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Summary form only given. Modern programming languages are provided with a mechanism for automatic memory management called garbage collection. A lot of researches had been introduced in this area. On the other hand, some problems existed when applying such languages to the real-time applications. Among these problems, there was the threat of predictability and schedulability of hard real-time tasks. As a solution, different concurrent garbage collection scheduling strategies had been proposed. Unfortunately, these concurrent garbage collectors suffered from redundant usage of system memory. A latter algorithm that minimizes the worst-case system memory requirement with the schedulability of tasks not jeopardized is the deferrable server based garbage collector scheduling strategy. The deferrable server based garbage collection had been proved to surpass all other garbage collection scheduling strategies in minimizing system memory requirements under the worst-case conditions. In this paper, the performance of the deferrable server based garbage collector is explored under the actual real-time working environment. It also investigates the selection of thresholds to further reduce the memory requirement by using higher capacities of the deferrable server task. The simulation results demonstrate that the deferrable sever based garbage collector achieves better performance under actual real-time environment than other garbage collection algorithms. It also shows that using higher values for the server capacities can lead to better memory usage if a proper value of threshold is chosen.