Cost-Aware Cloud Service Request Scheduling for SaaS Providers

As cloud computing becomes widely deployed, more and more cloud services are offered to end users in a pay-as-you-go manner. Today's increasing number of end user-oriented cloud services are generally operated by Software as a Service (SaaS) providers using rental virtual resources from third-party infrastructure vendors. As far as SaaS providers are concerned, how to process the dynamic user service requests more cost-effectively without any SLA violation is an intractable problem. To deal with this challenge, we first establish a cloud service request model with SLA constraints, and then present a cost-aware service request scheduling approach based on genetic algorithm. According to the personalized features of user requests and the current system load, our approach can not only lease and reuse virtual resources on demand to achieve optimal scheduling of dynamic cloud service requests in reasonable time, but can also minimize the rental cost of the overall infrastructure for maximizing SaaS providers' profits while meeting SLA constraints. The comparison of simulation experiments indicates that our proposed approach outperforms other revenue-aware algorithms in terms of virtual resource utilization, rate of return on investment and operation profit and provides a cost-effective solution for service request scheduling in cloud computing environments.