TY - GEN
T1 - On the viability of a cloud virtual service provider
AU - Zheng, Liang
AU - Joe-Wong, Carlee
AU - Brinton, Christopher G.
AU - Tan, Chee Wei
AU - Ha, Sangtae
AU - Chiang, Mung
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/6/14
Y1 - 2016/6/14
N2 - Cloud service providers (CSPs) often face highly dynamic user demands for their resources, which can make it difficult for them to maintain consistent quality-of-service. Some CSPs try to stabilize user demands by offering sustaineduse discounts to jobs that consume more instance-hours per month. These discounts present an opportunity for users to pool their usage together into a single \job." In this paper, we examine the viability of a middleman, the cloud virtual service provider (CVSP), that rents cloud resources from a CSP and then resells them to users. We show that the CVSP's business model is only viable if the average job runtimes and thresholds for sustained-use discounts are suffciently small; otherwise, the CVSP cannot simultaneously maintain low job waiting times while qualifying for a sustained-use discount. We quantify these viability conditions by modeling the CVSP's job scheduling and then use this model to derive users' utility-maximizing demands and the CVSP's profit-maximizing price, as well as the optimal number of instances that the CVSP should rent from the CSP. We verify our results on a one-month trace from Google's production compute cluster, through which we first validate our assumptions on the job arrival and runtime distributions, and then show that the CVSP is viable under these workload traces. Indeed, the CVSP can earn a positive profit without significantly impacting the CSP's revenue, indicating that the CSP and CVSP can coexist in the cloud market.
AB - Cloud service providers (CSPs) often face highly dynamic user demands for their resources, which can make it difficult for them to maintain consistent quality-of-service. Some CSPs try to stabilize user demands by offering sustaineduse discounts to jobs that consume more instance-hours per month. These discounts present an opportunity for users to pool their usage together into a single \job." In this paper, we examine the viability of a middleman, the cloud virtual service provider (CVSP), that rents cloud resources from a CSP and then resells them to users. We show that the CVSP's business model is only viable if the average job runtimes and thresholds for sustained-use discounts are suffciently small; otherwise, the CVSP cannot simultaneously maintain low job waiting times while qualifying for a sustained-use discount. We quantify these viability conditions by modeling the CVSP's job scheduling and then use this model to derive users' utility-maximizing demands and the CVSP's profit-maximizing price, as well as the optimal number of instances that the CVSP should rent from the CSP. We verify our results on a one-month trace from Google's production compute cluster, through which we first validate our assumptions on the job arrival and runtime distributions, and then show that the CVSP is viable under these workload traces. Indeed, the CVSP can earn a positive profit without significantly impacting the CSP's revenue, indicating that the CSP and CVSP can coexist in the cloud market.
UR - http://www.scopus.com/inward/record.url?scp=84978681949&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978681949&partnerID=8YFLogxK
U2 - 10.1145/2896377.2901452
DO - 10.1145/2896377.2901452
M3 - Conference contribution
AN - SCOPUS:84978681949
T3 - SIGMETRICS/ Performance 2016 - Proceedings of the SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Science
SP - 235
EP - 248
BT - SIGMETRICS/ Performance 2016 - Proceedings of the SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Science
PB - Association for Computing Machinery, Inc
T2 - 13th Joint International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS / IFIP Performance 2016
Y2 - 14 June 2016 through 18 June 2016
ER -