Date Added: Dec 2009
Cloud computing provides customers the illusion of infinite computing resources which are available from anywhere, anytime, on demand. Computing at such an immense scale requires a framework that can support extremely large datasets housed on clusters of commodity hardware. Two examples of such frameworks are Google's MapReduce and Microsoft's Dryad. First the authors discuss implementation details of these frameworks and drawbacks where future work is required. Next they discuss the challenges of computing at such a large scale. In particular, they focus on the security issues which arise in the cloud: the confidentiality of data, the retrievability and availability of data, and issues surrounding the correctness and confidentiality of computation executing on third party hardware.