We present a method for fine grain QoS control of multimedia applications. This method takes as input an application software composed of actions. The execution times are unknown increasing functions of quality level parameters. Our 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 requirements for safety (action deadlines are met) as well as optimality (maximization and smoothness of quality levels). In this paper, we use learning techniques for computation of quality management policies. Given input parameters of the actions, a neural network is used to refine online pre-computed average execution times. Using refine average execution times allows a better control of the application, which leads to a reduction of fluctuations of CPU load. We present experimental results including the implementation of the method and benchmarks for an MPEG4 video encoder.

In the context of embedded systems such as cell-phones, PDA or cars and planes software, optimizations of code are required because of timing and memory constraints imposed. Many problems arise when trying to debug optimized code. One of them is the irrelevance of the mapping between the source code and the optimized target program: the Code Location Problem. This paper proposes a solution to this problem in the case of highly optimized code in the context of embedded systems.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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 junctions 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.

We propose a method for fine grain QoS control of dataflow applications. We assume that the application software is described as the composition of actions (C-functions) with quality level parameters. The method allows a QoS controller to be computed from this description, and also average execution times, worst case execution times and deadlines for its actions. The controller computes dynamically feasible schedules and quality assignments for their actions. Furthermore, the control policy ensures optimal time budget utilization. A prototype tool implementing the method is shown, as well as experimental results for a non trivial example. The results show the interest of fine grain QoS control for video encoders.

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.

This paper presents the tools ALDEBARAN, CESAR, CESAR.ADT and CLEOPATRE which constitute a tool- box for compiling and verifying LOTOS programs. The principles of these tools are described, as well as their performances and limitations. Finally, the formal verification of the ret/REL atomic multicast protocol is given as an example to illustrate the practical use of the tool- box.