The project aims to propose MAPE based hybrid elasticity management framework on the basis of valuable insights accrued during systematic analysis of relevant literature. Each stage of MAPE process acts independently as a black box in proposed framework, while dealing with neighbouring stages. Thus, being modular in nature; underlying algorithms in any of the stage can be replaced with more suitable ones, without affecting any other stage. The hybrid framework enables proactive and reactive autoscaling approaches to be implemented simultaneously within same system. Proactive approach is incorporated as a core decision making logic on the basis of forecast data, while reactive approach being based upon actual data would act as a damage control measure; activated only in case of any problem with proactive approach. Thus, benefits of both the worlds; pre-emption as well as reliability can be achieved through proposed framework. It uses time series analysis (moving average method / exponential smoothing) and threshold based static rules (with multiple monitoring intervals and dual threshold settings) during analysis and planning phases of MAPE loop, respectively. Mathematical illustration of the framework incorporates multiple parameters namely VM initiation delay / release criterion, network latency, system oscillations, threshold values, smart kill etc. The research concludes that recommended parameter settings primarily depend upon certain autoscaling objective and are often conflicting in nature. Thus, no single autoscaling system with similar values can possibly meet all objectives simultaneously, irrespective of reliability of an underlying framework. The project successfully implements complete cloud infrastructure and autoscaling environment over experimental platforms i-e OpenStack and CloudSim Plus. In nutshell, the research provides solid understanding of autoscaling phenomenon, devises MAPE based hybrid elasticity management framework and explores its implementation potential over OpenStack and CloudSim Plus.

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.