Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment

Goodhead Tomvie Abraham; Evans Fiebibiseighe Osaisai; Abalaba Ineyekineye

doi:doi:10.11648/j.mcs.20210603.12

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Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment

Goodhead Tomvie Abraham, Evans Fiebibiseighe Osaisai, Abalaba Ineyekineye

Published in Mathematics and Computer Science (Volume 6, Issue 3)

Received: 17 May 2021 Accepted: 9 June 2021 Published: 21 June 2021

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Abstract

The computing Grid has emerged as a platform to solve the complex and ever-increasing processing need of man and advances in computing technology have birthed the multicore era aimed for high throughput and efficient parallel computing. However, most systems still rely on the underlying hardware for parallelism despite the hard evidence that sequential algorithms do not optimally exploit parallel systems. This research seeks to harness the benefits of multicore systems using job and machine grouping methods to enhance parallelism in the scheduling of Grid jobs. The paper presents the result of two separate experiments on a method that parallelize scheduling algorithm on two multicore platforms. An arbitrary method was employed to group machines; a summation of the total processing power of machines in each group was made. To ensure load balancing, jobs were allocated to machine groups based on the ratio of the total processing power of the machines in each group. The MinMin Grid scheduling algorithm was implemented independently within the groups using a range of threads varied in powers of two. Also, the numbers of groups were varied between 2, 4, and 8. The same experiment was executed on a single processor computer; a duocore machine and a quadcore machine. A performance improvement of 16% to 85% was recorded by the group method against the best ordinary MinMin results and an improvement of 50% to 84% was recorded by the group method against the ordinary MinMin on corresponding machines. We prove that an increase in the number of groups results in improved performance on corresponding machines (approximately 2 times using 2 groups, approximately 3 times using four groups, and approximately 6 times using 8 groups). And most importantly, we established that as the number of processors increases, the grouping method makes more significant improvements over the ordinary MinMin scheduling algorithm executed on the multicore systems.

Published in	Mathematics and Computer Science (Volume 6, Issue 3)
DOI	10.11648/j.mcs.20210603.12
Page(s)	49-58
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Multicore-environment, Parallelism, Multi-scheduling, Machine Grouping, Job Grouping, Scheduling

References

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Goodhead Tomvie Abraham, Evans Fiebibiseighe Osaisai, Abalaba Ineyekineye. (2021). Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment. Mathematics and Computer Science, 6(3), 49-58. https://doi.org/10.11648/j.mcs.20210603.12

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Goodhead Tomvie Abraham; Evans Fiebibiseighe Osaisai; Abalaba Ineyekineye. Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment. Math. Comput. Sci. 2021, 6(3), 49-58. doi: 10.11648/j.mcs.20210603.12

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Goodhead Tomvie Abraham, Evans Fiebibiseighe Osaisai, Abalaba Ineyekineye. Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment. Math Comput Sci. 2021;6(3):49-58. doi: 10.11648/j.mcs.20210603.12

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@article{10.11648/j.mcs.20210603.12,
  author = {Goodhead Tomvie Abraham and Evans Fiebibiseighe Osaisai and Abalaba Ineyekineye},
  title = {Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment},
  journal = {Mathematics and Computer Science},
  volume = {6},
  number = {3},
  pages = {49-58},
  doi = {10.11648/j.mcs.20210603.12},
  url = {https://doi.org/10.11648/j.mcs.20210603.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20210603.12},
  abstract = {The computing Grid has emerged as a platform to solve the complex and ever-increasing processing need of man and advances in computing technology have birthed the multicore era aimed for high throughput and efficient parallel computing. However, most systems still rely on the underlying hardware for parallelism despite the hard evidence that sequential algorithms do not optimally exploit parallel systems. This research seeks to harness the benefits of multicore systems using job and machine grouping methods to enhance parallelism in the scheduling of Grid jobs. The paper presents the result of two separate experiments on a method that parallelize scheduling algorithm on two multicore platforms. An arbitrary method was employed to group machines; a summation of the total processing power of machines in each group was made. To ensure load balancing, jobs were allocated to machine groups based on the ratio of the total processing power of the machines in each group. The MinMin Grid scheduling algorithm was implemented independently within the groups using a range of threads varied in powers of two. Also, the numbers of groups were varied between 2, 4, and 8. The same experiment was executed on a single processor computer; a duocore machine and a quadcore machine. A performance improvement of 16% to 85% was recorded by the group method against the best ordinary MinMin results and an improvement of 50% to 84% was recorded by the group method against the ordinary MinMin on corresponding machines. We prove that an increase in the number of groups results in improved performance on corresponding machines (approximately 2 times using 2 groups, approximately 3 times using four groups, and approximately 6 times using 8 groups). And most importantly, we established that as the number of processors increases, the grouping method makes more significant improvements over the ordinary MinMin scheduling algorithm executed on the multicore systems.},
 year = {2021}
}

