multicriteria driven resource management strategies in grms krzysztof kurowski, jarek nabrzyski,...

Multicriteria Driven Resource Management Strategies in GRMS

Krzysztof Kurowski, Jarek Nabrzyski,

Ariel Oleksiak, Juliusz Pukacki

Poznan Supercomputing and Networking Center, Poland

The 4th Cracow Grid Workshop -CGW Krzysztof Kurowski

Resource management strategies

Grid Computing


GRMS Architecture


GRMS as a part of the GridLab


Introduction to MC

MotivationGRMS is a solution for non-dedicated resourcesWe consider a resource management problem as a multicriteria decision making process with various stakeholders (end-users, administrators, etc.)Different preferences of stakeholders (sets of criteria and their importance)We need to aggregate somehow (negotiation and agreement processes are required) various criteria and stakeholders’ preferencesWe focus on a compromise solution rather than the optimal one We want to satisfy many stakeholders rather than the particular one

Various criteria and constraints depending on available information and scenarios

RequirementsFlexibility (in terms of criteria and multicriteria methods)Re-use of common functionality (ranking, non-dominated solutions, solutions meeting constraints)


Introduction to MC

MCEvaluatorImplementation of needed multicriteria models and toolsSet of classes providing abstraction of entities used in multicriteria models

Job description/propertiesIncluding user’s and administrator preferencesTaking into consideration multiple criteria, their importance, indifference thresholds and constraints

Two main effortsMulti-criteria analysis engine – MCEvaluatorIncluding user’s preferences in a job description


MCEvaluator - Design


MCEvaluator - Model

Main entitiesCriteria (objectives, soft constraints: user is interested in their greatest or lowest possible values)Constraints (hard constraints: if is not satisfied solution is not taken into consideration)Solutions (e.g. resources, schedules etc. along with description parameters)MCEvaluator (decision point)

Multi-criteria methodsEvaluation function (e.g. weighted sum)Non-dominated solutionsLexicographic orderRule-based systemsOther ...


Memory

Storage

Memory

Storage

End user 1 End user 2

Application 1(e.g. Data analysis )

Application 2(e.g. Data mining)

R1

R2

R3R4

R1

R2R3

R4

Hard constraints (e.g. RSL… ClassAd, scripts)<Mem = 100MB>, <Storage = 1G>

??? ???

Simple example


Memory

Storage

MAX Z = 1*Mem + 2*Storage(where z is the objective function)

Memory

Storage

MAX Z = 2*Mem + 1*Storage(where z is the objective function)

R1

R2

R3R4

R1

End user 1 End user 2

Application 1(e.g. Data analysis )

Application 2(e.g. Data mining)

R2R3

R4

Simple example


Job Description

Expressing user preferencesCriteria (I want as fast CPU as possible)

Weights (amount of memory is two times more important than a number of processors)

Constraints (amount of free memory must be greater than x)

Indifference thresholds (difference of 10MB is not significant)


Job Description - Schema

<constraints><resConstraint parameter=„MEMORY">

<constraintModel preferenceType="PRIORITY"

optimizationType="GAIN"><indiffThreshold>10</indiffThreshold> <value>1</value>

</constraintModel><constraintValue>

<min indiffThreshold="0">100</min><max indiffThreshold=„20">200</max>

</constraintValue> </resConstraint>

</constraints>


Examples

Selection of the best hosts for a jobBased on hosts parameters: CPU load, total memory, free memory, CPU count, CPU speed

Selection of the best host or the best queueBased on estimated runtime and queue waiting time (+ estimated errors) taken from the prediction system

Selection of the best job to migrate (to release resources for a new job)

Based on parameters of the host after job migration and migration costs

Selection of the best queue in the local resource management system

Based on aggregated historical data taken from the GridLab Adaptive Service (Delphoi)


Selection of the best hosts for a job


Example 1 (Host Parameters)

CriteriaCPU loadCPU countCPU speedTotal memoryFree memory

ConstraintsNo constraints (resources filtered in the resource discovery phase)

