Dynamic QoS adaptation of inter-dependent task sets in cooperative embedded systems

Due to the growing complexity and dynamism of many embedded application domains (including consumer electronics, robotics, automotive and telecommunications), it is increasingly difficult to react to load variations and adapt the system’s performance in a controlled fashion within an useful and bounded time. This is particularly noticeable when intending to benefit from the full potential of an open distributed cooperating environment, where service characteristics are not known beforehand and tasks may exhibit unrestricted QoS inter-dependencies. This paper proposes a novel anytime adaptive QoS control policy in which the online search for the best set of QoS levels is combined with each user’s personal preferences on their services’ adaptation behaviour. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial solution and effectively optimise the rate at which the quality of the current solution improves as the algorithms are given more time to run, with a minimum overhead when compared against their traditional versions.

[1]  Luís Nogueira,et al.  Dynamic Adaptation of Stability Periods for Service Level Agreements , 2006, 12th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'06).

[2]  Luís Nogueira,et al.  Dynamic QoS-aware coalition formation , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[3]  Luís Nogueira,et al.  Capacity Sharing and Stealing in Dynamic Server-based Real-Time Systems , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[4]  John P. Lehoczky,et al.  Integrated resource management and scheduling with multi-resource constraints , 2004, 25th IEEE International Real-Time Systems Symposium.

[5]  Raktim Bhattacharya,et al.  Anytime Control Algorithm: Model Reduction Approach , 2004 .

[6]  Luís Nogueira,et al.  Shared resources and precedence constraints with capacity sharing and stealing , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[7]  Luís Nogueira,et al.  Iterative Refinement Approach for QOS-Aware Service Configuration , 2006, DIPES.

[8]  Nick Hawes,et al.  ANYTIME DELIBERATION FOR COMPUTER GAME AGENTS , 2004 .

[9]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[10]  Luís Nogueira,et al.  Building adaptable, QoS-aware dependable embedded systems , 2006 .

[11]  Jur P. van den Berg,et al.  Anytime path planning and replanning in dynamic environments , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[12]  Gabi Dreo Rodosek.  Quality Aspects in IT Service Management , 2002, DSOM.

[13]  Shlomo Zilberstein,et al.  Using Anytime Algorithms in Intelligent Systems , 1996, AI Mag..

[14]  Mark Burgess,et al.  On the theory of system administration , 2000, Sci. Comput. Program..

[15]  Robert L. Winkler,et al.  The accuracy of extrapolation (time series) methods: Results of a forecasting competition , 1982 .

[16]  S. Cooper,et al.  Anytime scheduling for real-time embedded control applications , 2004, The 23rd Digital Avionics Systems Conference (IEEE Cat. No.04CH37576).

[17]  Cheng Wang,et al.  Parametric analysis for adaptive computation offloading , 2004, PLDI '04.

[18]  Shlomo Zilberstein,et al.  Operational Rationality through Compilation of Anytime Algorithms , 1995, AI Mag..

[19]  Scott A. Brandt,et al.  Flexible Soft Real-Time Processing in Middleware , 2004, Real-Time Systems.

[20]  R. Brown,et al.  Smoothing, Forecasting, and Prediction of Discrete Time Series , 1965 .

[21]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[22]  Giorgio C. Buttazzo,et al.  Integrating multimedia applications in hard real-time systems , 1998, Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279).