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Geo-distributed data centers (GDCs) house computing resources and provide cloud services across the world. As cloud computing flourishes, energy consumption and electricity cost for powering servers of GDCs also soar high. Energy consumption and cost minimization for GDCs has become the main challenge for the cloud service providers. This paper proposes a resource management framework that accomplishes resource demand prediction, ensuring service level objective (SLO), electricity price prediction, and energy-efficient and cost-effective resource allocation through GDCs. This paper also proposes an energy-efficient and cost-effective resource allocation (EECERA) algorithm which deploys energy efficiency factors and incorporates the electricity price diversity of GDCs. Extensive evaluations were performed based on real-world workload traces and real-life electricity price data of GDC locations. The evaluation results showed that the resource demand prediction model could predict the right amount of dynamic resource demand while achieving SLO, and also, the electricity price prediction model could provide promising accuracy. The performances of resource allocation algorithms were evaluated on CloudSim. This work contributes to minimizing the energy consumption and the average turnaround time taken to complete the task and offers cost-saving. HIGHLIGHTS SLO guaranteed, energy-efficient and cost-effective resource management framework Energy-efficient and cost-effective resource allocation (EECERA) algorithm Extensive evaluations based on real-world workload traces and real-life electricity price data of GDC locations Performances of resource allocation algorithms evaluated on CloudSim Minimizing the energy consumption and the average turnaround time taken to complete the task and also cost-saving GRAPHICAL ABSTRACT