Brief announcement: A new improved bound for coflow scheduling

Annual ACM Symposium on Parallelism in Algorithms and Architectures

Volumn Part F129316, Issue , 2017, Pages 91-93

  Shafiee, Mehrnoosh ^a   Ghaderi, Javad ^a  

^a Columbia University ^*  (United States)

Author keywords

Approximation algorithms; Coflow; Datacenter network; Scheduling algorithms

Indexed keywords

NETWORK ARCHITECTURE; POLYNOMIAL APPROXIMATION; SCHEDULING; SCHEDULING ALGORITHMS;

CO-FLOW; COMMUNICATION PATTERN; DATA CENTER NETWORKS; DATA PARALLEL COMPUTING; DETERMINISTIC ALGORITHMS; PERFORMANCE GUARANTEES; POLYNOMIAL TIME APPROXIMATION ALGORITHMS; TOTAL WEIGHTED COMPLETION TIME;

APPROXIMATION ALGORITHMS;

EID: 85027834631     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/3087556.3087598     Document Type: Conference Paper

References (14)

1. M. Chowdhury and I. Stoica. Coflow: A networking abstraction for cluster applications. In Proceedings of the 11th ACM Workshop on Hot Topics in Networks, pages 31-36. ACM, 2012.
2. M. Chowdhury, Y. Zhong, and I. Stoica. Efficient coflow scheduling with varys. In ACM SIGCOMM Computer Communication Review, volume 44, pages 443-454. ACM, 2014.
3. J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51(1):107-113, 2008.
4. L. A. Hall, D. B. Shmoys, and J.Wein. Scheduling to minimize average completion time: Off-line and on-line algorithms. In SODA, volume 96, pages 142-151, 1996.
5. M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fe.erly. Dryad: distributed dataparallel programs from sequential building blocks. In ACM SIGOPS Operating Systems Review, volume 41, pages 59-72. ACM, 2007.
6. S. Khuller and M. Purohit. Brief announcement: Improved approximation algorithms for scheduling co-flows. In Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures, pages 239-240. ACM, 2016.
7. S. Luo, H. Yu, Y. Zhao, S. Wang, S. Yu, and L. Li. Towards practical and nearoptimal coflow scheduling for data center networks. 2016.
8. M. Mastrolilli, M..eyranne, A. S. Schulz, O. Svensson, and N. A. Uhan. Minimizing the sum of weighted completion times in a concurrent open shop. Operations Research Le.ers, 38(5):390-395, 2010.
9. C. Poffs. An algorithm for the single machine sequencing problem with precedence constraints. In Combinatorial Optimization II, pages 78-87. Springer, 1980.
10. Z. Qiu, C. Stein, and Y. Zhong. Minimizing the total weighted completion time of coflows in datacenter networks. In Proceedings of the 27th ACM symposium on Parallelism in Algorithms and Architectures, pages 294-303. ACM, 2015.
11. M. Shafiee and J. Ghaderi. An improved bound for minimizing the total weighted completion time of coflows in datacenters. arXiv preprint arXiv:1704.08357 2017.
12. M. Shafiee and J. Ghaderi. Scheduling coflows in datacenter networks: Improved bound for total weighted completion time. ACM SIGMETRICS, Poster Paper, 2017.
13. K. Shvachko, H. Kuang, S. Radia, and R. Chansler. The hadoop distributed file system. In 2010 IEEE 26th symposium on mass storage systems and technologies (MSST), pages 1-10. IEEE, 2010.
14. M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica. Spark: Cluster computing with working sets. HotCloud, 10(10-10):95, 2010.