Graduate Thesis Or Dissertation
 

Hybrid adaptive controller for resource allocation of real-rate multimedia applications

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/1n79h760s

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  • Multimedia applications such as video streaming and Voice over IP are becoming common today with the tremendous growth of the Internet. General purpose operating systems thus are required to support these applications. These multimedia applications have some timing constraints that need to be satisfied for good quality. For example, video streaming applications require that each video frame be decoded in time to be displayed every 33.3 milliseconds. In order to satisfy these timing requirements, general purpose operating systems need to have fine-grained scheduling. Current general purpose operating systems unfortunately are designed to maximize throughput to serve traditional data-oriented applications and have coarse-grained scheduling and timers. Time Sensitive Linux (TSL), designed by Goel, et al., solves this problem with fine-grained timers and schedulers. The scheduler for TSL is implemented at a very low level. The controller that implements the algorithm for resource allocation is implemented at a higher level. This controller can easily be modified to implement new control algorithms. Successful implementation of resource allocation to satisfy timing constraints of multimedia applications requires two problems to be addressed. First, the resources required by the application to satisfy the timing constraints should not exceed the total available resources in the system. Second, the controller must adapt to changing needs of the applications and allocate enough resources to satisfy the timing constraints of each application over time. The first problem has been addressed elsewhere using intelligent data dropping with TSL. We focus on the second problem in this thesis. We design a proportion-period controller in this thesis for allocating CPU to multimedia video applications with timing constraints. The challenges for the controller design include the coarse granularity of the time-stamp markings of the video frames, the unpredictable decoding completion times of the frames, the large variations in the decoding times of the frames, and the limit of the control actuation to positive values. We set up the problem in a state space. We design a predictive estimating controller to allocate the proportion of the CPU to a thread when its long term error is small. When the decoding process is running behind by more than a certain threshold, we switch to a different controller to drive the error back to a small value. This controller is the solution to a dynamic optimization LQR tracking problem.
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