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TY  - JOUR
T1  - Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment
AU  - Goodhead Tomvie Abraham
AU  - Evans Fiebibiseighe Osaisai
AU  - Abalaba Ineyekineye
Y1  - 2021/06/21
PY  - 2021
N1  - https://doi.org/10.11648/j.mcs.20210603.12
DO  - 10.11648/j.mcs.20210603.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 49
EP  - 58
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20210603.12
AB  - The computing Grid has emerged as a platform to solve the complex and ever-increasing processing need of man and advances in computing technology have birthed the multicore era aimed for high throughput and efficient parallel computing. However, most systems still rely on the underlying hardware for parallelism despite the hard evidence that sequential algorithms do not optimally exploit parallel systems. This research seeks to harness the benefits of multicore systems using job and machine grouping methods to enhance parallelism in the scheduling of Grid jobs. The paper presents the result of two separate experiments on a method that parallelize scheduling algorithm on two multicore platforms. An arbitrary method was employed to group machines; a summation of the total processing power of machines in each group was made. To ensure load balancing, jobs were allocated to machine groups based on the ratio of the total processing power of the machines in each group. The MinMin Grid scheduling algorithm was implemented independently within the groups using a range of threads varied in powers of two. Also, the numbers of groups were varied between 2, 4, and 8. The same experiment was executed on a single processor computer; a duocore machine and a quadcore machine. A performance improvement of 16% to 85% was recorded by the group method against the best ordinary MinMin results and an improvement of 50% to 84% was recorded by the group method against the ordinary MinMin on corresponding machines. We prove that an increase in the number of groups results in improved performance on corresponding machines (approximately 2 times using 2 groups, approximately 3 times using four groups, and approximately 6 times using 8 groups). And most importantly, we established that as the number of processors increases, the grouping method makes more significant improvements over the ordinary MinMin scheduling algorithm executed on the multicore systems.
VL  - 6
IS  - 3
ER  -

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Author Information

Goodhead Tomvie Abraham

Computer Science Department, Niger Delta University, Yenagoa, Nigeria
Evans Fiebibiseighe Osaisai

Mathematics Department, Niger Delta University, Yenagoa, Nigeria
Abalaba Ineyekineye

Mathematics Department, Niger Delta University, Yenagoa, Nigeria

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Goodhead Tomvie Abraham, Evans Fiebibiseighe Osaisai, Abalaba Ineyekineye. (2021). Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment. Mathematics and Computer Science, 6(3), 49-58. https://doi.org/10.11648/j.mcs.20210603.12

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Goodhead Tomvie Abraham; Evans Fiebibiseighe Osaisai; Abalaba Ineyekineye. Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment. Math. Comput. Sci. 2021, 6(3), 49-58. doi: 10.11648/j.mcs.20210603.12

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Goodhead Tomvie Abraham, Evans Fiebibiseighe Osaisai, Abalaba Ineyekineye. Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment. Math Comput Sci. 2021;6(3):49-58. doi: 10.11648/j.mcs.20210603.12

