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 SaaS (Software as a Service) 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 also can 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.

With the rapid development of cloud computing, more and more cloud services are provided to users. Faced with multiple cloud services, how to scientifically predict and assess the performance of cloud service is an imperative task. To cope with the challenge, an prediction scheme via JOGM(1,1) model is proposed based on grey system theory, in which the performance of cloud service is quantified as the response time. Thus cloud users and the third-party institutes of cloud service performance evaluation can predict and assess the performance of cloud service as accurate as possible. In return, this will contribute to the cloud service provider selection for better performance on behalf of cloud consumers. The simulation results show that the proposed scheme has higher prediction precision compared with classic GM(1,1) model and weighted moving average model, which verifies the effectiveness of the proposed prediction scheme and the feasibility of forecasting the performance of cloud service employing grey system theory.