Article: Enhanced learning classifier to locate data in cloud data centres Journal: International Journal of Metaheuristics (IJMHEUR) 2015 Vol.4 No.2 pp.141 - 158 Abstract: Cloud subscribers would like to verify the location of outsourced data in the cloud data centres to ensure that the availability of data satisfies the service level agreement (SLA). Cloud users may not have access to their outsourced data in the event of operational failures in data centres or occurrence of natural disasters and/or power outages. Recently, IP geolocation techniques have been proposed to locate data files in cloud data centres. However, these techniques exploit relationships between internet delays and distance and are not extensible to incorporate different network measurements, which may be used along with internet delay to improve accuracy. Also, most of the existing techniques have only been validated with one cloud provider (Amazon Web Services). In this paper, we propose an enhanced learning classier IP geolocation algorithm, which incorporates multiple network measurements to improve the accuracy of geolocating data files in data centres in four commercial cloud providers. To demonstrate the accuracy of our approach, we evaluate the performance on Amazon Web Services, Microsoft Azure, Google App Engine, and Rackspace. Our experimental results demonstrate that our approach is geolocating data files accurately, more closely to the true location and also detecting violation of location restrictions. Inderscience Publishers - linking academia, business and industry through research

Title: Enhanced learning classifier to locate data in cloud data centres

Authors: Biswajit Biswal; Sachin Shetty; Tamara Rogers

Addresses: College of Engineering, Tennessee State University, Nashville, TN, USA ' College of Engineering, Tennessee State University, Nashville, TN, USA ' College of Engineering, Tennessee State University, Nashville, TN, USA

Abstract: Cloud subscribers would like to verify the location of outsourced data in the cloud data centres to ensure that the availability of data satisfies the service level agreement (SLA). Cloud users may not have access to their outsourced data in the event of operational failures in data centres or occurrence of natural disasters and/or power outages. Recently, IP geolocation techniques have been proposed to locate data files in cloud data centres. However, these techniques exploit relationships between internet delays and distance and are not extensible to incorporate different network measurements, which may be used along with internet delay to improve accuracy. Also, most of the existing techniques have only been validated with one cloud provider (Amazon Web Services). In this paper, we propose an enhanced learning classier IP geolocation algorithm, which incorporates multiple network measurements to improve the accuracy of geolocating data files in data centres in four commercial cloud providers. To demonstrate the accuracy of our approach, we evaluate the performance on Amazon Web Services, Microsoft Azure, Google App Engine, and Rackspace. Our experimental results demonstrate that our approach is geolocating data files accurately, more closely to the true location and also detecting violation of location restrictions.

Keywords: cloud auditing; IP geolocation; machine learning; learning classifiers; cloud data centres; cloud computing; data location; service level agreements; SLA; data files.

DOI: 10.1504/IJMHEUR.2015.074248

International Journal of Metaheuristics, 2015 Vol.4 No.2, pp.141 - 158

Received: 15 Jan 2015
Accepted: 06 Aug 2015
Published online: 19 Jan 2016 *

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Title: Enhanced learning classifier to locate data in cloud data centres

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