R-storm: Resource-aware scheduling in storm

Middleware 2015 - Proceedings of the 16th Annual Middleware Conference

Volumn , Issue , 2015, Pages 149-161

  Peng, Boyang ^a   Hosseini, Mohammad ^a   Hong, Zhihao ^a   Farivar, Reza ^b   Campbell, Roy ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b YAHOO INC   (United States)

Author keywords

Resource aware scheduling; Storm; Stream

Indexed keywords

BENCHMARKING; BIG DATA; DISTRIBUTED PARAMETER CONTROL SYSTEMS; MIDDLEWARE; REAL TIME SYSTEMS; ROUTERS; SCHEDULING; SCHEDULING ALGORITHMS; THROUGHPUT;

DISTRIBUTED STREAM PROCESSING; INTELLIGENT SCHEDULING; RESOURCE CONSTRAINT; RESOURCE UTILIZATIONS; RESOURCE-AWARE SCHEDULING; ROUND-ROBIN SCHEDULING; STREAM; STREAM PROCESSING SYSTEMS;

STORMS;

EID: 84967211111     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2814576.2814808     Document Type: Conference Paper

References (30)

1. Apache Flink. https://ink.apache.org/.
2. Apache Hadoop. http://hadoop.apache.org/.
3. Apache Hive. https://hive.apache.org/.
4. Apache Pig. http://pig.apache.org/.
5. Apache Spark. https://spark.apache.org/.
6. Apache Storm, available at . https://storm.apache.org.
7. Apache Zookeeper. http://zookeeper.apache.org/.
8. Emulab. http://emulab.net/.
9. What is big data? | Bringing big data to the enterprise. www.ibm.com, http://www.ibm.com/big-data/us/en/, 2013.
10. Big Data at the Speed of Business - What is big data? www.ibm.com, http://www.ibm.com/software/data/bigdata/what-is- big-data.html, 2014.
11. R. Andonov, V. Poirriez, and S. Rajopadhye. Unbounded knapsack problem: Dynamic programming revisited. European Journal of Operational Research, 123(2):394{407, 2000.
12. L. Aniello, R. Baldoni, and L. Querzoni. Adaptive online scheduling in storm. In Proceedings of the 7th ACM International Conference on Distributed Event-based Systems, DEBS '13, pages 207{218, New York, NY, USA, 2013. ACM.
13. C. Chekuri and S. Khanna. A ptas for the multiple knapsack problem. In Proceedings of the Eleventh Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '00, pages 213{222, Philadelphia, PA, USA, 2000. Society for Industrial and Applied Mathematics.
14. C. Chekuri and S. Khanna. A polynomial time approximation scheme for the multiple knapsack problem. SIAM Journal on Computing, 35(3):713{728, 2005.
15. R. Cohen and G. Grebla. Multi-dimensional ofdma scheduling in a wireless network with relay nodes. In INFOCOM, 2014 Proceedings IEEE, pages 2427{2435, April 2014.
16. D. Fayard and V. Zissimopoulos. An approximation algorithm for solving unconstrained two-dimensional knapsack problems. European Journal of Operational Research, 84(3):618{632, 1995.
17. G. Gallo, P. Hammer, and B. Simeone. Quadratic knapsack problems. In M. Padberg, editor, Combinatorial Optimization, volume 12 of Mathematical Programming Studies, pages 132{149. Springer Berlin Heidelberg, 1980.
18. H. Greenberg and R. L. Hegerich. A branch search algorithm for the knapsack problem. Management Science, 16(5):327{332, 1970.
19. M. Hosseini and G. Kurillo. Coordinated bandwidth adaptations for distributed 3d tele-immersive systems. In Proceedings of the 7th ACM International Workshop on Massively Multiuser Virtual Environments, MMVE '15, pages 13{18, 2015.
20. M. Hosseini, J. Peters, and S. Shirmohammadi. Energy-budget-compliant adaptive 3D texture streaming in mobile games. In Proceedings of the 4th ACM Multimedia Systems Conference, MMSys '13, pages 1{11, 2013.
21. M. Hosseini, J. Peters, and S. Shirmohammadi. Energy-efficient 3D texture streaming for mobile games. In Proceedings of the 6th ACM Workshop on Mobile Video Delivery, MoViD '14, pages 5:1{5:6, 2014.
22. M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: Distributed data-parallel programs from sequential building blocks. In ACM SIGOPS Operating Systems Review, volume 41, pages 59{72. ACM, 2007.
23. S. Kulkarni, N. Bhagat, M. Fu, V. Kedigehalli, C. Kellogg, S. Mittal, J. M. Patel, K. Ramasamy, and S. Taneja. Twitter heron: Stream processing at scale. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pages 239{250. ACM, 2015.
24. S. Martello, D. Pisinger, and P. Toth. Dynamic programming and strong bounds for the 0-1 knapsack problem. Management Science, 45(3):414{424, 1999.
25. J. Polo, C. Castillo, D. Carrera, Y. Becerra, I. Whalley, M. Steinder, J. Torres, and E. Ayguade. Resource-aware adaptive scheduling for mapreduce clusters. In Proceedings of the 12th ACM/IFIP/USENIX International Conference on Middleware, Middleware'11, pages 187{207, Berlin, Heidelberg, 2011. Springer-Verlag.
26. U. K. Sarkar, P. P. Chakrabarti, S. Ghose, and S. De Sarkar. Reducing reexpansions in iterative-deepening search by controlling cutoff bounds. Artificial Intelligence, 50(2):207{221, 1991.
27. Y. Song, C. Zhang, and Y. Fang. Multiple multidimensional knapsack problem and its applications in cognitive radio networks. In Military Communications Conference, 2008. MILCOM 2008. IEEE, pages 1{7, Nov 2008.
28. J. Wolf, N. Bansal, K. Hildrum, S. Parekh, D. Rajan, R. Wagle, K.-L. Wu, and L. Fleischer. Soda: An optimizing scheduler for large-scale stream-based distributed computer systems. In Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, Middleware '08, pages 306{325, New York, NY, USA, 2008. Springer-Verlag New York, Inc.
29. Z. Wu, Y. Yang, F. Bai, and M. Mammadov. Global optimality conditions and optimization methods for quadratic knapsack problems. Journal of Optimization Theory and Applications, 151(2):241{259, 2011.
30. X.-F. Xie and J. Liu. A mini-swarm for the quadratic knapsack problem. In Swarm Intelligence Symposium, 2007. SIS 2007. IEEE, pages 190{197, April 2007.