An ARM-based hadoop performance evaluation platform: Design and implementation

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

Volumn 163, Issue , 2016, Pages 82-94

  Fan, Xiaohu ^a   Chen, Si ^a   Qi, Shipeng ^a   Luo, Xincheng ^a   Zeng, Jing ^a   Huang, Hao ^a   Xie, Changsheng ^b  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

ARM cluster; Cost effective; Data intensive; HPC

Indexed keywords

COMPUTATIONAL EFFICIENCY; COST EFFECTIVENESS; ENERGY EFFICIENCY;

ARM CLUSTER; COST EFFECTIVE; DATA INTENSIVE; DATA-INTENSIVE COMPUTING; DESIGN AND IMPLEMENTATIONS; ENTERPRISE APPLICATIONS; HIGH PERFORMANCE CLUSTER; HYBRID ARCHITECTURES;

ARM PROCESSORS;

EID: 84958073447     PISSN: 18678211     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-28910-6_8     Document Type: Conference Paper

References (28)

1. GöDdeke, D., Komatitsch, D., Geveler, M., et al.: Energy efficiency vs. performance of the numerical solution of PDEs: an application study on a low-power ARM-based cluster. J. Comput. Phys. 237, 132-150 (2013).
2. Rajovic, N., Rico, A., Puzovic, N., et al.: Tibidabo: making the case for an ARM-based HPC system. Future Gener. Comput. Syst. 36, 322-334 (2014).
3. www.top500.rog.
4. www.green500.org.
5. Ebrahimi, K., Jones, G.F., Fleischer, A.S.: A review of data center cooling technology, operating conditions and the corresponding low-grade waste heat recovery opportunities. Renew. Sustain. Energy Rev. 31, 622-638 (2014).
6. Turley, J.: Cortex-A15 “Eagle” flies the coop. Microprocess. Rep. 24(11), 1-11 (2010).
7. ARM Ltd.: Cortex-A50 series. http://www.arm.com.
8. Ghemawat, S., Gobioff, H., Leung, S.T.: The Google file system. ACM SIGOPS Oper. Syst. Rev. 37(5), 29-43 (2003). ACM.
9. Chang, F., Dean, J., Ghemawat, S., et al.: Bigtable: a distributed storage system for structured data. ACM Trans. Comput. Syst. (TOCS) 26(2), 4 (2008).
10. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107-113 (2008).
11. Leverich, J., Kozyrakis, C.: On the energy (in) efficiency of hadoop clusters. ACM SIGOPS Oper. Syst. Rev. 44(1), 61-65 (2010).
12. Shvachko, K., Kuang, H., Radia, S., et al.: The hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1-10. IEEE (2010).
13. Zaharia, M., Konwinski, A., Joseph, A.D., et al.: Improving MapReduce performance in heterogeneous environments. In: OSDI, 8(4), p. 7 (2008).
14. Zaharia, M., Chowdhury, M., Das, T., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, p.2. USENIX Association (2012).
15. Fox, K., Mongan, W., Popyack, J.: Raspberry HadooPI: a low-cost, hands-on laboratory in big data and analytics. In: Proceedings of the 46th ACM Technical Symposium on Computer Science Education, p. 687. ACM (2015).
16. Kaewkasi, C., Srisuruk, W.: A study of big data processing constraints on a low-power Hadoop cluster. In: 2014 International Computer Science and Engineering Conference (ICSEC), pp. 267-272. IEEE (2014).
17. Aroca, R.V., Gonçalves, L.M.G.: Towards green data centers: a comparison of x86 and ARM architectures power efficiency. J. Parallel Distrib. Comput. 72(12), 1770-1780 (2012).
18. Klausecker, C., Kranzlmüller, D., Fürlinger, K.: Towards energy efficient parallel computing on consumer electronic devices. In: Kranzlmüller, D., Toja, A.M. (eds.) ICT-GLOW 2011. LNCS, vol. 6868, pp. 1-9. Springer, Heidelberg (2011).
19. Fürlinger, K., Klausecker, C., Kranzlmüller, D.: The AppleTV-cluster: towards energy efficient parallel computing on consumer electronic devices. Whitepaper, Ludwig-Maximilians-Universitat (2011).
20. Rajovic, N., Vilanova, L., Villavieja, C., et al.: The low power architecture approach towards exascale computing. J. Comput. Sci. 4(6), 439-443 (2013).
21. Dumitrel Loghin, B.M.T., Zhang, H., Ooi, B.C., et al.: A performance study of big data on small nodes. Proc. VLDB Endow. 8(7), 762-773 (2015).
22. Gu, L., Zeng, D., Guo, S., Yong, X., Hu, J.: A general communication cost optimization framework for big data stream processing in geo-distributed data center. IEEE Trans. Comput. (ToC) (2015).
23. Lin, G., Zeng, D., Li, P., Guo, S.: Cost minimization for big data processing in geo-distributed data centers. IEEE Trans. Emerg. Topics Comput. 2(3), 314-323 (2014).
24. Hu, C., Zhao, J., Yan, X., Zeng, D., Guo, S.: A MapReduce based parallel niche genetic algorithm for contaminant source identification in water distribution network. Ad Hoc Netw. 35, 116-126 (2015).
25. Gu, L., Zeng, D., Guo, S., Barnawi, A., Stojmenovic, I.: Optimal task placement with QoS constraints in geo-distributed data centers using DVFS. IEEE Trans. Comput. (ToC) 64(7), 2049-2059 (2014).
26. Plugaru, V., Varrette, S., Pinel, F., et al.: Evaluating the HPC performance and energy-efficiency of Intel and ARM-based systems with synthetic and bioinformatics workloads. In: CSC (2014).
27. McCool, M., Reinders, J., Robison, A.: Structured Parallel Programming: Patterns For Efficient Computation. Elsevier, Waltham (2012).
28. Chou, C.-Y., Chang, Hsi-Ya., Wang, S.-T., Tcheng, S.-C.: Modeling message-passing overhead on NCHC formosa PC cluster. In: Chung, Y.-C., Moreira, J.E. (eds.) GPC 2006. LNCS, vol. 3947, pp. 299-307. Springer, Heidelberg (2006).