Datacast: A Scalable and Efficient Reliable Group Data Delivery Service for Data Centers Jiaxin Cao, Chuanxiong Guo, Guohan Lu, Yongqiang Xiong, Yixin Zheng, Yongguang Zhang, Yibo Zhu, Chen Chen University of Science and Technology of China
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Datacast: A Scalable and Efficient Reliable Group Data Delivery Service for Data Centers Jiaxin Cao, Chuanxiong Guo, Guohan Lu, Yongqiang Xiong, Yixin Zheng, Yongguang Zhang, Yibo Zhu, Chen Chen University of Science and Technology of China
Microsoft Research Asia
Tsinghua University
University of California, Santa Barbara
University of Pennsylvania<br>
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Reliable Group Data Delivery The problem of RGDD is: <0,0> 01 02 03 10 11 12 13 20 21 22 23 30 31 32 33 <1,0> <1,1> <1,2> <1,3> <0,1> <0,2> <0,3> 00 given a data source, Src, and a set of receivers, R1, R2, …, Rn, how to reliably transmit bulk data from Src to all the receivers? In a data center network, Data Data Data Data Data Data<br>
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Reliable Group Data Delivery RGDD is important in DCNs:
Bootstrapping or OS upgrading.
Distributed file systems, e.g., GFS.
VM setup.
And more...<br>
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Reliable Group Data Delivery A good RGDD design should have the following properties:
Scalable (large group numbers and large group sizes)
High bandwidth efficiency<br>
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Existing solutions to RGDD Existing solutions can be classified into two categories: Reliable IP multicast. Not scalable, e.g., ACK implosion. End-host based overlays. Low bandwidth efficiency. None of the existing systems can perfectly achieve RGDD.<br>
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New opportunities in DCN Recently, there are two clear trends in DCN:
Multiple edge-disjoint Steiner trees for RGDD.
Practical packet caching abilities in network devices. We can cache packet! <0,0> 00 01 02 03 <0,1> 10 11 12 13 <0,2> 20 21 22 23 <0,3> 30 31 32 33 <1,1> <1,2> <1,3> <1,0> 00 10 20 30 <1,1> 01 11 21 31 <1,2> 02 12 22 32 <1,3> 03 13 23 33 <0,1> <0,2> <0,3><br>
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The architecture of Datacast Fabric Manager Master i Master j Src R1 R2 RGDD Group i1 RGDD Group i2 RGDD Group in Network
Topology How to calculate multiple Steiner trees? How to efficiently transmit data in each Steiner tree?<br>
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Multiple edge-disjoint Steiner trees in DCN Our multiple Steiner trees algorithm takes three steps:
Use specific algorithms to construct spanning trees.
Prune the spanning trees.
Use Breath First Search(BFS) to repair the trees broken by network failures.
This algorithm is fast (O(k|V|) + O(|E|) + O(k|E|)) and efficient.<br>
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Datacast transport protocol Datacast is built on top of Content Centric Network (CCN): 00 01 02 03 10 11 12 13 20 21 22 23 30 31 32 33 Inst Data Data Data Inst Data Data Inst Data Inst Data Data Data<br>
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Datacast transport protocol<br>
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Datacast transport protocol<br>
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Datacast transport protocol<br>
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Datacast transport protocol<br>
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Simulation: multiple Steiner trees algorithm We tested our algorithm in Fattree(24,3), BCube(8, 3), Torus(16, 3) under the link failure rates (LFR) of 1%, 3% and 5%. Running times. Steiner tree numbers.<br>
Simulation: Datacast congestion control Based on Theorem 1, Datacast needs 125KB caches to work at full rate.
Based on Theorem 2, the duplicate data ratios is 1.19%.<br>
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Simulation: Datacast congestion control Compare with BitTorrent. Fattree. BCube. Torus.<br>
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Experiment: Datacast congestion control<br>
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Experiment: Datacast congestion control We compare Datacast with BitTorrent. We use both of them to transmit 4GB data.<br>
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Related work Reliable IP multicast
Pgm congestion control (pgmcc)
Active Reliable Multicast (ARM)
End-host based overlays
SplitStream
End System Multicast
Cornet<br>
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Conclusion In this paper, we propose Datacast which
Calculates multiple edge-disjoint Steiner trees in DCNs
Uses CCN to turn hard group states to soft packet caching
Uses a simple rate-based AIMD congestion control algorithm to achieve high efficiency
Datacast is scalable and achieves high bandwidth efficiency<br>
