Reinforcement Learning applied to Meta -scheduling in grid environments Bernardo Costa Inês Dutra

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Reinforcement Learning applied to Meta-scheduling in grid environments

Bernardo Costa

Inês Dutra

Marta Mattoso

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Outline

Introduction Algorithms Experiments Conclusions and Future work

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Introduction Algorithms Experiments Conclusions and Future work

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Introduction

Relevance: Available grid schedulers usually do not employ a

strategy that may benefit a single or multiple users.

Some strategies employ performance information dependent algorithms (pida).

Most works are simulated.

Difficulty: monitoring information not reliable due to network latency.

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Introduction Algorithms Experiments Conclusions and Future work

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Study of 2 Algorithms (AG) A. Galstyan, K. Czajkowski, and K.

Lerman. Resource allocation in the grid using reinforcement learning. In AAMAS, pages 1314–1315. IEEE, 2004.

(MQD) Y. C. Lee and A. Y. Zomaya. A grid scheduling algorithm for bag-of-tasks applications using multiple queues with duplication. 5th IEEE/ACIS International Conference on Computer and Information Science and 1st IEEE/ACIS International Workshop on Component-Based Software Engineering, Software Architecture and Reuse. ICIS-COMSAR, pages 5–10, 2006.

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What is reinforcement learning?

Machine learning technique used to learn behaviours given a series of temporal events.

Non-supervised learning. Based on the idea of rewards and

punishments.

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Algorithms

AG and MQD use reinforcement learning to associate an efficiency rank to an RMS.

Reinforcement learning native to AG. MQD was modified to use this technique to

estimate computational power of an RMS. AG allocates RMS in a greedy and probabilistic

way. MQD allocates RMS associatively and

deterministically.

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Algorithms

Calculating efficiency: Reward is assigned to RMS that has performance

better than average. Reward can be negative (punishment). RMS may not change its efficiency value.

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Algorithms

Calculating efficiency: parameters: and l is the importance of the time spent executing a

task affects rewarding.

l is a learning parameter

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Algorithms

AG: With high prob, associates job to the best

available RMS, otherwise, selects randomly. MQD:

Groups of jobs sorted according execution time are associated to an RMS. Most efficient executes the heaviest jobs. Initial allocation to estimate RMS´ efficiency

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Algorithm AG

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J1 J3J2 J4 J5

J6 J7 J8 J9

R1E = 0

R2E = 0

R3E = 0

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J4 J5

J6 J7 J8 J9

R1E = 0

R2E = 0,3

R3E = -0,3

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J7 J8 J9

R1E = 0,3

R2E = 0,057

R3E = 0,51

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Algorithm MQD

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J140

J350

J215

J430

J510

J670

J720

J820

J940

R1E = 0

R2E = 0

R3E = 0

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J140

J350

J215

J430

J510

J670

J720

J820

J940

R1E = 0

R2E = 0

R3E = 0

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J140

J350

J430

J670

J820

J940

R1E = 0,3

R2E = -0,3

R3E = 0

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J140

J350

J820

R1E = 0,09

R2E = -0,09

R3E = -0,3

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Avg per proc

Global Avg

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Introduction Algorithms Experiments Conclusions and Future work

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Experiments

GridbusBroker:No need to install it in other grid sitesOnly requirement: ssh access to a grid

nodeRound-robin scheduler (RR)

Limitations:Does not support job duplication Imposes a limit on the number of active

jobs per RMS

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Experiments

Resources in 6 grid sites:LabIA: 24 (Torque/Maui)LCP: 28 (SGE)Nacad: 16 (PBS PRO)UERJ: 144 (Condor)UFRGS: 4 (Torque)LCC: 44 (Torque)

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Experiments

Objective: study performance of algorithms in a real grid environment.

Application: bag-of-tasks. CPU intensive.

Duration between 3 and 8 minutes.

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Experiments

Evaluation criteria: makespan.

Makespan was normalized with respect to RR

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Experiments

Phase I: Tuning of parameters and l 500 jobs.

Phase II: Performance of re-scheduling. Later load increased to 1000 jobs.

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Experiments

One experiment is a run of consecutive executions of RR, AG and MQD.

A scenario is a set of experiments with fixed parameters.

For each scenario: 15 runs. T-tests to verify statistical difference

beteween AG/MQD e RR, with 95% confidence (the results have a normal distribution).

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Experiments (Phase I)

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Experiments (Phase II)

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Introduction Algorithms Experiments Conclusions and Future work

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Conclusions and Future work

Results showed that was possible to achieve optimizations with both AG and MQD wrt RR

Experiments validate MQD simulation results found in the literature.

Reinforcement learning is a promising technique to classify resources in real grid environments.

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Conclusions and Future work

Study the behavior of AG and MQD with other kinds of applications, e.g., data intensive, with dependencies.

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Questions?

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Annex

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Definições

Gerenciador de recursos: sistema que gerencia a submissão e execução de jobs dentro de um domínio específico.

Resource Management System (RMS): sinônimo para gerenciador de recursos.

Batch job scheduler: escalonador típico de um RMS. Ex: SGE, PBS/Torque.

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Definições

Meta-escalonador: um escalonador que não tem acesso direto aos recursos, mas apenas aos RMS que os gerenciam.

Aprendizado por reforço: técnica que induz um agente a tomar decisões por meio de recompensas oferecidas.

Makespan: tempo total gasto por um meta-escalonador para finalizar a execução de um conjunto de jobs a ele designado.

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Definições

Job: aplicativo submetido ao grid por um usuário, executado em geral por um RMS. Exemplos de tipos de jobs: Bag-of-Tasks: jobs que não possuem relação de

dependência ou precedência explícita entre si. Troca de parâmetros (APST): jobs de um mesmo

executável que diferenciam-se por um valor de entrada que varia entre as execuções.

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