Enhanced ant colony algorithm for cost-aware data-intensive service provision

Huge collections of data have been created in recent years. Cloud computing has been widely accepted as the nextgeneration solution to addressing data-proliferation problems. Because of the explosion in digital data and the distributed nature of the cloud, as well as the increasingly large number of providers in the market, providing efficient cost models for composing dataintensive services will become central to this dynamic market. The location of users, service composers, service providers, and data providers will affect the total cost of service provision. Different providers will need to make decisions about how to price and pay for resources. Each of them wants to maximize its profit as well as retain its position in the marketplace. Based on our earlier work, this paper addresses the effect of data intensity and the communication cost of mass data transfer on service composition, and proposes a service selection algorithm based on an enhanced ant colony system for data-intensive service provision. In this paper, the data-intensive service composition problem is modeled as an AND/OR graph, which is not only able to deal with sequence relations and switch relations, but is also able to deal with parallel relations between services. In addition, the performance of the service selection algorithm is evaluated by simulations.