analysis of cooperation in multi-organization scheduling pierre-françois dutot (grenoble...

Analysis of cooperation in multi-

organization Scheduling

Pierre-François Dutot (Grenoble University)

Krzysztof Rzadca (Polish-Japanese school, Warsaw)

Fanny Pascual (LIP6, Paris)Denis Trystram (Grenoble University

and INRIA)

NCST, CIRM, may 13, 2008

Goal The evolution of high-performance execution platforms leads to distributed entities (organizations) which have their own « local » rules. Our goal is to investigate the possibility of cooperation for a better use of a global computing system made from a collection of autonomous clusters.

Work partially supported by the Coregrid Network of Excellence of the EC.

Main result

We show in this work that it is always possible to produce an efficient collaborative solution (i.e. with a theoretical performance guarantee) that respects the organizations’ selfish objectives.

A new algorithm with theoretical worst case analysis plus experiments for a case study (each cluster has the same objective: makespan).

Target platforms

Our view of computational grids is a collection of independent clusters belonging to distinct organizations.

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Computational model(one cluster)Independent applications are

submitted locally on a cluster. The are represented by a precedence task graph.An application is a parallel rigid job.Let us remind briefly the model (close to what presented Andrei Tchernykh this morning)… See Feitelson for more details and classification.

Cluster

J1J2J3… …

…

Local queue of submitted jobs

overheadComputational area

Rigid jobs: the number of processors is fixed.

#of required processors qi

Runtime pi

#of required processors qi

Runtime pi

high jobs (those which require more than m/2 processors) and low jobs (the others).

Scheduling rigid jobs:Packing algorithms

Scheduling independent rigid jobs may be solved as a 2D packing Problem (strip packing). List algorithm (off-line).

m

Cluster

J1J2J3… …

…Organization k

n organizations.

m processors(identical for the sake of presentation)

k

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n organizations (here, n=3, red 1, green 2 and blue 3)

Each organization k aims at minimizing « its » own makespan max(Ci,k)We want also that the global makespan is minimized

Cmax(Ok) organizationCmax(Mk) cluster

n organizations (here, n=3, red 1, green 2 and blue 3)

Each organization k aims at minimizing « its » own makespan max(Ci,k)We want also that the global makespan (max over all local ones) is minimized

Cmax(O2)

Cmax(O3)= Cmax(M1)

Cmax(Ok) organizationCmax(Mk) cluster

Problem statement

MOSP: minimization of the « global » makespan under the constraint that no local schedule is increased.

Consequence: taking the restricted instance n=1 (one organization) and m=2 with sequential jobs, the problem is the classical 2 machines problem which is NP-hard. Thus, MOSP is NP-hard.

Multi-organizations

Motivation:A non-cooperative solution is that all the organizations compute their local jobs (« my job first » policy).However, such a solution is arbitrarly far from the global optimal (it grows to infinity with the number of organizations n). See next example with n=3 for jobs of unit length.

no cooperation with cooperation (optimal)

O1

O2

O3

O1

O2

O3

Preliminary resultsSingle organization scheduling (packing).

Resource constraint list algorithm (Garey-Graham 1975).

• List-scheduling: (2-1/m) approximation ratio• HF: Highest First schedules (sort the jobs by decreasing

number of required processors). Same theoretical guaranty but better from the practical point of view.

Analysis of HF (single cluster)

high utilization zone (I)(more than 50% of processors are busy)

low utilization zone (II)

Proposition. All HF schedules have the same structure which consists in two consecutive zones of high (I) and low (II) utilization.

Proof. (2 steps)By contracdiction, no high job appears after zone (II) starts

Collaboration between clusters can substantially

improve the results.Other more sophisticated algorithms than the simple load balancing are possible: matching certain types of jobs may lead to bilaterally profitable solutions.

no cooperation with cooperation

O1

O2

O1

O2

If we can not worsen any local makespan, the global optimum can

not be reached.

1

2

1

2 2

1

2

1

2

2local globally optimal

O1

O2

O1

O2


not be reached.

1

2

1

2 2

1

2

1

2

2

1

2

1

2

2

local globally optimal

best solution that does notincrease Cmax(O1)

O1

O2

O1

O2

O1

O2


not be reached.Lower bound on approximation ratio greater than 3/2.

1

2

1

2 2

1

2

1

2

2

1

2

1

2

2best solution that does notincrease Cmax(O1)

O1

O2

O1

O2

O1

O2

Multi-Organization Load-Balancing

1 Each cluster is running local jobs with Highest First LB = max (pmax,W/nm) 2. Unschedule all jobs that finish after 3LB.3. Divide them into 2 sets (Ljobs and Hjobs)4. Sort each set according to the Highest first order5. Schedule the jobs of Hjobs backwards from 3LB

on all possible clusters6. Then, fill the gaps with Ljobs in a greedy manner

let consider a cluster whose last job finishes before 3LB

3LB

LjobHjob

3LB

LjobHjob

3LB

Ljob

Notice that the centralized scheduling mechanism isnot work stealing, moreover, the idea is to changeas minimum as we can the local schedules.

Feasibility (insight)

Zone (I)

3LB

Zone (I)Zone (II)

Sketch of analysis

Case 1: x is a small job.Global surface argument

Case 2: x is a high job.Much more complicated, see the paper for technical details

Proof by contradiction: let us assume that it is not feasible, and call x the first job that does not fit in a cluster.

3-approximation (by construction)

This bound is tight

Local HF schedules


This bound is tight

Optimal (global) schedule


This bound is tight

Multi-organization load-balancing

Improvement

Local schedules

Multi-org LB

load balance

O3

O1O2

O4O5

O3

O1O2

O4O5

O3

O1O2

O4O5

We add an extra load-balancing procedure

O3

O1O2

O4O5

Compact

Some experiments

Conclusion

We proved in this work that cooperation may help for a better global performance (for the makespan).We designed a 3-approximation algorithm.It can be extended to any size of organizations (with an extra hypothesis).

Conclusion

We proved in this work that cooperation may help for a better global performance (for the makespan).We designed a 3-approximation algorithm.It can be extended to any size of organizations (with an extra hypothesis).

Based on this, it remains a lot of interesting open problems, including the study of the problem for different local policies or objectives (Daniel Cordeiro)

Thanks for attentionDo you have any questions?

Using Game Theory?

We propose here a standard approach using CombinatorialOptimization.

Cooperative Game Theory may also be usefull, but it assumes that players (organizations) can communicate and form coalitions. The members of the coalitions split the sum of their playoff after the end of the game.We assume here a centralized mechanism and no communication between organizations.

analysis of cooperation in multi-organization scheduling pierre-françois dutot (grenoble...

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