NDLI: On Estimating Minimum Bids for Amazon EC2 Spot Instances

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On Estimating Minimum Bids for Amazon EC2 Spot Instances

Content Provider	ACM Digital Library
Author	Kowalczyk, Ryszard Lumpe, Markus Vo, Quoc Bao Chhetri, Mohan Baruwal
Abstract	Consumers can realize significant cost savings by procuring resources from computational spot markets such as Amazon Elastic Compute Cloud (EC2) Spot Instances. They can take advantage of the price differentials across time slots, regions, and instance types to minimize the total cost of running their applications on the cloud. However, Spot markets are inherently volatile and dynamic, as a consequence of which Spot prices change continuously. As such, prospective bidders can benefit from intelligent insights into the Spot market dynamics that can help them make more informed bidding decisions. To enable this, we propose a descriptive statistics approach for the analysis of Amazon EC2 Spot markets to detect typical pricing patterns including the presence of seasonal components, extremes and trends. We use three statistical measures -- the Gini coefficient, the Theil index, and the exponential weighted moving average. We also devise a model for estimating minimum bids such that the Spot instances will run for specified durations with a probability greater than a set value based on different look back periods. Experimental results show that our estimation yields on average a bidding strategy that can reliably secure an instance at least 80% of the time at minimum target guarantee between 50% and 95%.
Starting Page	391
Ending Page	400
Page Count	10
File Format	PDF
ISBN	9781509066100
DOI	10.1109/CCGRID.2017.76
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-14
Access Restriction	Subscribed
Subject Keyword	Bid price estimation Spot market Descriptive statistics Gini coefficient Thiel index
Content Type	Text
Resource Type	Article

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