DocumentCode :
2015534
Title :
Optimal bidding in spot instance market
Author :
Song, Yang ; Zafer, Murtaza ; Lee, Kang-Won
Author_Institution :
IBM T. J. Watson Res. Center, Hawthorne, NY, USA
fYear :
2012
fDate :
25-30 March 2012
Firstpage :
190
Lastpage :
198
Abstract :
Amazon introduced Spot Instance Market to utilize the idle resources of Amazon Elastic Compute Cloud (EC2) more efficiently. The price of a spot instance changes dynamically according to the current supply and demand for cloud resources. Users can bid for a spot instance and the job request will be granted if the current spot price falls below the bid, whereas the job will be terminated if the spot price exceeds the bid. In this paper, we investigate the problem of designing a bidding strategy from a cloud service broker´s perspective, where the cloud service broker accepts job requests from cloud users, and leverages the opportunistic yet less expensive spot instances for computation in order to maximize its own profit. In this context, we propose a profit aware dynamic bidding (PADB) algorithm, which observes the current spot price and selects the bid adaptively to maximize the time average profit of the cloud service broker. We show that our bidding strategy achieves a near-optimal solution, i.e., (1-∈) of the optimal solution to the profit maximization problem, where ∈ can be arbitrarily small. The proposed dynamic bidding algorithm is self-adaptive and requires no a priori statistical knowledge on the distribution of random job sizes from cloud users.
Keywords :
cloud computing; electronic commerce; optimisation; resource allocation; supply and demand; Amazon elastic compute cloud resources; PADB algorithm; cloud service broker perspective; cloud users; near-optimal solution; optimal bidding; profit aware dynamic bidding algorithm; profit maximization problem; random job size distribution; self-adaptive dynamic bidding algorithm; spot instance market; spot price; statistical knowledge; supply and demand; Cloud computing; Heuristic algorithms; History; Kernel; Resource management; Stochastic processes; Tin;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
INFOCOM, 2012 Proceedings IEEE
Conference_Location :
Orlando, FL
ISSN :
0743-166X
Print_ISBN :
978-1-4673-0773-4
Type :
conf
DOI :
10.1109/INFCOM.2012.6195567
Filename :
6195567
Link To Document :
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