Abstract
In this work we present a novel approach, based on genetic algorithms, for automatic scheduling and allocation of tasks in a multi-processor multi-threaded architecture, together with an assignment of the appropriate voltage and frequency of each processor in a way the overall energy consumption is optimized and all task deadlines are met. The approach deals with scheduling, allocation and voltage and frequency assignment at the same time, and provides good solutions in a very short time. As far as we know, this is the first approach that supports two levels of parallelism: multi-processor and multi-thread.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Entra project (2013), http://entraproject.eu/
Speccpu2006 (2013), http://www.spec.org/cpu2006/
Ying, C.-T., Yu, J.: Energy-aware genetic algorithms for task scheduling in cloud computing. In: 2012 Seventh ChinaGrid Annual Conference, ChinaGrid, pp. 43–48 (2012)
Kianzad, V., Bhattacharyya, S.S., Qu, G.: Casper: an integrated energy-driven approach for task graph scheduling on distributed embedded systems. In: 16th IEEE International Conference on Application-Specific Systems, Architecture Processors, ASAP 2005, pp. 191–197 (2005)
Kumar, P.R., Palani, S.: A dynamic voltage scaling with single power supply and varying speed factor for multiprocessor system using genetic algorithm. In: 2012 International Conference on Pattern Recognition, Informatics and Medical Engineering, PRIME, pp. 342–346 (2012)
XMos Ltd. Xs1-l active energy conservation (April 2010)
XMos Ltd. Estimating power consumption for xs1-l devices (May 2012)
XMos Ltd. Xs1-su01a-fb96 datasheet (November 2012)
Mezmaz, M., Lee, Y.C., Melab, N., Talbi, E., Zomaya, A.Y.: A bi-objective hybrid genetic algorithm to minimize energy consumption and makespan for precedence-constrained applications using dynamic voltage scaling. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)
Mezmaz, M.-S., Kessaci, Y., Lee, Y.C., Melab, N., Talbi, E.-G., Zomaya, A.Y., Tuyttens, D.: A parallel island-based hybrid genetic algorithm for precedence-constrained applications to minimize energy consumption and makespan. In: 2010 11th IEEE/ACM International Conference on Grid Computing, GRID, pp. 274–281 (2010)
Paterna, F., Acquaviva, A., Caprara, A., Papariello, F., Desoli, G., Benini, L.: An efficient on-line task allocation algorithm for QoS and energy efficiency in multicore multimedia platforms. In: Design, Automation Test in Europe Conference Exhibition, DATE, pp. 1–6 (March 2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Banković, Z., López-García, P. (2013). Genetic Algorithm-Based Allocation and Scheduling for Voltage and Frequency Scalable XMOS Chips. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-40846-5_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40845-8
Online ISBN: 978-3-642-40846-5
eBook Packages: Computer ScienceComputer Science (R0)