Alea and GridSim

GridSim is a message passing toolkit.  Messages are passed around to different components that are either instructions of what to do, or data (or task).

Typical messages/commands are:

– Data: task or gridlet sent to the scheduler

– Control messages: schedule tasks, done with scheduling, tasks pending, task started, …

There are a number of control messages – many I added to the Alea framework, but on a couple of data messages.  The concept doesn’t change, however.  Message of a given type is sent to a destination, and there is logic on the receiving side that knows how to decode that message.

Alea framework is for the most part incomplete and very difficult to work with.  Data is hard-coded, the configuration is rigid and the code is not structured well (not proper OOD practices/patterns were followed).  I did made changes to the base code, but started to refactor and create “new” classes that are better designed and manageable.

There is now a submission strategy/policy via which you can change your submission policy.  The policy is API controlled, but I am hoping to make everything be file-based/controlled.  Alea would read job information from a file; that’s very limiting.  We change that to be all automated.  Based on the policy and the number of jobs/tasks, you can publish any which way you want, simulating as many clients as you want.

There is now a proper Fair-share scheduler.  It is not efficient b/c it uses too many locks, but it implements the scheduler pattern that I outlined in an earlier post

Control messages:  added a number of control messages to signal the state of the queues.  Are they empty? should we do another round of scheduling? etc, etc.

Lot more to go…  I am working on my second paper which is due the end of December 2014.  Once that paper is completed, I will move on to phase 2, which is better measurement of the grid resources/tasks.

Art Sedighi

Scheduling Simulator

My next task is to create a scheduling simulator – and a scheduler to further test my hypothesis.

I have been playing with Alea and GridSim.  I will upload the papers here to this post, but you can google for both of these research projects.

1.GridSim is an established project, and used in the community.  The creator is Rajkumar Buyya, who is very well known researcher and author in this field (http://www.buyya.com/gridsim).

2.Alea though not well known or an established project, presented a great idea in how to simulate a scheduler and large grid environments.  (http://www.fi.muni.cz/~xklusac/alea/)

Alea, however, was not complete and I ended up rewriting the scheduler module, a fair-share scheduler and a job loader.  I am not sure if I would like to open source the project – or my portions of the project as they present a great tool for simulating large grid environments and may be commercialized.

The greatest challenge was to create a job loader that can create a task profile similar to max{0, a*sin(bx)}.  This pattern of task submission is very common in HPC environments.  In a typical scenario, a client submits a certain number of tasks; waits for some responses to come back or creates another set of tasks, and repeats.  This start-stop fashion of task submission is where the greatest ability to game the system comes from.  For example, for one sets of submits a = 1000 (or 1000 tasks are submitted), but for a subsequent set, a = 5000.  This fluctuation, although not modeled in this version of the scheduling simulator, can present a great challenge to a scheduler aiming to fairly distribute resources.

 

Art Sedighi

 

Published paper in SCPE

Hi everyone,

As promissed, I am attaching my first published paper:

FAIRNESS OF TASK SCHEDULING IN HIGH PERFORMANCE COMPUTING ENVIRONMENTS

You can download it here: Fairness-sedighi

 

Abstract:

We claim that the current scheduling systems for high performance computing environments are unable to fairly distribute resources among the users, and as such, are unable to maximize the overall user satisfaction. We demonstrate that a user can game the system to cause a temporal starvation to the other users of the system, even though all users will eventually finish their job in the shared-computing environment. Undesired and unfair delays in the current fair-shared schedulers impede these schedulers from wide deployments to users with diverse usage profiles.

 

Source:

http://www.scpe.org/index.php/scpe/article/view/1020

 

Art Sedighi

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

Congestion Games

The concept of Congestion Games is a very interesting one as it pertains to Scheduling.  Rosenthal (1973) coined the term and defined a congestion game to be a scenario where the payoff of a given player depends on the resource it ends up on, and the how busy that resource may be based on how many other players are also on that resource.  Rosenthal, however, focused on having identical machines, and same-size jobs.

A scheduler may schedule a number of tasks on a given machine, if the number of pending tasks is greater than the total number of resources.  As the number of tasks scheduled on a given machine increases – they are all competing for the same resource (CPU, memory, etc) and therefore the utility perceived by a given client could change based on how “congested” a node gets.

The fact of the matter is that most schedulers schedule one task per CPU, so avoid congestion.  I do agree that there could be other processes running on that machine as part of the Operating System (for example, time sync daemon runs on a box every few minutes), but these processes in a dedicated node scenario consume very minimal resources and can be ignored.

