In cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, thereby requiring large-scale cloud data centers. Although, there are many existing energy-aware approaches focusing on minimizing energy consumption while ignoring the SLA violation at the time of a virtual machine (VM) selection from overloaded hosts. Also, they do not consider that the current network traffic causes performance degradation and thus may not really reduce SLA violation under a variety of workloads. In this context, this paper proposes three adaptive models, namely, gradient descent-based regression (Gdr), maximize correlation percentage (MCP), and bandwidth-aware selection policy (Bw), that can significantly minimize energy consumption and SLA violation.