TY - GEN
T1 - Burstable Instances for Clouds
T2 - 2019 IEEE Conference on Computer Communications, INFOCOM 2019
AU - Jiang, Yuxuan
AU - Shahrad, Mohammad
AU - Wentzlaff, David
AU - Tsang, Danny H.K.
AU - Joe-Wong, Carlee
N1 - Funding Information:
We thank Hedyeh Beyhaghi, Ting-Jung Chang, Zhe Huang, Tri Nguyen, Liang Zheng, and anonymous reviewers for their feedback. This material is based on research sponsored by the NSF under Grants No. CNS-1751075 and CCF-1453112, Air Force Research Laboratory (AFRL) and Defense Advanced Research Projects Agency (DARPA) under agreement No. FA8650-18-2-7846, FA8650-18-2-7852, and FA8650-18-2-7862. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory (AFRL) and Defense Advanced Research Projects Agency (DARPA), the NSF, or the U.S. Government.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Leading cloud providers recently introduced a new instance type named burstable instances to better match the time-varying workloads of tenants and further reduce their costs. In the research community, however, little has been done to understand burstable instances from a theoretical perspective. This paper presents the first unified framework to model, analyze, and optimize the operation of burstable instances. Specifically, we model the resource provisioning of burstable instances in different service classes, identify key performance metrics, and derive the performance given the resource provisioning decisions. We then characterize the equilibrium behind tenants' responses to the prices offered for different burstable instance service classes, taking into account the impact of tenants' actions on the performance achieved by each service class. In addition, we investigate how a cloud provider can leverage the knowledge of this equilibrium to find the prices that maximize its total revenue. Finally, we validate our framework on real traces and demonstrate its usage to price a public cloud.
AB - Leading cloud providers recently introduced a new instance type named burstable instances to better match the time-varying workloads of tenants and further reduce their costs. In the research community, however, little has been done to understand burstable instances from a theoretical perspective. This paper presents the first unified framework to model, analyze, and optimize the operation of burstable instances. Specifically, we model the resource provisioning of burstable instances in different service classes, identify key performance metrics, and derive the performance given the resource provisioning decisions. We then characterize the equilibrium behind tenants' responses to the prices offered for different burstable instance service classes, taking into account the impact of tenants' actions on the performance achieved by each service class. In addition, we investigate how a cloud provider can leverage the knowledge of this equilibrium to find the prices that maximize its total revenue. Finally, we validate our framework on real traces and demonstrate its usage to price a public cloud.
KW - burstable instances
KW - cloud
KW - equilibrium
KW - revenue maximization
UR - http://www.scopus.com/inward/record.url?scp=85068240663&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068240663&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2019.8737634
DO - 10.1109/INFOCOM.2019.8737634
M3 - Conference contribution
AN - SCOPUS:85068240663
T3 - Proceedings - IEEE INFOCOM
SP - 1576
EP - 1584
BT - INFOCOM 2019 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 April 2019 through 2 May 2019
ER -