In unrelated machines where the infrastructure is heterogeneous, Nir Andelman argues in Strong Price of Anarchy (2006),  that congestion does not take place since the load of a given task is different on different machines.  I strongly disagree with this sentiment.  Based on one of my previous postings called Utility of Master-worker Systems, we must start thinking about the utility of the node itself.  A node (CPU) desires a high utility as well, and that utility is to be idle.  Based on this, unrelated machines cannot be treated any different than related machines.   A system of nodes breaks down to its components of individual nodes with each node desiring a level of utility that matches the utility of all the other nodes.

In short, there is an intrinsic commonality between related and unrelated machines.  This commonaility is the fact that each CPU can achieve the same level of utility by doing the same thing (taking on the same strategy in GT lingo), and that is to finish the task that it was given as soon as possible.

Furthermore, we need to look at scheduling a job on a CPU as being two seperate and sometimes conflciting games:

– Macro-level: a game played by the job submitters trying to minimize their runtime (makespan)

-Micro-level: a game played by the job and the CPU where the CPU wishes to be idle and the job wishes to be compeleted.

 

Reference:

Strong Price of Anarchy N. Andelman, M. Feldman, Y. Mansour, 2006

 

Zero-sum game

It is conceivable to think of a Grid Scheduler as the mediator to a zero-sum game.  The number of resources do not change — not taking into account the hype that is cloud these days with elastic computing.  If the number of resources available to a scheduler and in turn the clients/players of the system is constant, the number of CPU’s that one client gets assigned directly affects the number of CPU’s that has been “taken” away from the pool of resources available to the second client.

As the total number of resources does not change, and one players action based on fairshare scheduling affects the number of resources that it gets assigned, one can conclude:

Based on client’s task submission strategy, a client realizes a utility that is directly a result of the number of resources it received – or got assigned to by the scheduler.   

Fairness in scheduling

What does it mean to be fair when scheduling tasks across a Grid?

Depending on the perspective of the affected entity, fairness could mean different things.  For  a heavy user of the system, “fair” could mean:

“I need (should read “neeeed”) more, so it is fair for me to get more”

From a casual user’s perspective:

“As long as I get to do my work, it is fair”

From a light-user:

“I don’t use the system that often, so when I do, I should have higher priority”

There can be cases made for each of these scenarios.  The first scenario, the heavy user, is the one which more schedulers tend to please.  It is an implied favouritism in that in order to drain the pending queues the fastest, the scheduler schedules more tasks from the heavy user as it had a higher percentage of pending tasks.

What is fairshare?  how can schedulers pony up resources in a shared manner?

 

Art Sedighi

 

 

game theory vs. graph theory in scheduling

Most scheduling systems, resource managers, HPC schedulers, etc, use some sort of fair share algorithm where the portion of share resource being scheduled (divided up) depends on a very simple ratio analysis of “the person who ‘deserves’ more, gets more”.  The amount that one deserves usually depends on the number of tasks pending to be completed.  Essentially, the scheduler takes a snapshot of the queue sizes, and makes a determination that in order to drain the queues faster, the large queue must get a larger portion of the available resources.

Without the loss of generality, assume that we have two users (A and B).  ‘A’ has a queue size of 10 and ‘B’ has a Queue size of 90.  ‘A’ ends up with 10% of the resources and ‘B’ ends up with 90% of the resources.  If you have only 8 machines available at a time, there is a good chance that ‘A’ is starved.

Scheduling systems follow ‘a’ graph algorithm to make a determination.  Whether that’s implicit or explicit, a graph algorithm is somehow used.  Graphs are essentially decision trees, and based on the number of levels and/or the fan-out, they could be either complicated or a simple binary decision tree of “if not this way, then it must be the other way”.  That is why a two-node system scheduling system is deterministic, and anything above a two-node system falls under the nondeterministic realm (exception is a 3-node system).

Graph algorithms do not consider history — how did i get here?  That is the main reason why most fair-share algorithms are not fair.

Games depend solely on the history of events or strategies.  There is an implicit history that is built in to each strategy that allows one to determine all the previous steps.  As such, one’s current state is not the only state required to make a decision about the next state.

As opposed to using a fair-share algorithm, if schedulers treated each transaction as a game, and each event a strategy of that game, schedulers would actually be fair. 

Art Sedighi

 

Fair-share

Been thinking a lot about fair-share and if something is fair or how one can make it fair?

Fair-share, specially when it comes to computing, is figured out based on the load on the decision maker.  If I (a router for example) want to give fair access to a shared network, I look at my load of incoming and try to give out the most to the person with the largest queue.  Relatively speaking, all the queues get the same (fair-share) access, but not if you are the “little guy” that submits packets at a lower rate than others.

There are other mechanisms that try to take care of the little guy first and then go to the big users. In job scheduling, there is an algo for this called Shortest Job First.  You get the little guys and empty those queues first; then you are off to the longer running jobs.  The concept makes sense, right? You don’t want a short job to be stuck behind a job that runs for days.

So, what is fair?  How does one get its fair-share of a system?

Art Sedighi