A deadline constrained preemptive scheduler using queuing systems for multi-tenancy clouds

Ru Jia, Yun Yang, John Grundy, Jacky Keung, Hao Li

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

Scheduling on clouds is required so that service providers can meet Quality of Service (QoS) requirements of tenants. Deadline is a major criterion in judging QoS. This work presents a real-time, preemptive, constrained scheduler using queuing theory-PDSonQueue-which enables better meetinhg of QoS requirements. PDSonQueue also shortens a job's completion time and improves system's throughput. PDSon-Queue, as a dynamic priority real-time greedy scheduler, builds a queuing-based mathematical model to accurately predict a job's execution and waiting time, where jobs arrive by following a stochastic process and request resources. Our scheduler introduces a novel 'Earliest Maximal Waiting Time First (EMWTF)' concept to fine tune job scheduling to guarantee the job being accomplished within the deadline. Deadline constrained jobs are scheduled preemptively from low priority jobs with the intent of maximising the number of jobs completed within the deadlines, while allowing system's resources to be shared by other regular jobs. PDSonQueue integrates an improved Dominant Resource Fairness (DRF) greedy resource allocation approach to capture the essence of tenants' resource allocation and run as many jobs as possible. Our experimental results indicate that PDSonQueue can improve by at least 20% of deadline-based QoS rate, and by at least 30% for throughput.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Cloud Computing - IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services
EditorsElisa Bertino, Carl K. Chang, Peter Chen, Ernesto Damiani, Michael Goul, Katsunori Oyama
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages63-67
Number of pages5
ISBN (Electronic)9781728127057, 9781728127040
ISBN (Print)9781728127064
DOIs
Publication statusPublished - 2019
EventInternational Conference on Cloud Computing 2019 - Milan, Italy
Duration: 8 Jul 201913 Jul 2019
Conference number: 12th
https://conferences.computer.org/cloud/2019/

Conference

ConferenceInternational Conference on Cloud Computing 2019
Abbreviated titleCLOUD 2019
CountryItaly
CityMilan
Period8/07/1913/07/19
Internet address

Keywords

  • Deadline
  • Multi-tenancy
  • Queuing Theory
  • Resource preemption
  • Scheduling

Cite this

Jia, R., Yang, Y., Grundy, J., Keung, J., & Li, H. (2019). A deadline constrained preemptive scheduler using queuing systems for multi-tenancy clouds. In E. Bertino, C. K. Chang, P. Chen, E. Damiani, M. Goul, & K. Oyama (Eds.), Proceedings - 2019 IEEE International Conference on Cloud Computing - IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services (pp. 63-67). [8814540] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CLOUD.2019.00022
Jia, Ru ; Yang, Yun ; Grundy, John ; Keung, Jacky ; Li, Hao. / A deadline constrained preemptive scheduler using queuing systems for multi-tenancy clouds. Proceedings - 2019 IEEE International Conference on Cloud Computing - IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services. editor / Elisa Bertino ; Carl K. Chang ; Peter Chen ; Ernesto Damiani ; Michael Goul ; Katsunori Oyama. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 63-67
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title = "A deadline constrained preemptive scheduler using queuing systems for multi-tenancy clouds",
abstract = "Scheduling on clouds is required so that service providers can meet Quality of Service (QoS) requirements of tenants. Deadline is a major criterion in judging QoS. This work presents a real-time, preemptive, constrained scheduler using queuing theory-PDSonQueue-which enables better meetinhg of QoS requirements. PDSonQueue also shortens a job's completion time and improves system's throughput. PDSon-Queue, as a dynamic priority real-time greedy scheduler, builds a queuing-based mathematical model to accurately predict a job's execution and waiting time, where jobs arrive by following a stochastic process and request resources. Our scheduler introduces a novel 'Earliest Maximal Waiting Time First (EMWTF)' concept to fine tune job scheduling to guarantee the job being accomplished within the deadline. Deadline constrained jobs are scheduled preemptively from low priority jobs with the intent of maximising the number of jobs completed within the deadlines, while allowing system's resources to be shared by other regular jobs. PDSonQueue integrates an improved Dominant Resource Fairness (DRF) greedy resource allocation approach to capture the essence of tenants' resource allocation and run as many jobs as possible. Our experimental results indicate that PDSonQueue can improve by at least 20{\%} of deadline-based QoS rate, and by at least 30{\%} for throughput.",
keywords = "Deadline, Multi-tenancy, Queuing Theory, Resource preemption, Scheduling",
author = "Ru Jia and Yun Yang and John Grundy and Jacky Keung and Hao Li",
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Jia, R, Yang, Y, Grundy, J, Keung, J & Li, H 2019, A deadline constrained preemptive scheduler using queuing systems for multi-tenancy clouds. in E Bertino, CK Chang, P Chen, E Damiani, M Goul & K Oyama (eds), Proceedings - 2019 IEEE International Conference on Cloud Computing - IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services., 8814540, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 63-67, International Conference on Cloud Computing 2019, Milan, Italy, 8/07/19. https://doi.org/10.1109/CLOUD.2019.00022

A deadline constrained preemptive scheduler using queuing systems for multi-tenancy clouds. / Jia, Ru; Yang, Yun; Grundy, John; Keung, Jacky; Li, Hao.

Proceedings - 2019 IEEE International Conference on Cloud Computing - IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services. ed. / Elisa Bertino; Carl K. Chang; Peter Chen; Ernesto Damiani; Michael Goul; Katsunori Oyama. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 63-67 8814540.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AB - Scheduling on clouds is required so that service providers can meet Quality of Service (QoS) requirements of tenants. Deadline is a major criterion in judging QoS. This work presents a real-time, preemptive, constrained scheduler using queuing theory-PDSonQueue-which enables better meetinhg of QoS requirements. PDSonQueue also shortens a job's completion time and improves system's throughput. PDSon-Queue, as a dynamic priority real-time greedy scheduler, builds a queuing-based mathematical model to accurately predict a job's execution and waiting time, where jobs arrive by following a stochastic process and request resources. Our scheduler introduces a novel 'Earliest Maximal Waiting Time First (EMWTF)' concept to fine tune job scheduling to guarantee the job being accomplished within the deadline. Deadline constrained jobs are scheduled preemptively from low priority jobs with the intent of maximising the number of jobs completed within the deadlines, while allowing system's resources to be shared by other regular jobs. PDSonQueue integrates an improved Dominant Resource Fairness (DRF) greedy resource allocation approach to capture the essence of tenants' resource allocation and run as many jobs as possible. Our experimental results indicate that PDSonQueue can improve by at least 20% of deadline-based QoS rate, and by at least 30% for throughput.

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Jia R, Yang Y, Grundy J, Keung J, Li H. A deadline constrained preemptive scheduler using queuing systems for multi-tenancy clouds. In Bertino E, Chang CK, Chen P, Damiani E, Goul M, Oyama K, editors, Proceedings - 2019 IEEE International Conference on Cloud Computing - IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 63-67. 8814540 https://doi.org/10.1109/CLOUD.2019.00022