A Deadline Constrained Preemptive Scheduler Using Queuing Systems for Multi-tenancy Clouds

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Detail(s)

Original languageEnglish
Title of host publication2019 IEEE International Conference on Cloud Computing, IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services
Subtitle of host publicationProceedings
PublisherIEEE
Pages63-67
ISBN (Electronic)978-1-7281-2705-7
ISBN (Print)978-1-7281-2706-4
Publication statusPublished - Jul 2019

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
PublisherIEEE
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190

Conference

Title12th IEEE International Conference on Cloud Computing, CLOUD 2019
PlaceItaly
CityMilan
Period8 - 13 July 2019

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.

Research Area(s)

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

Citation Format(s)

A Deadline Constrained Preemptive Scheduler Using Queuing Systems for Multi-tenancy Clouds. / Jia, Ru; Yang, Yun; Grundy, John; Keung, Jacky; Li, Hao.

2019 IEEE International Conference on Cloud Computing, IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services: Proceedings. IEEE, 2019. p. 63-67 8814540 (IEEE International Conference on Cloud Computing, CLOUD).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review