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研究生:郭哲岳
研究生(外文):Che-Yueh Kuo
論文名稱:應用於資料中心之負載平衡演算法
論文名稱(外文):Intra Data Center Path Load Balancing
指導教授:張貴雲張貴雲引用關係
指導教授(外文):Guey-Yun Chang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:39
中文關鍵詞:資料中心架構負載平衡分散式
外文關鍵詞:Datacenter fabricLoad balancingDistributed
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在數據中心中,負載平衡是一個很重要的技術,用來處理動態與不
可預測的交通需求量。一般而言,負載平衡的目標是分配相等的交通
量到多重路徑上。然而,大多數的方法都受制於封包亂序或者快速回
應。近年來,Flare 引進基於flowlet 的分流方法,它達到快速回應且不
造成封包亂序。但是,資料中心內的高頻寬環境造成產生flowlet 的間
隔減少。除此之外,分流的細膩度會隨著交通量變大而變粗,在此篇
論文中,我們提出一個人工flowlet 為基底的負載平衡演算法,其能保
持好的分流細膩度且避免封包亂序,在實驗中顯示,我們的方法在流
的完成時間好20%。
Load balancing is an important technique to cope with dynamic and unpredictable
traffic demands in data center networks. In general, load balancing
schemes aim to split traffics evenly among multiple paths. However, most
existing approaches either suffers from packet reordering (which may confuse
TCP congestion control) or fail to quick response (i.e., coarse slicing
granularity). Recently, FLARE introduced a burst (called flowlet) based traffic
splitting, which attains responsiveness without causing packet reordering.
However, the very high bandwidth of internal datacenter flows suggests that
the gaps needed for flowlets may be rare. Besides, in Flare, splitting granularity
increases (i.e., coarse granularity) when flow size increases. In this
paper, we propose an artificial flowlet-based load balancing algorithm which
can maintain fine-granularity (even in large flows) and can also avoid packet
reordering. Our scheme has at least 20% improvement in flow completion
time under the same incidence of packet reordering.
中文摘要i
Abstract ii
致謝iii
Contents iv
List of Figures vi
List of Tables viii
1 Introduction 1
2 Related work and Preliminary 4
2.1 Flow-based Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Packet-based Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Sub-Flow-based Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Problem Statement 7
3.1 Traffic Splitting Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Environment Description . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4 Design 10
4.1 Artificial Flowlet-based Splitting . . . . . . . . . . . . . . . . . . . . . . 10
4.2 Slot-based Dequeue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
iv
4.3 Enqueue Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5 Practical Issue 16
5.1 Dequeue Ratio and Queue Occupancy . . . . . . . . . . . . . . . . . . . 16
5.2 Slot Boundary and Packet Size . . . . . . . . . . . . . . . . . . . . . . . 17
6 Simulation 19
6.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6.2 Parameter Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6.3 Comparison Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.3.1 Artificial Flowlet vs. Spontaneous Flowlet . . . . . . . . . . . . 21
6.3.2 Load Balancing Efficiency . . . . . . . . . . . . . . . . . . . . . 22
6.3.3 Reordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.3.4 Flow Completion Time (FCT) . . . . . . . . . . . . . . . . . . . 24
6.3.5 Ovsersubscription . . . . . . . . . . . . . . . . . . . . . . . . . 24
7 Conclusion 27
Bibliography 28
[1] Mohammad Al-Fares, Sivasankar Radhakrishnan, Barath Raghavan, Nelson Huang,
and Amin Vahdat. Hedera: Dynamic flow scheduling for data center networks. In
Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation,
NSDI’10, pages 19–19, Berkeley, CA, USA, 2010. USENIX Association.
[2] K Papagiannakit, Nina Taft, and Christophe Diot. Impact of flow dynamics on traffic
engineering design principles. In INFOCOM 2004. Twenty-third AnnualJoint
Conference of the IEEE Computer and Communications Societies, volume 4, pages
2295–2306. IEEE, 2004.
[3] Matthew Roughan, Albert Greenberg, Charles Kalmanek, Michael Rumsewicz, Jennifer
Yates, and Yin Zhang. Experience in measuring backbone traffic variability:
Models, metrics, measurements and meaning. In Proceedings of the 2nd ACM SIGCOMM
Workshop on Internet measurment, pages 91–92. ACM, 2002.
[4] Srikanth Kandula, Dina Katabi, Shantanu Sinha, and Arthur Berger. Dynamic load
balancing without packet reordering. ACM SIGCOMM Computer Communication
Review, 37(2):51–62, 2007.
[5] Mohammad Alizadeh, Tom Edsall, Sarang Dharmapurikar, Ramanan Vaidyanathan,
Kevin Chu, Andy Fingerhut, Francis Matus, Rong Pan, Navindra Yadav, George
Varghese, et al. Conga: Distributed congestion-aware load balancing for datacenters.
In ACM SIGCOMM Computer Communication Review, volume 44, pages 503–514.
ACM, 2014.

[6] Costin Raiciu, Sebastien Barre, Christopher Pluntke, Adam Greenhalgh, Damon
Wischik, and Mark Handley. Improving datacenter performance and robustness with
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pages 266–277. ACM, 2011.
[7] Hong Xu and Baochun Li. Repflow: Minimizing flow completion times with replicated
flows in data centers. In IEEE INFOCOM 2014-IEEE Conference on Computer
Communications, pages 1581–1589. IEEE, 2014.
[8] David Zats, Tathagata Das, Prashanth Mohan, Dhruba Borthakur, and Randy Katz.
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[9] Jiaxin Cao, Rui Xia, Pengkun Yang, Chuanxiong Guo, Guohan Lu, Lihua Yuan,
Yixin Zheng, Haitao Wu, Yongqiang Xiong, and Dave Maltz. Per-packet loadbalanced,
low-latency routing for clos-based data center networks. In Proceedings of
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CoNEXT ’13, pages 49–60, New York, NY, USA, 2013. ACM.
[10] Stanislav Rost and Hari Balakrishnan. Rate-aware splitting of aggregate traffic.
Technical report, Technical report, MIT, 2003.
[11] Mohammad Alizadeh, Shuang Yang, Milad Sharif, Sachin Katti, Nick McKeown,
Balaji Prabhakar, and Scott Shenker. pfabric: Minimal near-optimal datacenter
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[12] Chi-Yao Hong, Matthew Caesar, and P Godfrey. Finishing flows quickly with preemptive
scheduling. ACM SIGCOMM Computer Communication Review, 42(4):
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