Abstract
Multiprocessors have emerged as a powerful computing means for running real-time applications, especially where a uniprocessor system would not be sufficient enough to execute all the tasks. The high performance and reliability of multiprocessors have made them a powerful computing resource. Such computing environment requires an efficient algorithm to determine when and on which processor a given task should be executed. In multiprocessor systems, an efficient scheduling of parallel tasks onto the processors is known to be NP- Hard problem. With growing of applications of the embedded system technology, energy efficiency and timing requirement are becoming important issues for designing real time embedded systems. This paper focuses the combinational optimization problem, namely, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint for independent parallel tasks on multiprocessor computers. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other and vice versa. The performance of the proposed algorithm with optimal solution is validated analytically and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Burd, T.D., Brodersen, R.W.: Energy efficient cmos microprocessor design. In: Proc. of The HICSS Conference, Maui, Hawaii, pp. 288–297 (1995)
Krishna, C.M., Lee, Y.H.: Voltage clock scaling adaptive scheduling techniques for low power in hard real-time systems. In: Proc. of The 6th IEEE Real-Time Technology and Applications Symposium (RTAS 2000), Washington D.C (2000)
Aydin, H., Melhem, R., Mossé, D., Mejia-Alvarez, P.: Dynamic and aggressive scheduling techniques for power-aware real-time systems. In: Proc. of the 22nd IEEE Real-Time Systems Symposium, London, UK (2001)
Trescases, O., Ng, W.T.: Variable Output, Soft-Switching DC/DC Converter for VLSI Dynamic Voltage Scaling Power Supply Applications. In: 35th Annual IEEE Power Electronics Specialists Conference, pp. 4149–4155 (2004)
Benten, T., Sait, M.: Genetic Scheduling of Task Graphs. International Electron Journal 77(4), 401–415 (1994)
Ahmad, I., Dhodhi, K.: Multiprocessor Scheduling in a Genetic Paradigm. IEEE Parallel Computing 22, 395–406 (1996)
Hou, H., Ansari, N., Ren, H.: A Genetic Algorithm form Multiprocessor Scheduling. IEEE Transaction of Parallel and Distributed Systems 5(2), 113–120 (1997)
Andrei, A., Eles, P., Peng, Z., Schmitz, M., Al-Hashimi, B.M.: Voltage selection for time-constrained multiprocessor systems on chip (in Press)
Henkel, J., Parameswaran, S.: Designing Embedded Processors: A Low Power Perspective, pp. 259–282. Springer, Heidelberg (2007)
Schmitz, M.T., Al-Hashimi, B., Eles, P.: Considering Power Variation of DVS Processing Elements for Energy-Minimization in Distributed Systems. In: Proc. ISSS (2001)
Schmitz, M.T., Al-Hashimi, B., Eles, P.: System Level Design Techniques for Energy- Efficient Embedded Systems. Kluwer Academic Publishers, Dordrecht (2004)
Bohler, M., Moore, F., Pan, Y.: Improved Multiprocessor Task Scheduling Using Genetic Algorithms. In: Proceedings of the Twelfth International FLAIRS Conference (1999)
Rahmani, A.M., Vahedi, M.A.: A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method (in press)
Kwok, K., Ahmad, I.: Efficient Scheduling of Arbitrary Task Graphs to Multiprocessors Using a Parallel Genetic Algorithm. Parallel and Distributed Computing Journal 47, 58–71 (2006)
Gorjiara, B., Bagherzadeh, N.: Ultra-Fast and Efficient Algorithm for Energy Optimization by Gradient-Based Stochastic Voltage and Task Scheduling. ACM Transactions on Design Automation of Electronic Systems 12(4), article 39 (2007)
Zhang, L., Chen, Y., Sun, R., Jing, S., Yang, B.: A Task Scheduling Algorithm Based on PSO for Grid Computing. International Journal of Computational Intelligence Research 4(1), 37–43 (2008)
Barnett, J.A.: Dynamic Task-Level Voltage Scheduling Optimizations. IEEE Trans. Computers 54(5), 508–520 (2005)
Ding, D., Zhang, L., Wei, Z.: A Novel Voltage Scaling Algorithm through Ant Colony Optimization for Embedded Distributed Systems. In: Proceedings of the 2007 IEEE International Conference on Integration Technology, Shenzhen, China, March 20-24, pp. 547–552 (2007)
Bunde, D.P.: Power-Aware Scheduling for Makespan and Flow. In: Proc. 18th ACM Symp. Parallelism in Algorithms and Architectures (SPAA 2006), pp. 190–196 (2006)
Rusu, Melhem, R., Mossé, D.: Maximizing the System Value While Satisfying Time and energy Constraints. In: Proc. 23rd IEEE Real-Time Systems Symp. (RTSS 2002), pp. 256–265 (2002)
Gara, et al.: Overview of the Blue Gene/L System Architecture. IBM J. Research and Development 49(2/3), 195–212 (2005)
Graham, R.L.: Bounds on Multiprocessing Timing Anomalies. SIAM J. Applied Math. 2, 416–429 (1969)
Li, K.: Performance Analysis of Power-Aware Task Scheduling Algorithms on Multiprocessor Computers with Dynamic Voltage and Speed. IEEE Transactions On Parallel and Distributed Systems 19(11), 1484–1497 (2008)
Zhang, L., Chen, Y., Sun, R., Jing, S., Yang, B.: A Task Scheduling Algorithm Based on PSO for Grid Computing. International Journal of Computational Intelligence Research 4(1), 37–43 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
kumar, P.R., Palani, S. (2011). An Evolutionary Algorithm Based Performance Analysis of Multiprocessor Computers through Energy and Schedule Length Model. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Parallel Distributed Computing. PDCTA 2011. Communications in Computer and Information Science, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24037-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-24037-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24036-2
Online ISBN: 978-3-642-24037-9
eBook Packages: Computer ScienceComputer Science (R0)