QoS control for optimality and safety

We propose a method for fine grain QoS control of real-time applications. The method allows adapting the overall system behavior by adequately setting the quality level parameters of its actions. The objective of the control policy is to meet QoS requirements including three types of properties: 1) safety that is, no deadline is missed; 2) optimality that is, maximization of the available time budget; 3) smoothness of quality levels. The method takes as input a model of the application software, QoS requirements and platform-dependent timing information, and produces a controlled application software meeting the QoS requirements on the target platform. This paper provides a complete formalization of the quality control problem. It proposes a new control management policy ensuring safety, near-optimality and smoothness. It also describes a prototype tool implementing the quality control algorithm and experimental results about its application to a video encoder. Copyright 2005 ACM

Using speed diagrams for symbolic quality management

We present a quality management method for multimedia applications. The method takes as input an application software composed of actions. The execution times of actions are unknown increasing functions of quality level parameters. The method allows the construction of a Quality Manager which computes adequate action quality levels so as to meet QoS requirements for a given platform. These include deadlines for the actions as well as quality maximization and smoothness. We extend and improve results of a previous paper by focusing on the reduction of overhead due to quality management. We propose a symbolic quality management method using speed diagrams, a representation of the system's dynamics. Instead of numerically computing a quality level for each action, the Quality Manager changes action quality levels based on the knowledge of constraints characterizing control relaxation regions. These are sets of states in which quality management for a given number of steps can be relaxed without degrading quality. We provide experimental results for quality management of an MPEG encoder, in particular performance benchmarks for both numeric and symbolic quality management. © 2007 IEEE

A methodology and supporting tools for the development of component-based embedded systems

The paper presents a methodology and supporting tools for developing component-based embedded systems running on resource-limited hardware platforms. The methodology combines two complementary component frameworks in an integrated tool chain: BIP and Think. BIP is a framework for model-based development including a language for the description of heterogeneous systems, as well as associated simulation and verification tools. Think is a software component framework for the generation of small-footprint embedded systems. The tool chain allows generation, from system models described in BIP, of a set of functionally equivalent Think components. From these and libraries including OS services for a given hardware platform, a minimal system can be generated. We illustrate the results by modeling and implementing a software MPEG encoder on an iPod

Assessment of Image Quality vs. Computation Cost for Different Parameterizations of Ultrasound Imaging Pipelines

Ultrasound imaging is a technique widely used in medicine to visualize organs and other body structures, capturing their position, size, morphology and any pathological lesions. Its use is unfortunately limited to specialized centers with trained personnel, and it would be beneficial to expand its applicability to environments like on-the-sheld emergency response and family physician cabinets. This requires the development of new ultrasound platforms that must be faster, lower-power, easier to use, safe and reliable. One of the major challenges to be met is to dynamically manage a myriad of different imaging options and configuration parameters, which impact image quality and computation cost at the same time. Focusing on this challenge, in this paper we first give an overview of ultrasound imaging techniques and of their possible configuration and parametrization options. We then discuss the impact of these options on computation cost and image quality, showing outcomes from a prototype Matlab ultrasound imaging pipeline

Symbolic quality control for multimedia applications

We present a fine grain quality control method for multimedia applications. The method takes as input an application software composed of actions. The execution times of actions are unknown increasing functions of quality level parameters. The method allows the construction of a Controller which computes adequate action schedules and corresponding quality levels, so as to meet QoS requirements for a given platform. These include requirements for safety (action deadlines are met) as well optimality (maximization and smoothness of quality levels). The Controller consists of a Quality Manager and a Scheduler. For each action, the Controller uses a quality management policy for choosing a schedule and quality levels meeting the QoS requirements. The schedule is selected amongst a set of optimal schedules computed by the Scheduler. We extend and improve results of previous papers providing a solid theoretical basis for designing and implementing the Controller. We propose a symbolic quality management method using speed diagrams, a representation of the controlled system's dynamics. Instead of numerically computing a quality level for each action, the Quality Manager changes action quality levels based on the knowledge of constraints characterizing control relaxation regions. These are sets of states in which quality management for a given number of computation steps can be relaxed without degrading quality. We study techniques for efficient computation of optimal schedules. We present experimental results including the implementation of the method and benchmarks for an MPEG4 video encoder. The benchmarks show drastic performance improvement for controlled quality with respect to constant quality. They also show that symbolic quality management allows significant reduction of the overhead with respect to numeric quality management. Finally, using optimal schedules can lead to considerable performance gains. © 2008 Springer Science+Business Media, LLC