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 language | English |
|---|---|
| Title of host publication | Proceedings - 2019 IEEE International Conference on Cloud Computing - IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services |
| Editors | Elisa Bertino, Carl K. Chang, Peter Chen, Ernesto Damiani, Michael Goul, Katsunori Oyama |
| Place of Publication | Piscataway NJ USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 63-67 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781728127057, 9781728127040 |
| ISBN (Print) | 9781728127064 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | International Conference on Cloud Computing 2019 - Milan, Italy Duration: 8 Jul 2019 → 13 Jul 2019 Conference number: 12th https://conferences.computer.org/cloud/2019/ |
Conference
| Conference | International Conference on Cloud Computing 2019 |
|---|---|
| Abbreviated title | CLOUD 2019 |
| Country | Italy |
| City | Milan |
| Period | 8/07/19 → 13/07/19 |
| Internet address |
Keywords
- Deadline
- Multi-tenancy
- Queuing Theory
- Resource preemption
- Scheduling
Cite this
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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 proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - A deadline constrained preemptive scheduler using queuing systems for multi-tenancy clouds
AU - Jia, Ru
AU - Yang, Yun
AU - Grundy, John
AU - Keung, Jacky
AU - Li, Hao
PY - 2019
Y1 - 2019
N2 - 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.
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.
KW - Deadline
KW - Multi-tenancy
KW - Queuing Theory
KW - Resource preemption
KW - Scheduling
UR - http://www.scopus.com/inward/record.url?scp=85072315415&partnerID=8YFLogxK
U2 - 10.1109/CLOUD.2019.00022
DO - 10.1109/CLOUD.2019.00022
M3 - Conference Paper
SN - 9781728127064
SP - 63
EP - 67
BT - Proceedings - 2019 IEEE International Conference on Cloud Computing - IEEE CLOUD 2019 - Part of the 2019 IEEE World Congress on Services
A2 - Bertino, Elisa
A2 - Chang, Carl K.
A2 - Chen, Peter
A2 - Damiani, Ernesto
A2 - Goul, Michael
A2 - Oyama, Katsunori
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway NJ USA
ER -