Real-time computing is an enabling technology for many current and future application areas. Many future generation real-time systems are expected to be highly dynamic and operate in fault-prone non-deterministic environments under strict timing constraints. Therefore, these systems need to be robust while delivering high real-time performance. This motivates the need for robust resource management mechanisms that dynamically address real-time requirements and provide graceful degradation in the presence of uncertainty. Despite the significant body of results in resource management in real-time systems, most of them are based on "open-loop" strategies which are effective when the workload can be accurately modeled. These schemes are not effective for many real world problems wherein the workload cannot be accurately modeled. Thus, there is a need for efficient architectures for resource management where predictable performance guarantees can be obtained in the presence of uncertainty.

Feedback control theory has been central to modeling systems operating in uncertain environments. In the past few decades, this theory has made impressive strides in this direction. Correct adaptation as illustrated by feedback control theory will yield significant dividends with respect to robustness.

It is therefore crucial to make use of this theory for resource management purposes. The main goal of our research is to develop a robust real-time resource management methodology employing feedback control strategies. Towards achieving this goal, our research addresses the following issues:

Develop a comprehensive understanding of the interplay between resource management theory and control theory.

Identify the key elements of the resource management methodology, which will be pivotal in meeting the challenge of providing predictable performance in the presence of uncertainty.

Develop a framework to characterize and analyze performance and uncertainty in a precise manner.

Develop robust scheduling algorithms using feedback that apply to many important real-time systems.