QoS control for optimality and safety

Combaz, Jacques; Fernandez, Jean-Claude; Lepley, Thierry; Sifakis, Joseph

doi:10.1145/1086228.1086247

Cited by 13 publications

(30 citation statements)

References 10 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The symbolic quality management method improves and extends our previous results (Combaz, Fernandez, Lepley, and Sifakis 2005a;Combaz, Fernandez, Lepley, and Sifakis 2005b).…”

Section: Resultssupporting

confidence: 80%

“…The proposed model (Q-RAM) is event-driven and does not encompass uncertainty about resource consumption, that is, actual resource utilization is determined by the resource allocation algorithms. This paper improves and extends results presented in (Combaz, Fernandez, Lepley, and Sifakis 2005a;Combaz, Fernandez, Lepley, and Sifakis 2005b) in two directions. It proposes a symbolic quality management technique and studies techniques for computing optimal schedules.…”

Section: Introductionsupporting

confidence: 85%

“…We show that the constraint applied by the quality management policy defined in (Combaz, Fernandez, Lepley, and Sifakis 2005b) is satisfied for a given quality, if and only if the quality chosen (at a state) is such that the ideal speed is the least ideal speed exceeding the optimal speed.…”

Section: Introductionmentioning

confidence: 99%

“…In previous papers (Combaz, Fernandez, Lepley, and Sifakis 2005a;Combaz, Fernandez, Lepley, and Sifakis 2005b), we have presented an adaptive method for QoS management in multimedia applications. The method allows adapting the overall system behavior by adequately setting quality level parameters for its actions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Symbolic quality control for multimedia applications

et al. 2008

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The symbolic quality management method improves and extends our previous results (Combaz, Fernandez, Lepley, and Sifakis 2005a;Combaz, Fernandez, Lepley, and Sifakis 2005b).…”

Section: Resultssupporting

confidence: 80%

Section: Introductionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Symbolic quality control for multimedia applications

et al. 2008

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bullets represent incomplete ports and thick lines represent buffered connections (i.e 2 connectors with a buffer component in the middle). We used scheduling policies proposed in [31] to control the execution of the model so as to respect given deadlines and optimize quality. The BIP to Think compiler produces 56 components.…”

Section: Deploymentmentioning

confidence: 99%

A Methodology and Supporting Tools for the Development of Component-Based Embedded Systems

Poulhiès

Pulou

Rippert

et al. 2008

Composition of Embedded Systems. Scientific and Industrial Issues

Self Cite

View full text Add to dashboard Cite

Abstract. The paper presents a methodology and supporting tools for developing component-based embedded systems running on resourcelimited 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.

show abstract

Fine and coarse grained composition and adaptation of spark applications

Shmeis

Jaber

2018

Future Generation Computer Systems

View full text Add to dashboard Cite

QoS control for optimality and safety

Cited by 13 publications

References 10 publications

Symbolic quality control for multimedia applications

Symbolic quality control for multimedia applications

A Methodology and Supporting Tools for the Development of Component-Based Embedded Systems

Fine and coarse grained composition and adaptation of spark applications

Contact Info

Product

Resources

About