Efficient algorithms for scheduling multiple bulk data transfers in inter-datacenter networks

SUMMARYBulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter‐datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store‐and‐forward transfer mode offers the...

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Published in:	International journal of communication systems Vol. 27; no. 12; pp. 4144 - 4165
Main Authors:	Su, Sen, Wang, Yiwen, Jiang, Sujuan, Shuang, Kai, Xu, Peng
Format:	Journal Article
Language:	English
Published:	Chichester Blackwell Publishing Ltd 01.12.2014 Wiley Subscription Services, Inc
Subjects:	Algorithms Allocations Bandwidth bulk data transfers cloud computing Data transfer (computers) Dynamics graph theory and algorithms inter-datacenter networks Networks Optimization optimization models and methods Utilization
ISSN:	1074-5351, 1099-1131
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Abstract	SUMMARYBulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter‐datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store‐and‐forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter‐datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max–min fairness and aim at computing the lexicographically maximized solution. Leveraging the time‐expanded technique, the problem in dynamic networks is formulated as a static multi‐flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select an appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max–min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time. Copyright © 2013 John Wiley & Sons, Ltd. Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter‐datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store‐and‐forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter‐datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max–min fairness and aim at computing the lexicographically maximized solution. Leveraging the time‐expanded technique, the problem in dynamic networks is formulated as a static multi‐flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max–min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time.
AbstractList	SUMMARY Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter-datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store-and-forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter-datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max-min fairness and aim at computing the lexicographically maximized solution. Leveraging the time-expanded technique, the problem in dynamic networks is formulated as a static multi-flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select an appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max-min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time. Copyright © 2013 John Wiley & Sons, Ltd. Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter-datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store-and-forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter-datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max-min fairness and aim at computing the lexicographically maximized solution. Leveraging the time-expanded technique, the problem in dynamic networks is formulated as a static multi-flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select an appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max-min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time. Copyright copyright 2013 John Wiley & Sons, Ltd. Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter-datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store-and-forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter-datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max-min fairness and aim at computing the lexicographically maximized solution. Leveraging the time-expanded technique, the problem in dynamic networks is formulated as a static multi-flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max-min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time. Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter‐datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store‐and‐forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter‐datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max–min fairness and aim at computing the lexicographically maximized solution. Leveraging the time‐expanded technique, the problem in dynamic networks is formulated as a static multi‐flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select an appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max–min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time. Copyright © 2013 John Wiley & Sons, Ltd. SUMMARYBulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter‐datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store‐and‐forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter‐datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max–min fairness and aim at computing the lexicographically maximized solution. Leveraging the time‐expanded technique, the problem in dynamic networks is formulated as a static multi‐flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select an appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max–min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time. Copyright © 2013 John Wiley & Sons, Ltd. Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter‐datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store‐and‐forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter‐datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max–min fairness and aim at computing the lexicographically maximized solution. Leveraging the time‐expanded technique, the problem in dynamic networks is formulated as a static multi‐flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max–min fairness, the network utilization is significantly improved while honoring each individual performance; (ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time.
Author	Xu, Peng Jiang, Sujuan Wang, Yiwen Shuang, Kai Su, Sen
Author_xml	– sequence: 1 givenname: Sen surname: Su fullname: Su, Sen email: Correspondence to: Sen Su, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 10 Xi Tu Cheng Road, Hai Dian District, Beijing, China., susen@bupt.edu.cn organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 10 Xi Tu Cheng Road, Beijing, China – sequence: 2 givenname: Yiwen surname: Wang fullname: Wang, Yiwen organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 10 Xi Tu Cheng Road, Beijing, China – sequence: 3 givenname: Sujuan surname: Jiang fullname: Jiang, Sujuan organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 10 Xi Tu Cheng Road, Beijing, China – sequence: 4 givenname: Kai surname: Shuang fullname: Shuang, Kai organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 10 Xi Tu Cheng Road, Beijing, China – sequence: 5 givenname: Peng surname: Xu fullname: Xu, Peng organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 10 Xi Tu Cheng Road, Beijing, China
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Copyright	Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd.
