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...
Uloženo v:
| Vydáno v: | International journal of communication systems Ročník 27; číslo 12; s. 4144 - 4165 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Chichester
Blackwell Publishing Ltd
01.12.2014
Wiley Subscription Services, Inc |
| Témata: | |
| ISSN: | 1074-5351, 1099-1131 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | 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. |
|---|---|
| Bibliografie: | 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 |
| ISSN: | 1074-5351 1099-1131 |
| DOI: | 10.1002/dac.2603 |