TY - GEN
T1 - How to bid the cloud
AU - Zheng, Liang
AU - Joe-Wong, Carlee
AU - Tan, Chee Wei
AU - Chiang, Mung
AU - Wang, Xinyu
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/8/17
Y1 - 2015/8/17
N2 - Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Amazon sets the spot price dynamically and accepts user bids above this price. Jobs with lower bids (including those already running) are interrupted and must wait for a lower spot price before resuming. Spot pricing thus raises two basic questions: how might the provider set the price, and what prices should users bid? Computing users' bidding strategies is particularly challenging: higher bid prices reduce the probability of, and thus extra time to recover from, interruptions, but may increase users' cost. We address these questions in three steps: (1) modeling the cloud provider's setting of the spot price and matching the model to historically offered prices, (2) deriving optimal bidding strategies for different job requirements and interruption overheads, and (3) adapting these strategies to MapReduce jobs with master and slave nodes having different interruption overheads. We run our strategies on EC2 for a variety of job sizes and instance types, showing that spot pricing reduces user cost by 90% with a modest increase in completion time compared to on-demand pricing.
AB - Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Amazon sets the spot price dynamically and accepts user bids above this price. Jobs with lower bids (including those already running) are interrupted and must wait for a lower spot price before resuming. Spot pricing thus raises two basic questions: how might the provider set the price, and what prices should users bid? Computing users' bidding strategies is particularly challenging: higher bid prices reduce the probability of, and thus extra time to recover from, interruptions, but may increase users' cost. We address these questions in three steps: (1) modeling the cloud provider's setting of the spot price and matching the model to historically offered prices, (2) deriving optimal bidding strategies for different job requirements and interruption overheads, and (3) adapting these strategies to MapReduce jobs with master and slave nodes having different interruption overheads. We run our strategies on EC2 for a variety of job sizes and instance types, showing that spot pricing reduces user cost by 90% with a modest increase in completion time compared to on-demand pricing.
KW - Cloud pricing
KW - Optimization
KW - Spot instance
UR - http://www.scopus.com/inward/record.url?scp=84962295418&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962295418&partnerID=8YFLogxK
U2 - 10.1145/2785956.2787473
DO - 10.1145/2785956.2787473
M3 - Conference contribution
AN - SCOPUS:84962295418
T3 - SIGCOMM 2015 - Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication
SP - 71
EP - 84
BT - SIGCOMM 2015 - Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication
PB - Association for Computing Machinery, Inc
T2 - ACM Conference on Special Interest Group on Data Communication, SIGCOMM 2015
Y2 - 17 August 2015 through 21 August 2015
ER -