Presently, digital continuous media (CM) are well established as an integral part of many applications. Scant attention has been paid to servers that can record such streams in real time. However, more and more devices produce direct digital output streams. Hence, the need arises to capture and store these streams with an efficient recorder that can handle both recording and playback of many streams simultaneously and provide a central repository for all data. Because of the continuously decreasing cost of memory, more and more memory is available on a large scale recording system. Unlike most previous work that focuses on how to minimize the server buffer size, this paper investigates how to effectively utilize the additional available memory resources in a recording system. We propose an effective resource management framework that has two parts: (1) a dynamic memory allocation strategy, and (2) a deadline setting policy (DSP) that can be applied consistently to both playback and recording streams, satisfying the timing requirements of CM, and also ensuring fairness among different streams. Furthermore, to find the optimal memory configuration, we construct a probability model based on the classic M/G/1 queueing model and the recently developed Real Time Queueing Theory (RTQT). Our model can predict (a) the missed deadline probability of a playback stream, and (b) the blocking probability of recording streams. The model is applicable to admission control and capacity planning in a recording system.