Cloud Bursting Scheduler for cost efficiency

Young Choon Lee; Bing Lian

doi:10.1109/CLOUD.2017.112

Cloud Bursting Scheduler for cost efficiency

Young Choon Lee, Bing Lian

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

19 Citations (Scopus)

Abstract

Clouds have been increasingly adopted due to primarily their elasticity and pay-as-you-go (PAYG) pricing. While many organizations outsource the entire ICT solution to public clouds like Amazon Web Services Elastic Compute Cloud (EC2), others consider occasional workload offloading (cloud bursting) due to various reasons including governance and security. In this paper, we present Cloud Bursting Scheduler (CBS), a new cloud bursting algorithm. CBS explicitly takes into account cost factors of private in-house system (or private cloud) and public cloud. In particular, CBS attempts to optimize the cost to performance ratio by offloading jobs to public cloud explicitly taking into account time-varying electricity rates with private clouds and the timeinvariant rental rate of many public clouds. Based on simulation results obtained using real workload traces, CBS saves costs of running workloads by 55% and 12% compared with costs of cloud sourcing and private cloud, respectively. It also improves resource utilization (to 89%) by judiciously (de)activating inhouse resources and dynamically provisioning cloud resources.

Original language	English
Title of host publication	Proceedings - 2017 IEEE 10th International Conference on Cloud Computing
Subtitle of host publication	CLOUD 2017
Editors	Geoffrey C. Fox
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	774-777
Number of pages	4
ISBN (Electronic)	9781538619933
DOIs	https://doi.org/10.1109/CLOUD.2017.112
Publication status	Published - 8 Sept 2017
Event	10th IEEE International Conference on Cloud Computing, CLOUD 2017 - Honolulu, United States Duration: 25 Jun 2017 → 30 Jun 2017

Conference

Conference	10th IEEE International Conference on Cloud Computing, CLOUD 2017
Country/Territory	United States
City	Honolulu
Period	25/06/17 → 30/06/17

Keywords

cloud bursting
Cloud computing
cost efficiency
energy efficiency
scheduling

Access to Document

10.1109/CLOUD.2017.112

Cite this

@inproceedings{d094af6120af45ebba063cec587eb23c,

title = "Cloud Bursting Scheduler for cost efficiency",

abstract = "Clouds have been increasingly adopted due to primarily their elasticity and pay-as-you-go (PAYG) pricing. While many organizations outsource the entire ICT solution to public clouds like Amazon Web Services Elastic Compute Cloud (EC2), others consider occasional workload offloading (cloud bursting) due to various reasons including governance and security. In this paper, we present Cloud Bursting Scheduler (CBS), a new cloud bursting algorithm. CBS explicitly takes into account cost factors of private in-house system (or private cloud) and public cloud. In particular, CBS attempts to optimize the cost to performance ratio by offloading jobs to public cloud explicitly taking into account time-varying electricity rates with private clouds and the timeinvariant rental rate of many public clouds. Based on simulation results obtained using real workload traces, CBS saves costs of running workloads by 55% and 12% compared with costs of cloud sourcing and private cloud, respectively. It also improves resource utilization (to 89%) by judiciously (de)activating inhouse resources and dynamically provisioning cloud resources.",

keywords = "cloud bursting, Cloud computing, cost efficiency, energy efficiency, scheduling",

author = "Lee, {Young Choon} and Bing Lian",

year = "2017",

month = sep,

day = "8",

doi = "10.1109/CLOUD.2017.112",

language = "English",

pages = "774--777",

editor = "Fox, {Geoffrey C.}",

booktitle = "Proceedings - 2017 IEEE 10th International Conference on Cloud Computing",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "10th IEEE International Conference on Cloud Computing, CLOUD 2017 ; Conference date: 25-06-2017 Through 30-06-2017",

}

Lee, YC & Lian, B 2017, Cloud Bursting Scheduler for cost efficiency. in GC Fox (ed.), Proceedings - 2017 IEEE 10th International Conference on Cloud Computing: CLOUD 2017., 8030670, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 774-777, 10th IEEE International Conference on Cloud Computing, CLOUD 2017, Honolulu, United States, 25/06/17. https://doi.org/10.1109/CLOUD.2017.112

Cloud Bursting Scheduler for cost efficiency. / Lee, Young Choon; Lian, Bing.
Proceedings - 2017 IEEE 10th International Conference on Cloud Computing: CLOUD 2017. ed. / Geoffrey C. Fox. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 774-777 8030670.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Cloud Bursting Scheduler for cost efficiency

AU - Lee, Young Choon

AU - Lian, Bing

PY - 2017/9/8

Y1 - 2017/9/8

N2 - Clouds have been increasingly adopted due to primarily their elasticity and pay-as-you-go (PAYG) pricing. While many organizations outsource the entire ICT solution to public clouds like Amazon Web Services Elastic Compute Cloud (EC2), others consider occasional workload offloading (cloud bursting) due to various reasons including governance and security. In this paper, we present Cloud Bursting Scheduler (CBS), a new cloud bursting algorithm. CBS explicitly takes into account cost factors of private in-house system (or private cloud) and public cloud. In particular, CBS attempts to optimize the cost to performance ratio by offloading jobs to public cloud explicitly taking into account time-varying electricity rates with private clouds and the timeinvariant rental rate of many public clouds. Based on simulation results obtained using real workload traces, CBS saves costs of running workloads by 55% and 12% compared with costs of cloud sourcing and private cloud, respectively. It also improves resource utilization (to 89%) by judiciously (de)activating inhouse resources and dynamically provisioning cloud resources.

AB - Clouds have been increasingly adopted due to primarily their elasticity and pay-as-you-go (PAYG) pricing. While many organizations outsource the entire ICT solution to public clouds like Amazon Web Services Elastic Compute Cloud (EC2), others consider occasional workload offloading (cloud bursting) due to various reasons including governance and security. In this paper, we present Cloud Bursting Scheduler (CBS), a new cloud bursting algorithm. CBS explicitly takes into account cost factors of private in-house system (or private cloud) and public cloud. In particular, CBS attempts to optimize the cost to performance ratio by offloading jobs to public cloud explicitly taking into account time-varying electricity rates with private clouds and the timeinvariant rental rate of many public clouds. Based on simulation results obtained using real workload traces, CBS saves costs of running workloads by 55% and 12% compared with costs of cloud sourcing and private cloud, respectively. It also improves resource utilization (to 89%) by judiciously (de)activating inhouse resources and dynamically provisioning cloud resources.

KW - cloud bursting

KW - Cloud computing

KW - cost efficiency

KW - energy efficiency

KW - scheduling

UR - http://www.scopus.com/inward/record.url?scp=85032173032&partnerID=8YFLogxK

U2 - 10.1109/CLOUD.2017.112

DO - 10.1109/CLOUD.2017.112

M3 - Conference proceeding contribution

AN - SCOPUS:85032173032

SP - 774

EP - 777

BT - Proceedings - 2017 IEEE 10th International Conference on Cloud Computing

A2 - Fox, Geoffrey C.

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 10th IEEE International Conference on Cloud Computing, CLOUD 2017

Y2 - 25 June 2017 through 30 June 2017

ER -

Cloud Bursting Scheduler for cost efficiency

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this