Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Optimization-Based Dynamic Reconfiguration of Real-Time Schedulers With Support for Stochastic Processor Consumption

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Camponogara, E. ; Dept. of Autom. & Syst. Eng., Fed. Univ. of Santa Catarina, Florianópolis, Brazil ; de Oliveira, A.B. ; Lima, G.

The complexity of real-time systems has substantially increased in the past few years regarding both hardware and software aspects. The use of modern sensors, able to capture image and audio data, demands predictable multimedia-like data processing. Moreover, applications like autonomous robots, surveillance, or modern multimedia players may well be characterized by several operation modes, each one associated with light conditions, vision angle, change in user requirements, etc. In this paper, we describe suitable scheduling mechanisms that address these aspects. Application modes are characterized by their required processing bandwidth and benefit values. By using bandwidth reservation schedulers, dynamic reconfiguring scheduling parameters is seen as an optimization problem whose goal is to maximize the overall system benefit subject to schedulability constraints. Two different models for the problem are defined, Discrete and Continuous. The former gives rise to an NP-Hard problem for which efficient approximate solutions are derived. An optimal and polynomial solution to the Continuous model is derived. Both models are then extended to incorporate task execution times described as probability distributions. Making use of this stochastic modeling one is able to dynamically reconfigure the scheduler subject to probabilistic schedulability guarantees. The derived solutions are evaluated by extensive simulation, which indicates the good performance of the proposed reconfiguration mechanisms.

Published in:

Industrial Informatics, IEEE Transactions on  (Volume:6 ,  Issue: 4 )