Facilitating an ant colony algorithm for multi-objective data-intensive service provision

•This paper investigates bio-inspired algorithms applied in data intensive service provision.•The paper focuses on the multi-objective optimization problems related to QoS and cost.•The paper developed and facilitated ant colony systems to solve the Pareto optimal problems.•This paper also compared ACO with genetic algorithms applied in the same problem.•The paper demonstrated ACO had special features for further research in this area.

The explosion of enormous sources of digital data has led to greater dependence on data-intensive services. Applications based on data-intensive services have become one of the most challenging applications in cloud computing. The service provision, and in particular service composition, will face new challenges as the services and data grow. In this paper, we will evaluate an ant colony system to resolve the multi-objective data-intensive service composition problem. The algorithm for a multi-objective context will get a set of Pareto-optimal solutions considering two objectives at the same time: the total cost and the total execution time of a composite service.