Abstract
Data generation has increased drastically over the past few years. Processing large amounts of data requires huge compute and storage infrastructures, which consume substantial amounts of energy. Moreover, another important aspect to consider is that more and more the data is analyzed on-board battery operated mobile devices like smart-phones and sensors. Therefore, data processing techniques are required to operate while meeting resource constraints such as memory and power to prolong a mobile device network’s lifetime. This chapter reviews representative methods used for energy efficient Big Data analysis, providing first a generic overview of the issue of energy conservation and then presenting a more detailed analysis of the issue of energy efficiency in mobile and sensor networks.
References
Alsalih W, Akl SG, Hassanein HS (2005) Energy-aware task scheduling: towards enabling mobile computing over MANETs. In: IPDPS’05, pp 242a
Alwadi M, Chetty G (2015) Energy efficient data mining scheme for high dimensional data. Procedia Comput Sci 46:483–490
Aydin H, Melhem R, Moss D, Mejia-Alvarez P (2004) Power-aware scheduling for periodic real-time tasks. IEEE Trans Comput 53(5):584–600
Bhargava R, Kargupta H, Powers M. (2003) Energy consumption in data analysis for on-board and distributed applications. In: ICML’03
Bianchini R, Rajaniony R (2004) Power and energy management for server systems. Computer 37(11):68–76
Catena M, Tonellotto N (2017) Energy-efficient query processing in web search engines. Trans Knowl Data Eng 29:1412–1425
Comito C, Talia D (2014) Energy-aware clustering of ubiquitous devices for collaborative mobile applications. In: Proceeding of MobiCASE, pp 133–142
Comito C, Talia D (2017) Energy consumption of data mining algorithms on mobile phones: evaluation and prediction. In: Pervasive and mobile computing, vol 42, pp 248–264
Comito C, Talia D, Trunfio P (2011) An energy-aware clustering scheme for mobile applications. In: IEEE Scalcom’11, pp 15–22
Comito C, Talia D, Trunfio P (2012) An energy aware framework for mobile data mining, chapt. 23. In: Energy efficient distributed computing systems. Wiley-IEEE Computer Society Press, New Jersey
Comito C, Falcone D, Talia D, Trunfio P (2017) Energy-aware task allocation for small devices in wireless networks. Concurrency Comput Pract Exp 29(1):1–24
Guo B, Yu J, Liao B, Yang D, Lu L (2017) A green framework for DBMS based on energy-aware query optimization and energy-efficient query processing. J Netw Comput Appl 84:118–130
Kargupta H, Park B, Pitties S, Liu L, Kushraj D, Sarkar K (2002) Mobimine: monitoring the stock marked from a PDA. ACM SIGKDD Explor 3(2):37–46
Kargupta H, Bhargava R, Liu K, Powers M, Blair P, Bushra S, Dull J (2003) VEDAS: a mobile and distributed data stream mining system for RealTime vehicle monitoring. In: SIAM data mining conference
Lang W, Patel JM (2009) Towards Eco-friendly database management systems. CoRR, vol. abs/0909.1767
Lang W, Kandhan R, Patel JM (2011) Rethinking query processing for energy efficiency: slowing down to win the race. Computer Sciences Department, University of Wisconsin, Madison
Lefurgy C, Rajamani K, Rawson F, Felter W, Kistler M, Keller T (2003) Energy management for commercial servers. Computer 36(12):39–48
Li K, Kumpf R, Horton P, Anderson T (1994) A quantitative analysis of disk driver power management in portable computers. In: USENIX conference, pp 279–292
Li Z, Wang C, Xu R (2001) Computation offloading to save energy on handheld devices: a partition scheme. In: ACM international conference compilers, architecture, and synthesis for embedded systems, pp 238–246
Liu L, Wang H, Liu X, Jin X, He W, Wang Q, Chen Y (2009) GreenCloud: a new architecture for green data center. In: 6th international conference on autonomic computing and communications, pp 29–38
Luo J, Jha NK (2000) Power-conscious joint scheduling of periodic task graphs and aperiodic tasks in distributed real-time embedded systems. In: ICCAD
Mohapatra S, Venkatasubramanian N (2003) PARM: power aware reconfigurable middleware. In: 23rd international conference on distributed computing systems, pp 312–319
Petrucci V, Loques O, Niteroi B, Mossé D (2009) Dynamic configuration support for power-aware virtualized server clusters. In: 21th Euromicro conference on real-time systems
Rosemark R, Lee WC, Urgaonkar B (2007) Optimizing energy-efficient query processing in wireless sensor networks. In: International conference on mobile data management, pp 24–29
Roukh A, Bellatreche L, Ordonez C (2016) EnerQuery: energy-aware query processing. In: Proceeding of the 25th ACM CIKM conference, pp 2465–2468
Rudenko A, Reiher P, Popek GJ, Kuenning GH (1998) Saving portable computer battery power through remote process execution. SIGMOBILE Mob Comput Commun Rev 2(1):19–26
Seth K, Anantaraman A, Mueller F, Rotenberg E (2003) FAST: frequency-aware static timing analysis. In: IEEE RTSS, pp 40–51
Sun JZ (2008) An energy-efficient query processing algorithm for wireless sensor networks. In: Sandnes FE, Zhang Y, Rong C, Yang LT, Ma J (eds) Ubiquitous intelligence and computing
Verma A, Ahuja P, Neogi A (2008) Power-aware dynamic placement of HPC applications. In: International conference on supercomputing, pp 175–184
Wang F, Helian N, Guo Y, Jin H (2003) A distributed and mobile data mining system. In: Proceeding of the international conference on parallel and distributed computing, applications and technologies
Weiser M, Welch B, Demers A, Shenker S (1996) Scheduling for reduced CPU energy. In: Mobile computing. Springer, Boston, pp 449–471
Yang J, Mo T, Lim L, Sattler KU, Misra A (2013) Energy-efficient collaborative query processing framework for mobile sensing services. In: IEEE 14th international conference on mobile data management, pp 147–156
Zhang Y, Hu X, Chen D (2002) Task scheduling and voltage selection for energy minimization. In: DAC’02, pp 183–188
Zhuo J, Chakrabarti C (2005) An efficient dynamic task scheduling algorithm for battery powered DVS systems. In: ASP-DAC’05, pp 846–849
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this entry
Cite this entry
Comito, C. (2018). Energy Efficiency in Big Data Analysis. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_141-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-63962-8_141-1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63962-8
Online ISBN: 978-3-319-63962-8
eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering