In dynamic resource allocation systems, servers are moved between pools when overloading is detected. In this work, we investigate the impact to such systems of combining three adaptive monitoring techniques. First we employ two well known switching policies -- the Proportional Switching Policy (PSP) and the Bottleneck Aware Switching Policy (BSP) -- to move servers between server pools as appropriate. Second we use a meta-forecasting technique to predict the movement in future system workload. Third, we use a Dynamic Active Window Model (DAWM), which defines the period over which workload data is analysed. We have previously shown that request servicing capability can be improved by as much as 40\% when the right combination of dynamic server switching and workload forecasting are used. This extended model shows that a further 51.5\% improvement can be achieved when the switching server policy, meta-forecasting and dynamic active window management are employed together over a real-world workload based on Internet traces.