Meaningful measurement

What is a meaningful measurement of performance of a scheduler?  I am embarking on the next paper which is due in two weeks, and unsure about what I am actually going to write.  Well, I know what I want to write, but not sure about its presentation mechanism.

This is the status of where I am right now…

Alea + GridSim have now successfully been morphed into a new framework called dSim.  Why dSim?  not really sure, but it had a good ring to it, and potentially a commercially viable name.  Anyhow, dSim is capable of creating a dynamic load, creating dynamic set of resources, schedule appropriately, and measure results.  All of this can be done via configuration files and parameters.

One of the results that I am collecting is the makespan for the tasks, and as a result the entire job.  I dont really care about the job itself, but the makespan of a given task.

I am collecting data that is like the following:

Client Name task ID Submission time Finished Time Makespan
Client 1 1034 3000 11000 8000

For a given run, I have 1000’s of these rows.  I have one row entry per task; which makes sense.  The question is then what to do with this data?

Statistical analysis of the data would give me the mean, median, and the STD.  But is that enough?  For example, i may have following:

Client 1:
mean: 33000
STD: 18500

Client 2:
mean: 44000
STD: 9000

Obviously, that’s good info, but is it enough?

The work can potentially be extended to a cloud environment, but for now, the focus is HPC environments.

Scheduling Architecture

As I am working my way thru the Alea framework to continue my research.  I realized the Alea does not actually implement a scheduling algo, at least not a fairshare one.

Schedulers are difficult to write, but I have had the fortune to develop a few over the years.  Wanted to document the internal architecture of a “good” scheduler.  Frankly, it does not matter what type of scheduling algorithm you implement.  As long at you follow the following approach, you will be ok.

Schedulers need to have 3 distinct stages:
– Pre-processing of tasks

– Scheduling of tasks

– Dispatching of tasks

most people lump the first two together.  That’s a mistake!

– Step 1: preprocessing:

This is where you apply the desired policy to the incoming tasks.  If you are interested in a FCFS policy, well, then you make sure that you queue up your tasks in a FCFS manner, but putting all the tasks in the order of arrival in a queue.  If you are interested in SJF (shortest job first), well, then you sort the incoming tasks in a manner that the shortest jobs are first in the queue.  Maybe you have more than one queue; it does not matter.  The point here is that in the preprocessing, you are not actually scheduling anything, but rather enforcing a policy or set of desired policies to the incoming tasks.

– Step 2: scheduling:

After your tasks are sorted based on some policy, you are ready to schedule these tasks.  Scheduling is supply vs. demand.
The most common scheduling policy is fairshare.  I have many posts about fairshare, but the gist of it is that you are given a set of resources based on your “fair share”, which is determined by “you deserve more because you need more” mentality.  If A has 10x as many tasks as B, it will get 10x more resources.
There are other policies, like highest priority first, etc.

– Step 3: dispatching:

After you have decided which tasks should get executed first, you now need to dispatch the tasks.  Most scheduling systems use gang-bang scheduling in that a number of tasks are scheduled.  The reason here is efficiency and practicality.  You dont make a scheduling decision on one task, but rather a set of tasks.  I am classifying gang-bang in the dispatching of the tasks, as it does not make a scheduling decision.  Many papers (ref needed) simply claim that gang-bang is a scheduling policy of its own.  The dispatcher essentially goes thru the set of tasks assigned to be executed, and sends those tasks to the nodes.  Once the set is dispatched, it goes to the next set.

There are optimization that can be done at every step, but that’s where the innovation comes in.  Also, the steps are not discrete as they depend on each other.  You *can* and should make them as decoupled as possible, but that’s just a general software development rule of thumb!

Art Sedighi

Policy as it pertains to high-performance systems

I was recently asked to think about how high performance systems deal with policies?

Two clarifications are required here:

  1. What are high-performance systems in the context of Grid, HPC and scheduling?
  2. What are the policies that a typical high-performance system deals with or in other words, sets?

In the context of high-performance schedulers, a high-performance system is the scenario where we are dealing with a large number of tasks (potentially millions of tasks) that are fairy short in duration, and the total job is only complete once all the tasks have been completed.

What is “short” in our context?  I can easily say that short is in the order of milliseconds or even seconds, but more quantitatively, I will assume that a task is short in duration iff:

  1. Scheduling overhead directly impacts the speedup factor (i.e. the time that it takes to schedule that task cannot be neglected)
  2. The runtime of a give task is significantly shorter (two-orders of magnitude) than the overall runtime of given job.

The bigger question becomes what these policies actually are and why would they be of importance?

The following is a subset of policies that we could be referring to:

  1. Sharing policy
  2. Fair-share policy pertaining to scheduling
  3. (others – TBD)

In a sharing policy, a client can allow some or all if its resources to be shared (given out) to other client[s] that may need them.  This obviously has a risk that the resources are not immediately available when the original owner needs them back.  At the same time, if one waits before lending out resources, there could a high degree of unutilized resources.

 

The fair-share policy is scheduling is probably the most implicitly set policy in shared environments.  The users get their “fair-share” of the available resources based on some preset fair-share policy.  A user may assist or hint the policy with priorities, for example, but generally speaking, the policy is set and agreed to by all the users.

My research focuses on Fair-share policy and how it affects users – and to an extend resources.  Users agree to the fair-share policy with the assumption that what the scheduler does is “fair”.

Furthermore, users interact with the system unbeknownst to how the fair-share scheduling policy is affecting their runtime.  The side effect of a fair-share scheduler is that timing is severely affects the outcome.  Since there is no historical perspective kept to aid the scheduler to better aid the enforcement of such policy, and some users end up keep temporarily starved.

 

Art Sedighi

Go Top