A genetic algorithm for service flow management with budget constraint in heterogeneous computing

Heterogeneous computing supply various and scalable resources for many applications requirements. Its structure is based on interconnecting machines with several processing capacity spread over networks. The scientific bioinformatics and many other applications demand service flow processing in which services have dependencies execution. The environments of this computing are suitable for huge computational needs that contains diverse groups of services. Managing and mapping services of service flow to the suitable candidates who provides the service is classified as NP-complete problem. The managing such interdependent services on heterogeneous environments also takes the Quality of Service (QoS) requirements from users into account. This paper firstly proposes a model of service flow management with service cost quality requirement in heterogeneous computing. After that a service flow mapping algorithm named genetic to reduce the consumed cost of an application in heterogeneous environments is proposed. This algorithm gives a robust search technique that allow a soft cost solution to be derived from a huge search space of solutions by inheriting the evolution concepts. The obtained results from the applied experiments prove that genetic can save more than fifteen percent from the cost and also outperforms the compared algorithms in the metric of speedup and SLR.