SEEC: A Framework for Self-aware Management of Multicore Resources

This paper presents SEEC, a self-aware programming model, designed to reduce programming effort in modern multicore systems. In the SEEC model, application programmers specify application goals and progress, while systems programmers separately specify actions system software and hardware can take to affect an application (e.g. resource allocation). The SEEC runtime monitors applications and dynamically selects actions to meet application goals optimally (e.g. meeting performance while minimizing power consumption). The SEEC runtime optimizes system behavior for the application rather than requiring the application programmer to optimize for the system. This paper presents a detailed discussion of the SEEC model and runtime as well as several case studies demonstrating their benefits. SEEC is shown to optimize performance per Watt for a video encoder, find optimal resource allocation for an application with complex resource usage, and maintain the goals of multiple applications in the face of environmental fluctuations

Probabilistic admission control to govern real-time systems under overload

Existing real-time research focuses on how to formulate, model and enforce timeliness guarantees for task sets whose correctness has a temporal aspect. However, the resulting systems often exhibit poor resource utilization due to the resource scheduler reserving more resources than required in order to ensure that admitted schedules can be satisfied under worst case conditions. Weakening the guarantees leads to the known concepts of firm and soft real-time tasks, but we think the paradigm needs to be shifted further, reifying efficient utilization. With Quality-Assuring Scheduling (QAS) we presented such an algorithm. However, its practical applicability is restricted to uniform and harmonic periods, due to its complexity for arbitrary periods. To overcome this limitation, we introduce Quality-Rate-Monotonic Scheduling (QRMS), which, although slightly more pessimistic, is less complex compared to QAS. The admission control is again based on a probabilistic model to ensure that a requested fraction of jobs is successfully executed. Thus, the amount of missed deadlines can be externally controlled, even in sustained overload situations. 1

Practical Real-Time with Look-Ahead Scheduling

In my dissertation, I present ATLAS — the Auto-Training Look-Ahead Scheduler. ATLAS improves service to applications with regard to two non-functional properties: timeliness and overload detection. Timeliness is an important requirement to ensure user interface responsiveness and the smoothness of multimedia operations. Overload can occur when applications ask for more computation time than the machine can offer. Interactive systems have to handle overload situations dynamically at runtime. ATLAS provides timely service to applications, accessible through an easy-to-use interface. Deadlines specify timing requirements, workload metrics describe jobs. ATLAS employs machine learning to predict job execution times. Deadline misses are detected before they occur, so applications can react early.:1 Introduction 2 Anatomy of a Desktop Application 3 Real Simple Real-Time 4 Execution Time Prediction 5 System Scheduler 6 Timely Service 7 The Road Ahead Bibliography Inde