The focus of this dissertation is adaptive CPU-budget allocation for periodic soft-real-time applications. The presented algorithms are developed in the context of a power-management framework. First, the prediction-based bandwidth scheduler (PBS) is developed. This algorithm is designed to adapt CPU-budget allocations at a faster rate than previous adaptive algorithms. Simulation results are presented to demonstrate that this approach allows for a faster response to under allocations than previous algorithms. A second algorithm is presented called Two-Stage Prediction (TSP) that improves on the PBS algorithm. Specifically, a more sophisticated algorithm is used to predict execution times and a stronger guarantee is provided on the timeliness of jobs. Implementation details and experimental results are presented for both the PBS and TSP algorithms. An abstraction is presented called virtual instruction count (VIC) to allow for more efficient budget allocation in power-managed systems. Power management decisions affect job-execution times. VIC is an abstract measure of computation that allows budget allocations to be made independent of power-management decisions. Implementation details and experimental results are presented for a VIC-based budget mechanism. Finally, a power-management framework is presented called the linear adaptive models based system (LAMbS). LAMbS is designed to minimize power consumption while honoring budget allocations specified in terms of VIC.

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Computer systems now run drastically different workloads than they did two decades ago. The enormous advances in hardware power, such as processor speed, memory and storage capacity, and network bandwidth, enable them to run new kinds as well as a large number of applications simultaneously. Software technologies, such as garbage collection and multi-threading, also reshape applications and their behaviors, introducing more challenges to system resource management. However, existing general-purpose operating systems do not provide adequate support for these modern applications. These operating systems were designed over two decades ago, when garbage-collected applications were not prevalent and users interacted with systems using consoles and command lines, rather than graphical user interfaces. As a result, they fail to allow necessary coordinations among resource management components to ensure consistent performance guarantees. For example, garbage-collected applications cannot adjust themselves to maintain high throughput under dynamic memory pressure, simply because existing virtual memory managers do not collect and expose enough information to them. Furthermore, despite the increasing demand of supporting co-existing interactive applications in desktop environment, resource managers (especially memory and disk I/O) mostly focus on optimizing throughput. They each work independently, ignoring the response time requirements that the CPU scheduler attempts to satisfy. Consequently, pressure on any of these resources can significantly degrade application responsiveness. In order to deliver robust performance to these modern applications, an operating system has to coordinate its resource managers (e.g., CPU, memory, and disk I/O), as well as cooperate with resource managers in the user space, such as the garbage collector and the thread manger. To support garbage-collected applications, we present CRAMM, a system that enables them to predict an appropriate heap size using information supplied by the underlying operating system, allowing them to maintain high throughput in the face of changing memory pressure. To support highly interactive workloads, we present Redline, a system that manages CPU, memory, and disk I/O in an integrated manner. It uses lightweight specifications to drive CPU scheduling and to coordinate memory and disk I/O management to serve the needs of interactive applications. Such coordination enables it to maintain responsiveness in the face of extreme resource contention, without sacrificing resource utilization. We also show that Redline can be used to support response time sensitive multi-threaded server applications. Our experiences and extensive experiments show that we can coordinate resource managers, both inside and outside the operating system, efficiently without destroying the modularity of the existing system. Such coordination prevents resource managers from working at cross purposes, and dramatically improve the performance of applications when facing heavy resource contention, sometimes by orders of magnitude.