Application-specific workload shaping in resource-constrained media players

Abstract

Much research in system-level design for multimedia devices is based on analysis with system models, but how insightful are they? System simulation is the prime technique used in computer architecture and embedded system design to explore potential design solutions and validate design choices. Unfortunately, simulation seldomly gives real insight and strong guarantees on the dynamic behavior of a system. On the other hand, existing analytical models could not capture some important attributes of multimedia systems. Consequently, the analysis with such mathematical models is not beneficial for efficient system design. A useful analysis with either simulation or analytical models should provide resource saving techniques. These methods can exploit the key characteristic features of the multimedia streams. The fluid nature in arrivals and inconstant processing requirements of data items are multimedia's inherent characteristic features. But, these characteristic features are predictable. So, the foreseeable properties could be studied to yield techniques that can significantly save on-chip resources.

This thesis proposes techniques to shape multimedia workload so as to effectively utilize on-chip resources such as processor and memory. These shaping techniques attempt to solve the problem in providing guarantees for high-quality media output with minimal on-chip resources. The research approach is to use analytical models and accurately capture the variable characteristics in arrival and execution of items in multimedia streams. Such mathematical models after analysis yield deep-insights to tune certain application parameters. Using this parameter tuning, it is possible to reshape variable media workloads to reduce processing and storage requirements. The central tenet of this parametric tuning is to adapt the workload such that only average or minimum processor cycle time required for every multimedia data item is provided, and not the maximum.

Our results show that choosing the appropriate initial playout delay (after which the video starts) can lead to effective processor utilization. This delay parameter is typically arbitrarily chosen. Instead, we propose to estimate the value of the parameter such that it is sufficient to provide average cycle time required for every data item. This delay, however, could be large and can lead to huge buffer sizes. Hence we propose two-ways to reduce the buffer sizes: (1) in a multi-processor set-up this delay parameter could be redistributed to different processors i.e., apart from the output device, the processors also start after some delay; and (2) allowing tolerable loss in quality. Both these methods show substantial reduction in buffer size. The model we have estimates the delay parameter in all of the above mentioned techniques.

Our mathematical framework fits well to deal with media streams in that it could express variability effortlessly and quickly explore cost-quality trade-offs. These essential attributes of our model substantially brought out the benefits in workload shaping. An important advantage of the workload fitting techniques is from the stochastic models; relaxing constraints that guarantee full output quality yielded significant reductions in processing and memory requirements.