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@article{10.11648/j.mcs.20210603.12,
  author = {Goodhead Tomvie Abraham and Evans Fiebibiseighe Osaisai and Abalaba Ineyekineye},
  title = {Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment},
  journal = {Mathematics and Computer Science},
  volume = {6},
  number = {3},
  pages = {49-58},
  doi = {10.11648/j.mcs.20210603.12},
  url = {https://doi.org/10.11648/j.mcs.20210603.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20210603.12},
  abstract = {The computing Grid has emerged as a platform to solve the complex and ever-increasing processing need of man and advances in computing technology have birthed the multicore era aimed for high throughput and efficient parallel computing. However, most systems still rely on the underlying hardware for parallelism despite the hard evidence that sequential algorithms do not optimally exploit parallel systems. This research seeks to harness the benefits of multicore systems using job and machine grouping methods to enhance parallelism in the scheduling of Grid jobs. The paper presents the result of two separate experiments on a method that parallelize scheduling algorithm on two multicore platforms. An arbitrary method was employed to group machines; a summation of the total processing power of machines in each group was made. To ensure load balancing, jobs were allocated to machine groups based on the ratio of the total processing power of the machines in each group. The MinMin Grid scheduling algorithm was implemented independently within the groups using a range of threads varied in powers of two. Also, the numbers of groups were varied between 2, 4, and 8. The same experiment was executed on a single processor computer; a duocore machine and a quadcore machine. A performance improvement of 16% to 85% was recorded by the group method against the best ordinary MinMin results and an improvement of 50% to 84% was recorded by the group method against the ordinary MinMin on corresponding machines. We prove that an increase in the number of groups results in improved performance on corresponding machines (approximately 2 times using 2 groups, approximately 3 times using four groups, and approximately 6 times using 8 groups). And most importantly, we established that as the number of processors increases, the grouping method makes more significant improvements over the ordinary MinMin scheduling algorithm executed on the multicore systems.},
 year = {2021}
}

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TY  - JOUR
T1  - Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment
AU  - Goodhead Tomvie Abraham
AU  - Evans Fiebibiseighe Osaisai
AU  - Abalaba Ineyekineye
Y1  - 2021/06/21
PY  - 2021
N1  - https://doi.org/10.11648/j.mcs.20210603.12
DO  - 10.11648/j.mcs.20210603.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 49
EP  - 58
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20210603.12
AB  - The computing Grid has emerged as a platform to solve the complex and ever-increasing processing need of man and advances in computing technology have birthed the multicore era aimed for high throughput and efficient parallel computing. However, most systems still rely on the underlying hardware for parallelism despite the hard evidence that sequential algorithms do not optimally exploit parallel systems. This research seeks to harness the benefits of multicore systems using job and machine grouping methods to enhance parallelism in the scheduling of Grid jobs. The paper presents the result of two separate experiments on a method that parallelize scheduling algorithm on two multicore platforms. An arbitrary method was employed to group machines; a summation of the total processing power of machines in each group was made. To ensure load balancing, jobs were allocated to machine groups based on the ratio of the total processing power of the machines in each group. The MinMin Grid scheduling algorithm was implemented independently within the groups using a range of threads varied in powers of two. Also, the numbers of groups were varied between 2, 4, and 8. The same experiment was executed on a single processor computer; a duocore machine and a quadcore machine. A performance improvement of 16% to 85% was recorded by the group method against the best ordinary MinMin results and an improvement of 50% to 84% was recorded by the group method against the ordinary MinMin on corresponding machines. We prove that an increase in the number of groups results in improved performance on corresponding machines (approximately 2 times using 2 groups, approximately 3 times using four groups, and approximately 6 times using 8 groups). And most importantly, we established that as the number of processors increases, the grouping method makes more significant improvements over the ordinary MinMin scheduling algorithm executed on the multicore systems.
VL  - 6
IS  - 3
ER  -

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