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Notes	Some preliminary result of this paper was published at the IEEE ICC Workshop on Clouds, Networks and Data Centers (ICC'12 WS - CloudNetsDC). In this paper, we have made the following improvements. First, for multiple bulk data transfers problem, we prove the optimality of our max-min fair multi-transfer algorithm. Second, we devise an efficient heuristic algorithm to achieve the near optimal solution within practical time. Third, more extensive simulations are conducted to evaluate our heuristic algorithm and to investigate the relationship between the way in selecting paths for routing and the allocation rate ones obtained from these paths. ArticleID:DAC2603 ark:/67375/WNG-GM61C4WF-Q istex:98D510C712515F723F6610C78DA1DD9D8CEAFA11 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
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References_xml	– reference: Vygen J. Np-completeness of some edge-disjoint paths problems. Discrete Applied Mathematics 1995; 61(1):83-90. – reference: Nace D, Nhat Doan L, Klopfenstein O, Bashllari A. Max-min fairness in multi-commodity flows. Computers & Operations Research 2008; 35(2):557-573. – reference: Dijkstra E. A note on two problems in connexion with graphs. Numerische Mathematik 1959; 1(1):269-271. – reference: Hu RQ, Hu W, Jin M, Qian Y. Wavelength retuning without service interruption in an all-optical survivable network. International Journal of Communication Systems 2009; 22(6):719-738. – reference: Shahrokhi F, Matula D. The maximum concurrent flow problem. Journal of the ACM (JACM) 1990; 37(2):318-334. – reference: Freeman L. A set of measures of centrality based on betweenness. Sociometry 1977; 40:35-41. – reference: Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D. Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America 2004; 101(9):2658-2663. – reference: Bertsekas D, Gallager R. Data Networks. Prentice-Hall: New Jersey, 1987. – reference: Ma Q, Steenkiste P, Zhang H. Routing high-bandwidth traffic in max-min fair share networks. ACM Sigcomm Computer Communication Review 1996; 26:206-217. – reference: Adami D, Callegari C, Giordano S, Pagano M, Pepe T. Skype-hunter: a real-time system for the detection and classification of skype traffic. International Journal of Communication Systems 2012; 25(3):386-403. (Available: from: http://dx.doi.org/10.1002/dac.1247). – reference: Rimal BP, Choi E. A service-oriented taxonomical spectrum, cloudy challenges and opportunities of cloud computing. International Journal of Communication Systems 2012; 25(6):796-819. – reference: Greenberg A, Hamilton J, Maltz D, Patel P. The cost of a cloud: research problems in data center networks. ACM SIGCOMM Computer Communication Review 2008; 39(1):68-73. – reference: Ford Jr L, Fulkerson D. Constructing maximal dynamic flows from static flows. Operations Research 1958; 6:419-433. – reference: Dunn D, Grover W, MacGregor M. Comparison of k-shortest paths and maximum flow routing for network facility restoration. IEEE Journal on Selected Areas in Communications 1994; 12(1):88-99. – reference: Allalouf M, Shavitt Y. Centralized and distributed algorithms for routing and weighted max-min fair bandwidth allocation. IEEE/ACM Transactions on Networking 2008; 16(5):1015-1024. – reference: Molnr S, Pernyi M. On the identification and analysis of skype traffic. International Journal of Communication Systems 2011; 24(1):94-117. (Available: from: http://dx.doi.org/10.1002/dac.1142). – reference: Ford Jr L, Fulkerson DR. Flows in networks. Physics Today 1963; 16:54. – reference: Rajah K, Ranka S, Xia Y. Advance reservations and scheduling for bulk transfers in research networks. 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Snippet	SUMMARYBulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter‐datacenter traffic. These bulk transfers... Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter‐datacenter traffic. These bulk transfers... SUMMARY Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter-datacenter traffic. These bulk... Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter-datacenter traffic. These bulk transfers...
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SubjectTerms	Algorithms Allocations Bandwidth bulk data transfers cloud computing Data transfer (computers) Dynamics graph theory and algorithms inter-datacenter networks Networks Optimization optimization models and methods Utilization
Title	Efficient algorithms for scheduling multiple bulk data transfers in inter-datacenter networks
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