[1]
C. Mosharaf, Z. Matei, M. Justin, J. Michael, and S. Ion, Managing data transfers in computer clusters with Orchestra, ACM SIGCOMM Computer Communication Review, vol. 41, no. 4, pp. 98-109, 2011.
[2]
J. H. Howard, M. L. Kazar, S. G. Menees, D. A. Nichols, M. Satyanarayanan, R. N. Sidebotham, and M. J. West, Scale and performance in a distributed file system, ACM Transaction on Computer System, vol. 6, no. 1, pp. 51-81, 1988.
[3]
D. P. Woodruff and Q. Zhang, When distributed computation is communication expensive, Distributed Computing, vol. 30, no. 5, pp. 309-323, 2017.
[4]
J. Dean and S. Ghemawat, MapReduce: Simplified data processing on large clusters, in Proceedings of USENIX Symposium on Operating System Design and Implementation (OSDI’04), San Francisco, CA, USA, 2004, pp. 137-150.
[5]
Z. Shen, S. Subbiah, X. Gu, and J. Wilkes, Cloudscale: Elastic resource scaling for multi-tenant cloud systems, in Proceedings of ACM Symposium on Cloud Computing (SOCC’11), Cascais, Portugal, 2011, pp. 5-17.
[6]
C. Delimitrou and C. Kozyrakis, Quasar: Resource-efficient and QoS-aware cluster management, in Proceedings of ACM Architectural Support for Programming Languages and Operating Systems (ASPLOS’14), Salt Lake City, UT, USA, 2014, pp. 127-144.
[7]
B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph, R. Katz, S. Shenker, and I. Stoica, Mesos: A platform for fine-grained resource sharing in the data center, in Proceedings of USENIX Symposium on Networked Systems Design and Implementation (NSDI’11), Boston, MA, USA, 2011, pp. 429-483.
[8]
V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, and S. Seth, Apache Hadoop YARN: Yet another resource negotiator, in Proceedings of ACM Symposium on Cloud Computing (SOCC’13), Santa Clara, CA, USA, 2013, pp. 1-16.
[9]
H. Mao, M. Alizadeh, I. Menache, and S. Kandula, Resource management with deep reinforcement learning, in Proceedings of ACM HotNet Workshop on Hot Topics in Networks (HotNet’16), Atlanta, GA, USA, 2016, pp. 50-56.
[10]
T. Bonald, L. Massouli, A. Prouti, and J. T. Virtamo, A queueing analysis of max-min fairness, proportional fairness and balanced fairness, Queueing Systems, vol. 53, nos. 1&2, pp. 65-84, 2006.
[11]
A. Ghodsi, M. Zaharia, B. Hindman, A. Konwinski, S. Shenker, and I. Stoica, Dominant resource fairness: Fair allocation of multiple resource types, in Proceedings of USENIX Symposium on Networked Systems Design and Implementation (NSDI’13), Boston, MA, USA, 2013, pp. 323-336.
[12]
M. Zaharia, D. Borthakur, J. S. Sarma, K. Elmeleegy, S. Shenker, and I. Stoica, Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling, in Proceedings of European Conference on Computer Systems (EuroSys’10), Paris, France, 2010, pp. 265-278.
[13]
R. Grandl, G. Ananthanarayanan, S. Kandula, S. Rao, and A. Akella, Multi-resource packing for cluster schedulers, in Proceedings of ACM Special Interest Group on Data Communication (SIGCOMM’14), Chicago, IL, USA, 2014, pp. 455-466.
[14]
S. Venkataraman, Z. Yang, M. J. Franklin, B. Recht, and I. Stoica, Ernest: Efficient performance prediction for large-scale advanced analytics, in Proceedings of USENIX Symposium on Networked Systems Design and Implementatio (NSDI’16), Santa Clara, CA, USA, 2016, pp. 363-378.
[15]
Z. Bei, Z. Yu, H. Zhang, W. Xiong, C. Xu, L. Eeckhout, and S. Feng, RFHOC: A random-forest approach to auto-tuning Hadoop’s configuration, IEEE Transaction on Parallel and Distributed Systems, vol. 27, no. 5, pp. 1470-1483, 2016.
[16]
Z. Yu, Z. Bei, and X. Qian, Datasize-aware high dimensional configurations auto-tuning of in-memory cluster computing, in Proceedings of ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS’18), Williamsburg, VA, USA, 2018, pp. 564-577.
[17]
C. Re, D. Agrawal, M. Balazinska, M. J. Cafarella, M. I. Jordan, T. Kraska, and R. Ramakrishnan, Machine learning and databases: The sound of things to come or a cacophony of hype? in Proceedings of ACM International Conference on Management of Data (SIGMOD’15), Melbourne, Australia, 2015, pp. 283-284.
[18]
L. Sun, S. Sun, T. Wang, J. Li, and J. Lin, Parallel ADR detection based on spark and BCPNN, Tsinghua Science and Technology, vol. 24, no. 2, pp. 195-206, 2019.
[19]
X. Ye, X. Chen, D. Liu, W. Wang, L. Yang, G. Liang, and G. Shao, Notice of retraction: Efficient feature extraction using Apache Spark for network behavior anomaly detection, Tsinghua Science and Technology, vol. 23, no. 5, pp. 561-573, 2018.
[20]
M. Wang, Y. Cui, S. Xiao, X. Wang, D. Yang, K. Chen, and J. Zhu, Neural network meets DCN: Traffic-driven topology adaptation with deep learning, in Proceedings of ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS’18), Irvine, CA, USA, 2018, pp. 97-99.
[21]
N. Yamashita and M. Fukushima, On the rate of convergence of the Levenberg-Marquardt method, Springer Computing, vol. 15, pp. 239-249, 2001.
[22]
R. Grandl, M. Chowdhury, A. Akella, and G. Ananthanarayanan, Altruistic scheduling in multi-resource clusters, in Proceedings of USENIX Symposium on Operating Systems Design and Implementation (OSDI’16), Savannah, GA, USA, 2016, pp. 65-80.
[23]
D. Fooladivanda, A. A. Daoud, and C. Rosenberg, Joint channel allocation and user association for heterogeneous wireless cellular networks, IEEE Transaction on Wireless Communications, vol. 12, no. 1, pp. 248-257, 2011.
