IGOD: identification of geolocation of cloud datacenters

The geolocation of data has become a key concern with the evolution of cloud computing. Although data migration is quite common and sometimes essential for the purpose of load balancing or service guarantees, at times, it creates a risk for the user and could even violate the service agreement. A malicious service provider could also relocate the data, which could jeopardize data privacy and security. In this paper, we introduce a novel algorithm called IGOD to geolocate a cloud data center which can also be used for geolocating internet nodes. IGOD efficiently geolocates any target data center with higher accuracy and less cost. It provides audit control and assurance against such cloud storage providers who may move around a customer's data. We analyze and compare IGOD with currently available solutions of geolocating a target. We have used PlanetLab to validate IGOD and establish its cost-effective feature. To do so, we first use our own data collection to geolocate the test data center using emulation and compare IGOD's performance with other schemes. Finally, we use it to geolocate one of the Amazon S3 data centers. Our comparison shows that IGOD provides relatively higher location accuracy and is cost-effective (uses less resources).