The integration of The Internet of Things and cloud computing has led to the emergenceof new classes of applications ranging from smart healthcare, smart and precision agriculture,smart manufacturing to smart environmental monitoring. The rapid surge in the useof these applications is expected to generate massive amounts of data with differentcharacteristics that are yet not studied. It can be hypothesised that each IoT-enabledapplication may exhibit a diverse range of characteristics that if modelled correctly, maylead to efcient distributed systems. This thesis aims to study the trafc characteristics ofan IoT-enabled healthcare application to build intelligent policies for scalable IoT-cloudsystems by employing the use of workload prediction and load balancing demonstratedon CloudSim Plus platform. The realistic incoming trafc from the SSiO IoT healthcareapplication system is studied, developed and modeled. Workload prediction algorithmsare developed based on ARIMA and SARIMA. The workload prediction algorithms arethen performed and extensively evaluated to select the one with the best performance,which was SARIMA, outperforming ARIMA by 200% on the basis of MAE, RMSE andMAPE. On the basis of the SARIMA prediction for 2 time periods in advance, theload balancing algorithm is preempted to perform horizontal scaling. The results revealthat the load balancer with SARIMA prediction outperform round robin and active loadbalancers for response time and cost by atleast 64% when it comes to worst case scenario.To conclude, a reflection is commented upon about the load balancing for IoT systemsand the directions this could take in the future for a more holistic sustainable approachon real life platforms.