:
My apologies, in my excitement of finding a rather simple way to
accomplish the scheduling goal I have in mind, I hastily jumped
straight into a technical solution, without explaining that goal, or
the problem it's attempting to solve.
You are correct that I'm looking for an additional running mode for
the standalone scheduler. Perhaps you could/should classify it as a
different scheduler, but I don't want to give the impression that this
will be as difficult to implement as most schedulers are. Initially,
from a memory perspective, we would still allocate in a FIFO
manner. This new scheduling mode (or new scheduler, if you'd
rather) would mostly benefit any users with small-ish clusters, both
on-premise and cloud based. Essentially, my end goal is to be able to
run multiple *applications* simultaneously with each application
having *access* to the entire core count of the cluster.
I have a very cpu intensive application that I'd like to run weekly. I
have a second application that I'd like to run hourly. The hourly
application is more time critical (higher priority), so I'd like it to
finish in a small amount of time. If I allow the first app to run with
all cores (this takes several days on my 64 core cluster), then
nothing else can be executed when running with the default FIFO
scheduler. All of the cores have been allocated to the
first application, and it will not release them until it is finished.
Dynamic allocation does not help in this case, as there is always a
backlog of tasks to run until the first application is nearing the end
anyway. Naturally, I could just limit the number of cores that the
first application has access to, but then I have idle cpu time when
the second app is not running, and that is not optimal. Secondly in
that case, the second application only has access to the *leftover*
cores that the first app has not allocated, and will take a
considerably longer amount of time to run.
You could also imagine a scenario where a developer has a spark-shell
running without specifying the number of cores they want to utilize
(whether intentionally or not). As I'm sure you know, the default is
to allocate the entire cluster to this application. The cores
allocated to this shell are unavailable to other applications, even if
they are just sitting idle while a developer is getting their
environment set up to run a very big job interactively. Other
developers that would like to launch interactive shells are stuck
waiting for the first one to exit their shell.
My proposal would eliminate this static nature of core counts and
allow as many simultaneous applications to be running as the cluster
memory (still statically partitioned, at least initially) will allow.
Applications could be configured with a "cpu shares" parameter (just
an arbitrary integer relative only to other applications) which is
essentially just passed through to the linux cgroup cpu.shares
setting. Since each executor of an application on a given worker runs
in it's own process/jvm, then that process could be easily be placed
into a cgroup created and dedicated for that application.
Linux cgroups cpu.shares are pretty well documented, but the gist is
that processes competing for cpu time are allocated a percentage of
time equal to their share count as a percentage of all shares in that
level of the cgroup hierarchy. If two applications are both scheduled
on the same core with the same weight, each will get to utilize 50% of
the time on that core. This is all built into the kernel, and the only
thing the spark worker has to do is create a cgroup for each
application, set the cpu.shares parameter, and assign the executors
for that application to the new cgroup. If multiple executors are
running on a single worker, for a single application, the cpu time
available to that application is divided among each of those executors
equally. The default for cpu.shares is that they are not limiting in
any way. A process can consume all available cpu time if it would
otherwise be idle anyway.
Another benefit to passing cpu.shares directly to the kernel (as
opposed to some abstraction) is that cpu share allocations are
heterogeneous to all processes running on a machine. An admin could
have very fine grained control over which processes get priority
access to cpu time, depending on their
