Cross-Stack Predictive Control Framework for Multicore Real-Time Applications
1 online resource (115 pages) : PDF
University of North Carolina at Charlotte
Many of the next generation applications in entertainment, human computer interaction, infrastructure, security and medical systems are computationally intensive, always-on, and have soft real time (SRT) requirements. While failure to meet deadlines is not catastrophic in SRT systems, missing deadlines can result in an unacceptable degradation in the quality of service (QoS). To ensure acceptable QoS under dynamically changing operating conditions such as changes in the workload, energy availability, and thermal constraints, systems are typically designed for worst case conditions. Unfortunately, such overdesigning of systems increases costs and overall power consumption.In this dissertation we formulate the real-time task execution as a Multiple-Input, Single- Output (MISO) optimal control problem involving tracking a desired system utilization set point with control inputs derived from across the computing stack. We assume that an arbitrary number of SRT tasks may join and leave the system at arbitrary times. The tasks are scheduled on multiple cores by a dynamic priority multiprocessor scheduling algorithm. We use a model predictive controller (MPC) to realize optimal control. MPCs are easy to tune, can handle multiple control variables, and constraints on both the dependent and independent variables. We experimentally demonstrate the operation of our controller on a video encoder application and a computer vision application executing on a dual socket quadcore Xeon processor with a total of 8 processing cores. We establish that the use of DVFS and application quality as control variables enables operation at a lower power operating point while meeting real-time constraints as compared to non cross-stack control approaches. We also evaluate the role of scheduling algorithms in the control of homogeneous and heterogeneous workloads. Additionally, we propose a novel adaptive control technique for time-varying workloads.
Ravindran, ArunJoshi, BharatWilkinson, Anthony
Thesis (Ph.D.)--University of North Carolina at Charlotte, 2014.
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