extended-abstract

Online batch scheduling for flow objectives

Authors:

Benjamin MoseleyAuthors Info & Claims

SPAA '13: Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures

Pages 102 - 104

https://doi.org/10.1145/2486159.2486161

Published: 23 July 2013 Publication History

Abstract

Batch scheduling gives a powerful way of increasing the throughput by aggregating multiple homogeneous jobs. It has applications in large scale manufacturing as well as in server scheduling. In batch scheduling, when explained in the setting of server scheduling, the server can process requests of the same type up to a certain number simultaneously. Batch scheduling can be seen as capacitated broadcast scheduling, a popular model considered in scheduling theory. In this paper, we consider an online batch scheduling model. For this model we address flow time objectives for the first time and give positive results for average flow time, the k-norms of flow time and maximum flow time. For average flow time and the k-norms of flow time we show algorithms that are O(1)-competitive with a small constant amount of resource augmentation. For maximum flow time we show a 2-competitive algorithm and this is the best possible competitive ratio for any online algorithm.

References

[1]

S. Acharya, M. Franklin, and S. Zdonik. Dissemination-based data delivery using broadcast disks. Personal Communications, IEEE {see also IEEE Wireless Communications}, 2(6):50--60, Dec 1995.

[2]

D. Aksoy and M. J. Franklin. R x W: A scheduling approach for large-scale on-demand data broadcast. IEEE/ACM Trans. Netw., 7(6):846--860, 1999.

Digital Library

[3]

N. Bansal, M. Charikar, S. Khanna, and J. S. Naor. Approximating the average response time in broadcast scheduling. In SODA '05, pages 215--221, 2005.

Digital Library

[4]

N. Bansal, D. Coppersmith, and M. Sviridenko. Improved approximation algorithms for broadcast scheduling. SIAM J. Comput., 38(3):1157--1174, 2008.

Digital Library

[5]

N. Bansal, R. Krishnaswamy, and V. Nagarajan. Better scalable algorithms for broadcast scheduling. In ICALP '10, pages 324--335. Springer, 2010.

Digital Library

[6]

N. Bansal and K. Pruhs. Server scheduling to balance priorities, fairness, and average quality of service. SIAM J. Comput., 39(7):3311--3335, 2010.

Digital Library

[7]

A. Bar-Noy, S. Guha, Y. Katz, J. S. Naor, B. Schieber, and H. Shachnai. Throughput maximization of real-time scheduling with batching. ACM Trans. Algorithms, 5(2):18:1--18:17, Mar. 2009.

Digital Library

[8]

H.-L. Chan, J. Edmonds, and K. Pruhs. Speed scaling of processes with arbitrary speedup curves on a multiprocessor. In SPAA '09, pages 1--10, 2009.

Digital Library

[9]

J. Chang, T. Erlebach, R. Gailis, and S. Khuller. Broadcast scheduling: algorithms and complexity. In SODA '08, pages 473--482, 2008.

Digital Library

[10]

C. Chekuri, A. Gal, S. Im, S. Khuller, J. Li, R. M. McCutchen, B. Moseley, and L. Raschid. New models and algorithms for throughput maximization in broadcast scheduling - (extended abstract). In WAOA '10, pages 71--82. Springer, 2010.

Digital Library

[11]

C. Chekuri, S. Im, and B. Moseley. Online scheduling to minimize maximum response time and maximum delay factor. Theory of Computing, 8(7):165--195, 2012.

[12]

J. Edmonds, D. D. Chinn, T. Brecht, and X. Deng. Non-clairvoyant multiprocessor scheduling of jobs with changing execution characteristics. J. Scheduling, 6(3):231--250, 2003.

Digital Library

[13]

J. Edmonds, S. Im, and B. Moseley. Online scalable scheduling for the l_k-norms of flow time without conservation of work. In SODA '11, pages 109--119, 2011.

Digital Library

[14]

J. Edmonds and K. Pruhs. Scalably scheduling processes with arbitrary speedup curves. In SODA '09, pages 685--692, 2009.

Digital Library

[15]

T. Erlebach and A. Hall. Np-hardness of broadcast scheduling and inapproximability of single-source unsplittable min-cost flow. J. Scheduling, 7(3):223--241, 2004.

Digital Library

[16]

R. Y. S. Hung and H.-F. Ting. Design and analysis of online batching systems. Algorithmica, 57(2):217--231, 2010.

Digital Library

[17]

S. Im and B. Moseley. An online scalable algorithm for average flow time in broadcast scheduling. ACM Trans. Algorithms, 8(4):39:1--39:17, Oct. 2012.

Digital Library

[18]

B. Kalyanasundaram and K. Pruhs. Speed is as powerful as clairvoyance. J. ACM, 47(4):617--643, 2000.

Digital Library

[19]

M. Mathirajan and A. Sivakumar. A literature review, classification and simple meta-analysis on scheduling of batch processors in semiconductor. The International Journal of Advanced Manufacturing Technology, 29(9-10):990--1001, 2006.

[20]

K. Pruhs, J. Robert, and N. Schabanel. Minimizing maximum flowtime of jobs with arbitrary parallelizability. In WAOA '10, pages 237--248, 2010.

Digital Library

[21]

J. Wong. Broadcast delivery. Proceedings of the IEEE, 76(12):1566--1577, 1988.

Cited By

Shi HChen L(2022)Downlink Transmission Scheduling With Data SharingIEEE/ACM Transactions on Networking10.1109/TNET.2021.313894030:3(1193-1202)Online publication date: Jun-2022
https://doi.org/10.1109/TNET.2021.3138940
Shi HChen L(2022)On Batching Task Scheduling2022 IEEE Real-Time Systems Symposium (RTSS)10.1109/RTSS55097.2022.00017(79-91)Online publication date: Dec-2022
https://doi.org/10.1109/RTSS55097.2022.00017
Im SOh HShadloo M(2016)Minimizing the maximum flow time in batch schedulingOperations Research Letters10.1016/j.orl.2016.09.01644:6(784-789)Online publication date: Nov-2016
https://doi.org/10.1016/j.orl.2016.09.016

Index Terms

Online batch scheduling for flow objectives
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

SPAA '13: Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures

July 2013

348 pages

ISBN:9781450315722

DOI:10.1145/2486159

General Chair:
Guy Blelloch
Carnegie Mellon University, USA
,
Program Chair:
Berthold Vöcking
RWTH Aachen University, Germany

Copyright © 2013 ACM.

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Published: 23 July 2013

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July 23 - 25, 2013

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SPAA '13 Paper Acceptance Rate 31 of 130 submissions, 24%;

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Cited By

Shi HChen L(2022)Downlink Transmission Scheduling With Data SharingIEEE/ACM Transactions on Networking10.1109/TNET.2021.313894030:3(1193-1202)Online publication date: Jun-2022
https://doi.org/10.1109/TNET.2021.3138940
Shi HChen L(2022)On Batching Task Scheduling2022 IEEE Real-Time Systems Symposium (RTSS)10.1109/RTSS55097.2022.00017(79-91)Online publication date: Dec-2022
https://doi.org/10.1109/RTSS55097.2022.00017
Im SOh HShadloo M(2016)Minimizing the maximum flow time in batch schedulingOperations Research Letters10.1016/j.orl.2016.09.01644:6(784-789)Online publication date: Nov-2016
https://doi.org/10.1016/j.orl.2016.09.016

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