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.
['Biswajit Biswal', 'Sachin Shetty', 'Tamara Rogers']
Enhanced learning classifier to locate data in cloud data centres