Making cloud computing more efficient

For many companies, moving their web-application servers to the cloud is an attractive option, since cloud-computing services can offer economies of scale, extensive technical support and easy accommodation of demand fluctuations.

Illustration: Christine Daniloff/MIT

But for applications that depend heavily on database queries, cloud hosting can pose as many problems as it solves. Cloud services often partition their servers into ''virtual machines,'' each of which gets so many operations per second on a server's central processing unit, so much space in memory, and the like.

That makes cloud servers easier to manage, but for database-intensive applications, it can result in the allocation of about 20 times as much hardware as should be necessary. And the cost of that overprovisioning gets passed on to customers.

MIT researchers are developing a new system called DBSeer that should help solve this problem and others, such as the pricing of cloud services and the diagnosis of application slowdowns.

At the recent Biennial Conference on Innovative Data Systems Research, the researchers laid out their vision for DBSeer. And in June, at the annual meeting of the Association for Computing Machinery's Special Interest Group on Management of Data (SIGMOD), they will unveil the algorithms at the heart of DBSeer, which use machine-learning techniques to build accurate models of performance and resource demands of database-driven applications.

DBSeer's advantages aren't restricted to cloud computing, either. Teradata, a major database company, has already assigned several of its engineers the task of importing the MIT researchers' new algorithm - which has been released under an open-source license - into its own software.