Cloud federation allows collaboration among multiple cloud service providers (CSP), to share their idle computing resources with peers CSP to gain extra profit. They are offering seamless resource sharing with enhanced scalability and availability of resources. Hence, the cloud federation has overcome the many limitations of CSPs, during the sudden high demand for resources to maintain the quality of services (QoS). However, the presence of CSPs is an overloaded job that does not immediately respond to every job, and sometimes a high amount of store pending jobs are in the queue and dropped. Therefore, to guarantee the job is executed. It will be necessary to design a framework for a balanced load distribution of jobs. In this paper, we proposed the adaptive load-balancing framework (ALBF). The main objective of this research is to ensure fair and balanced job distribution of resources CSPs in the federation environment. Reduce pending jobs and response time, decrease job drop rates, and maximize the profit and throughput of the system. The proposed adaptive load-balancing framework (ALBF) has been compared with existing mechanisms. Simulation results analysis shows ALBF performance better than existing mechanisms in terms of pending jobs, dropped jobs, response time, profit, and system throughput.