cloud computing has become more powerful with the inclusion of software-defined networking (SDN) in its environment. In Cloud Data Centers (CDCs), an important research issue is how to forecast and allocate resources efficiently whilst achieving Quality of Service (QoS) of users request with minimal overall power consumption; taking into account the frequent changes in resource requirements. In this paper, we propose a Supervisor Controller-based Software-Defined Cloud Data Center (SC-boSD-CDC)

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... framework for dynamic resource allocation and prediction of cloud computing-based SDN. In this proposed module, Genetic Algorithm (GA) is proposed to deal with the multi-objective problem of dynamically forecasting the utilization of resources in both compute nodes and links bandwidth of network as well as energy consumption in the Cloud Data Center (CDC). Furthermore, a Virtual Machines (VMs) placement algorithm is also proposed to allocate computing resources and routing algorithms to choose the proper bandwidth links between switches; resulting in increased CPU and memory utilization and reduction in overall power consumption.