SolutionsSet on the basis of parameters describing hosts

//criteria NumericalCriterion[] criteria = new

NumericalCriterion[5]; criteria[0] = new NumericalCriterion("cpuLoad",

Criterion.GAIN); criteria[1] = new NumericalCriterion("cpuCount",

Criterion.GAIN); criteria[2] = new NumericalCriterion("cpuSpeed",

Criterion.GAIN); criteria[3] = new NumericalCriterion("memory",

Criterion.GAIN); criteria[4] = new NumericalCriterion("memAvail",

Criterion.GAIN); // //weights float[] weights = new float[5]; weights[0] = weightLoad; weights[1] = weightCPUCount; weights[2] = weightCPUSpeed; weights[3] = weightMemory; weights[4] = weightMemAvail; //evaluator mcEvaluators = new Vector(1); mcEvaluators.add(new

MCFuncEvaluator("GRMSmatchmaking", criteria, weights, null));


Selection of the best host or the best queue


Example 2 (Time Prediction)

CriteriaTime

Error

Standard Deviation

Maximal – minimal time

Constraintserr <= stdev

stdev < time/2

max-min <= time (+- 1 minute)

SolutionsSet on the basis of information from the prediction system

//criteria NumericalCriterion[] criteriaMCT = new NumericalCriterion[4]; criteriaMCT[0] = new NumericalCriterion("time", Criterion.COST); criteriaMCT[0].setIndiffThreshold(1); criteriaMCT[1] = new NumericalCriterion("err", Criterion.COST); criteriaMCT[2] = new NumericalCriterion("stdev", Criterion.COST); criteriaMCT[3] = new NumericalCriterion("max-min", Criterion.COST); criteriaMCT[3].setIndiffThreshold(1); // //weights float[] weightsMCT = new float[4]; weightsMCT[0] = weightTime; weightsMCT[1] = weightErr; weightsMCT[2] = weightStdev; weightsMCT[3] = weightMaxMin; //constraints MetricConstraint[] constraintsMCT = new MetricConstraint[3]; //err <= stdev constraintsMCT[0] = new MetricConstraint("err", MetricConstraint.LE,

null); constraintsMCT[0].setOtherMetric("stdev"); //stdev < time/2 constraintsMCT[1] = new MetricConstraint("stdev", MetricConstraint.LS,

null); constraintsMCT[1].setOtherMetric("time"); constraintsMCT[1].setOtherMetricFactor(0.5f); //max-min <= time (+- 1) constraintsMCT[2] = new MetricConstraint("max-min",

MetricConstraint.LE, null); constraintsMCT[2].setOtherMetric("time"); constraintsMCT[2].setIndiffThreshold(1);


Selection of the best job to migrate (to release resources for a new job)


Example 3 (Adaptive Service)

CriteriaavgNumberWaitingJobsavgJobWaitingTimesdJobWaitingTimeavgFreeNodescpuTimemaxJobsmaxNodes

ConstraintscpuTimemaxJobsmaxNodes

SolutionsSet on the basis of information from the adaptive service

// based on historic information (dynamic parameters) criteria[0] = new NumericalCriterion("avgNumberWaitingJobs", Criterion.COST); criteria[1] = new NumericalCriterion("avgJobWaitingTime", Criterion.COST); criteria[2] = new NumericalCriterion("sdJobWaitingTime", Criterion.COST);

criteria[3] = new NumericalCriterion("avgFreeNodes", Criterion.GAIN);

// based on queue configuration (static parameters) criteria[4] = new NumericalCriterion("cpuTime", Criterion.GAIN); criteria[5] = new NumericalCriterion("cpus", Criterion.GAIN); criteria[6] = new NumericalCriterion("maxJobs", Criterion.GAIN); criteria[7] = new NumericalCriterion("maxMem", Criterion.GAIN); criteria[7] = new NumericalCriterion("maxNodes", Criterion.GAIN);


MCEvaluator – Next Steps

Development of the engineImplementation of other multi-criteria methods

Negotiable constraints

Use in another scenariosUse of other decision models (currently the evaluation function)

Scheduling with advance reservation

Resource co-allocations

Other ?


Conclusion

Flexible engine for criteria and constraints management – various:

criteriaconstraintsdecision making methods

Used in a few scenarios/testbedsCurrently multi-criteria evaluation function

Job description containing user preferencesCriteria (+ their importance) and constraints

It would be nice to useCriteria in agreement negotiationsCriteria concerning deadlines, runtimes etc.


GRMS web page

www.gridlab.org/grms/

Thank you!

multicriteria driven resource management strategies in grms krzysztof kurowski, jarek nabrzyski,...

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