Abstract
In recent future, wireless sensor networks (WSNs) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of WSNs facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers (DCs). The high economical and environmental impact of the energy consumption in DCs requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed.
In this context, this paper shows the following on-going research lines and obtained results. In the field of WSNs: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of DCs: energy-optimal workload assignment policies in heterogeneous DCs, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.
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
Feng, J., Koushanfar, F., Potkonjak, M.: System-architectures for sensor networks issues, alternatives, and directions. In: Proceedings of the 20th International Conference on Computer Design (2002)
Yassin, Y., Kjeldsberg, P., Hulzink, J., Romero, I., Huisken, J.: Ultra low power application specific instruction-set processor design for a cardiac beat detector algorithm. In: NORCHIP 2009, pp. 1–4 (2009)
Kandemir, M.T., Kolcu, I., Kadayif, I.: Influence of Loop Optimizations on Energy Consumption of Multi-bank Memory Systems. In: Horspool, R.N. (ed.) CC 2002. LNCS, vol. 2304, pp. 276–292. Springer, Heidelberg (2002)
Gordon-Ross, A., Vahid, F., Dutt, N.D.: Fast configurable-cache tuning with a unified second-level cache. IEEE Trans. VLSI Syst. 17(1), 80–91 (2009)
Hauck, S., Fry, T.W., Hosler, M.M., Kao, J.P.: The chimaera reconfigurable functional unit. In: FCCM, pp. 87–93 (1997)
Motorola: Morphable functional units, http://ip.com/IPCOM/000004783
Solihin, Y., Cameron, K.W., Luo, Y., Lavenier, D., Gokhale, M.: Mutable functional units and their applications on microprocessors. In: ICCD, p. 234 (2001)
Barrio, A.A.D., Molina, M.C., Mendias, J.M., Perez, E.A., Hermida, R., Tirado, F.: Applying speculation techniques to implement functional units. In: ICCD, pp. 74–80 (2008)
Ramachandran, I., Das, A.K., Roy, S.: Analysis of the contention access period of ieee 802.15.4 mac. TOSNÂ 3(1), 4 (2007)
Neugebauer, M., Plonnigs, J., Kabitzsch, K.: A new beacon order adaptation algorithm for ieee 802.15. 4 networks. In: Proc. European Work. on Wirel. Sens. Netw. (EWSN 2005), pp. 302–311 (2005) (Adaptation)
Kim, T.H., Choi, S.: Priority-based delay mitigation for event-monitoring ieee 802.15.4 lr-wpans. IEEE Communications Letters, 213–215 (2006)
Bonivento, A., Carloni, L.P., Sangiovanni-Vincentelli, A.: Platform based design for wireless sensor networks. Mob. Netw. Appl. 11(4), 469–485 (2006)
Faza, A.Z., Sedigh-Ali, S.: A general purpose framework for wireless sensor network applications. In: Proc. Annual Intl. Computer Software and Applications Conference, COMPSAC, pp. 356–358 (2006)
Iyengar, S., Bonda, F.T., Gravina, R., Guerrieri, A., Fortino, G., Sangiovanni- Vincentelli, A.: A framework for creating healthcare monitoring applications using wireless body sensor networks. In: Proc. ICST Intl. Conf. on Body Area Networks, BodyNets, pp. 8:1–8:2 (2008)
Banerjee, A., Mukherjee, T., Varsamopoulos, G., Gupta, S.: Integrating cooling awareness with thermal aware workload placement for hpc data centers. Sustainable Computing: Informatics and Systems 1(2), 134–150 (2011)
Han, Y., Koren, I.: Simulated annealing based temperature aware floorplanning. J. Low Power Electronics 3(2), 141–155 (2007)
Xie, Y., Hung, W.L.: Temperature-aware task allocation and scheduling for embedded multiprocessor systems-on-chip (mpsoc) design. J. VLSI Signal Process. Syst. 45(3), 177–189 (2006)
Pakbaznia, E., Ghasemazar, M., Pedram, M.: Temperature-aware dynamic resource provisioning in a power-optimized datacenter. In: DATE, pp. 124–129 (2010)
Hsu, C.H., Feng, W.C.: A power-aware run-time system for high-performance computing. In: Proc. ACM/IEEE Conference on Supercomputing, SC, p. 1 (2005)
Zheng, X., Cai, Y.: Markov model based power management in server clusters. In: Proc. IEEE/ACM Int’l Conference on Green Computing and Communications, GREENCOM, pp. 96–102 (2010)
Mukherjee, T., Banerjee, A., Varsamopoulos, G., Gupta, S.K.S., Rungta, S.: Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers. Comput. Netw. 53(17), 2888–2904
Wu, G., Xu, Z., Xia, Q., Ren, J., Xia, F.: Task allocation and migration algorithm for temperature-constrained real-time multi-core systems. In: Proc. IEEE/ACM Int’l Conference on Green Computing and Communications, GREENCOM, pp. 189–196 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zapater, M., Ayala, J.L., Moya, J.M. (2012). GreenDisc: A HW/SW Energy Optimization Framework in Globally Distributed Computation. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2012. Lecture Notes in Computer Science, vol 7656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35377-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-35377-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35376-5
Online ISBN: 978-3-642-35377-2
eBook Packages: Computer ScienceComputer Science (R0)