Paper Info
Title
A Comparative Evaluation of Algorithms for Auction-Based Cloud Pricing Prediction
Abstract
Nowadays, cloud computing providers offer idle resources through an auction-based system in order to maximize resource utilization and economical revenue. Cloud computing consumers have the opportunity to take advantage of the resources offered at very low spot price in exchange for lower reliability in the provision of these resources. In this context, the Spot Price Prediction (SPP) is a well studied problem mainly formulated as a time series prediction, with particularities of auction-based cloud markets. This work presents a comparative evaluation of three different well-known prediction algorithms, applied for the first time to the SPP problem, against astate-of-the-art Three-Layer Perceptron (TLP) algorithm. In order to measure the accuracy of the evaluated algorithms, the following error metrics were considered: (1) Mean-Squared Error (2) Maximum Positive Error and (3) Mean Positive Error. Experimental results indicate that the Support Vector Poly Kernel Regression (SMOReg) algorithm outperforms other evaluated algorithms for the SPP problem, improving probabilities of obtaining resources in a highly dynamic spot market.
Year
DOI
Venue
2016
10.1109/IC2E.2016.45
2016 IEEE International Conference on Cloud Engineering (IC2E)
Keywords
Field
DocType
Spot Instances,Auction-based Resource Provisioning,Cloud Computing,Cloud Pricing,Prediction
Revenue,Time series,Data mining,Spot contract,Computer science,Support vector machine,Algorithm,Perceptron,Kernel regression,Cloud computing,Spot market
Conference
ISSN
Citations 
PageRank 
2373-3845
3
0.39
References 
Authors
13
3
Name
Order
Citations
PageRank
Sara Arevalos Flor150.77
Fabio López Pires2725.81
Benjamín Barán357247.27