NSGA-II based energy efficient scheduling in real-time embedded systems for tasks with deadlines and execution times as type-2 fuzzy numbers

In real-time systems, energy efficiency is a vital issue since usually such systems run on battery and are remotely placed. Another important aspect of these systems is their capabilities to produce timely results. In this paper, we have reported how these two conflicting issues of embedded real-time systems can be addressed with the help of an efficient evolutionary algorithm viz. NSGA-II (Non-Dominated Sorting Algorithm-II). Moreover, during the system design time, the timing parameters in real-time systems are all designers' approximation since those can hardly be predicted before runtime. This means that there exists some uncertainty and hence it is appropriate to consider fuzzy numbers to model these timing parameters. Although type-I fuzzy numbers were used by a number of researchers to model the timing parameters of real-time embedded systems, they suffer from the interpretability issues. To address this, we thus propose here to consider type-2 fuzzy numbers to model real-time tasks timing parameters. Few numerical examples are included to demonstrate our proposed technique.

[1]  Binoy Ravindran,et al.  Energy-efficient, utility accrual scheduling under resource constraints for mobile embedded systems , 2004, EMSOFT '04.

[2]  Woei Wan Tan,et al.  Towards an efficient type-reduction method for interval type-2 fuzzy logic systems , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[3]  Kaushal K. Shukla,et al.  Real-time task scheduling with fuzzy uncertainty in processing times and deadlines , 2008, Appl. Soft Comput..

[4]  W. Marsden.  I and J , 2012 .

[5]  F. Terrier,et al.  Fuzzy calculus applied to real time scheduling , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[6]  A. Sklar,et al.  Triangle Inequalities in a Class of Statistical Metric Spaces , 1963 .

[7]  Athanasios V. Vasilakos,et al.  ASAFES2: a novel, neuro-fuzzy architecture for fuzzy computing, based on functional reasoning , 1996, Fuzzy Sets Syst..

[8]  Jorge Santos,et al.  Power saving and fault-tolerance in real-time critical embedded systems , 2009, J. Syst. Archit..

[9]  H. Ishibuchi,et al.  Multiobjective fuzzy scheduling with the OWA operator for handling different scheduling criteria and different job importance , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[10]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[11]  John Yen,et al.  A fuzzy rule-based approach to real-time scheduling , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[12]  Witold Pedrycz,et al.  Energy-efficient differentiated coverage of dynamic objects using an improved evolutionary multi-objective optimization algorithm with fuzzy-dominance , 2012, 2012 IEEE Congress on Evolutionary Computation.

[13]  Jian-Jia Chen,et al.  Energy-Efficient Scheduling in Nonpreemptive Systems With Real-Time Constraints , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[15]  Hiroaki Ishii,et al.  Two scheduling problems with fuzzy due-dates , 1992 .

[16]  Daniel Mossé,et al.  Adaptive scheduling server for power-aware real-time tasks , 2004, TECS.

[17]  Rami G. Melhem,et al.  Maximizing rewards for real-time applications with energy constraints , 2003, TECS.

[18]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[19]  B. Schweizer,et al.  Statistical metric spaces. , 1960 .

[20]  G. Manimaran,et al.  Energy-Aware Scheduling of Real-Time Tasks in Wireless Networked Embedded Systems , 2007, 28th IEEE International Real-Time Systems Symposium (RTSS 2007).

[21]  Rami G. Melhem,et al.  Maximizing the system value while satisfying time and energy constraints , 2003, IBM J. Res. Dev..

[22]  Susanne Albers,et al.  Energy-efficient algorithms , 2010, Commun. ACM.

[23]  Krishnendu Chakrabarty,et al.  Real-time task scheduling for energy-aware embedded systems , 2001, J. Frankl. Inst..

[24]  Lotfi A. Zadeh,et al.  Is there a need for fuzzy logic? , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[25]  N. N. Karnik,et al.  Introduction to type-2 fuzzy logic systems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[26]  Marin Litoiu,et al.  Real-time task scheduling with fuzzy deadlines and processing times , 2001, Fuzzy Sets Syst..

[27]  Anantha Chandrakasan,et al.  Energy Efficient Real-Time Scheduling , 2001, ICCAD.

[28]  Robert Ivor John,et al.  Type 2 Fuzzy Sets: An Appraisal of Theory and Applications , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[29]  A.P. Chandrakasan,et al.  Energy efficient real-time scheduling [microprocessors] , 2001, IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281).

[30]  Kaushal K. Shukla,et al.  Real-time scheduling of periodic tasks with processing times and deadlines as parametric fuzzy numbers , 2009, Appl. Soft Comput..

[31]  Alireza Ejlali,et al.  A Comparative Study of System-Level Energy Management Methods for Fault-Tolerant Hard Real-Time Systems , 2011, IEEE Transactions on Computers.

[32]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.