Cost Optimization of Real-Time Cloud Applications Using Developmental Genetic Programming

This paper presents the methodology for the cost optimization of real-time applications, that are conformable to the Infrastructure as a Service (IaaS) model of cloud computing. We assume, that functions of applications are specified as a set of distributed echo algorithms with soft real-time constraints. Then our methodology schedules all tasks on available cloud infrastructure, minimizing the total costs of the IaaS services, while guaranteeing the required level of the quality of services, as far as real-time requirements are concerned. It takes into account limited bandwidth of communication channels as well as the limited computation power of server nodes. The cost is optimized using the method based on the developmental genetic programming. The method reduces the cost of hiring the cloud infrastructure by sharing cloud resources between applications. We also present experimental results, that show the benefits of using our methodology.