1. 1. Dean, J., Ghemawat, S.: MapReduce: Simplified data processing on large clusters. In: OSDI 2004, San Francisco, CA, pp. 137–150 (December 2004)
   2. Hadoop MapReduce, http://hadoop.apache.org/mapreduce/ 
   3. Thusoo, A., Shao, Z., Anthony, S., Borthakur, D., Jain, N., Sen Sarma, J., Murthy, R., Liu, H.: Data warehousing and analytics infrastructure at facebook. In: Proceedings of the 2010 International Conference on Management of Data, SIGMOD 2010, pp. 1013–1020. ACM, New York (2010)
   4. Ananthanarayanan, G., Kandula, S., Greenberg, A., Stoica, I., Lu, Y., Saha, B., Harris, E.: Reining in the outliers in map-reduce clusters using mantri. In: OSDI 2010, pp. 1–16. USENIX Asoc., Berkeley (2010)
   5. Polo, J., Carrera, D., Becerra, Y., Steinder, M., Whalley, I.: Performance-driven task co-scheduling for MapReduce environments. In: Network Operations and Management Symposium, NOMS, pp. 373–380. IEEE, Osaka (2010)
   6. Wolf, J., Rajan, D., Hildrum, K., Khandekar, R., Kumar, V., Parekh, S., Wu, K.-L., Balmin, A.: Flex: A Slot Allocation Scheduling Optimizer for Mapreduce Workloads. In: Gupta, I., Mascolo, C. (eds.) Middleware 2010. LNCS, vol. 6452, pp. 1–20. Springer, Heidelberg (2010)
   7. Dynamic Proportional share scheduling in Hadoop Thomas sandholm and Kevin Springer Berlin Heidelberg Volume 6253, 2010, pp 110-131
   8. Improving Map Reduce Performance through Data Placement in Heterogeneous Hadoop Clusters- Jiong Xie, Shu Yin, Xiaojun Ruan, Zhiyang Ding, Yun Tian, James Majors, Adam Manzanares, and Xiao Qin -Department of Computer Science and Software Engineering Auburn University, Auburn, AL 36849-5347
   9. An Empirical Analysis of Scheduling techniques for Real-time cloud based data processing-linh T.X. Phan Zhuoyao zhang, Qi Zheng Boon Thau Loo University of Pennsylvania
   10. Herodotou, H., and Babu, S. Profiling, what-if analysis, and cost-based optimization of MapReduce programs. In Proc. Int’ Conf. on Very Large Data Bases (VLDB) (2011).
   11. MapR. The executive’s guide to big data. http://www.mapr.com/resources/white-papers.
   12. Pettijohn, E., Guo, Y., Lama, P., and Zhou, X. Usercentric heterogeneity-aware mapreduce job provisioning in the public cloud. In Proc. Int’l Conference on Autonomic Computing (ICAC) (2014).
   13. Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F. B., and Babu, S. Starfish: A self-tuning system for big data analytics. In Proc. Conference on Innovative Data Systems Research (CIDR) (2011).
   14. Lama, P., and Zhou, X. Aroma: Automated resource allocation and configuration of mapreduce environment in the cloud. In Proc. Int’l Conf. on Autonomic computing (ICAC) (2012).
   15. Li, X., Wang, Y., Jiao, Y., Xu, C., and Yu, W. Coomr: Cross-task coordination for efficient data management in mapreduce programs. In Proc. Int’l Conference for High Performance Computing, Networking, Storage and Analysis (SC) (2013).
   16. Kambatla, K., Pathak, A., and Pucha, H. Towards optimizing hadoop provisioning in the cloud. In Proc. USENIX HotCloud Workshop (2009).
   17.  Li, Z., Cheng, Y., Liu, C., and Zhao, C. Minimum standard deviation difference-based thresholding. In Proc. Int’l Conference on Measuring Technology and Mechatronics Automation (ICMTMA) (2010).
   18. Jinda, A., Quian-Ruiz, J., and Dittrich, J. Trojan data layouts: Right shoes for a running elephant. In Proc. of ACM Symposium on Cloud Computing (SoCC) (2011).