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A Novel Parallel Traffic Control Mechanism for Cloud Computing
Zheng Li, Nenghai Yu, Zhuo Hao
MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China
A Novel Parallel Traffic Control Mechanism for Cloud Computing - - PowerPoint PPT Presentation
A Novel Parallel Traffic Control Mechanism for Cloud Computing - - PowerPoint PPT Presentation
A Novel Parallel Traffic Control Mechanism for Cloud Computing Zheng Li, Nenghai Yu, Zhuo Hao MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Outline Introduction
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Outline
Introduction Weaknesses of HTB Parallel HTB Experiments
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Traffic Control in Cloud Computing
Control the outbound bandwidth
require an effective bandwidth management traffic scheduler & shaper
Hierarchical Service
idea of cloud computing different service level an attempt of customized SLAs on bandwidth
A Contradiction
different service levels vs. user experience a possible solution : HTB
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Hierarchical Token Bucket
HTB
a traffic control algorithm currently implemented in Linux kernel a module of TC (Traffic Control)
Basic idea
bandwidth borrowing make full use of resource a solution for the contradiction hierarchical service & better user experience
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CBQ vs. HTB
CB CBQ (AR) HT HTB (AR/CR CR)
1Gbps bps 1Gbps bps 400 400Mbps bps 600 600Mbps bps
200 200Mbps bps 200 200Mbps bps 200 200Mbps bps 400 400Mbps bps
400 400/600 600Mbps bps 600 600/800 800Mbps bps
200 200/400 400 Mbps bps 200 200/400 400 Mbps bps 200 200/600 600 Mbps bps 400 400/800 800 Mbps bps
HTB allows bandwidth borrowing to break AR!
CBQ – Class Based Queueing HTB – Hierarchical Token Bucket
(300 300 200 200) (100 100 400 400) (200 200 200 200) (100 100 200 400)
[300 300] [400]
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Outline
Introduction Weaknesses of HTB Parallel HTB Experiments
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Wea Weakne nesses o s of HTB HTB
Processing speed
500Mbps at most not eligible for cloud computing
Reasons
the inherent limitation of sequential program usage of spin-lock in kernel
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Outline
Introduction Weaknesses of HTB Parallel HTB Experiments
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Basic Idea
Lock-free FIFOs based pipelining
port HTB from kernel to user space based on multi-core architecture try to eliminate necessity of using locks reduce concurrency selectively apply lock-free structures make it run in a 1-way 2-stage pipeline fashion
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Eliminate Locks
Basic 2 operations of HTB: enque & deque Remove htb_activate and htb_deactivate in the 2 operations Critical region is reduced to only the packet queues A tradeoff: using locks but no empty queues vs. elimate locks to parallelize HTB but might exist empty queues
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Lock-free FIFOs
Selectively used as the packet queue Eliminate time of lock/unlock operations Make it possible for HTB to run in a pipelined fashion We haven’t adopted the advanced cache-line distance and cache-line aggregation techniques in [1], because unnecessary
Stage age1 Stage age2 Loc Lock-free ee FIFO enque enque
…… ……
deque deque
[1] J. Giacomoni, T. Moseley, and M. Vachharajani, “Fastforward for efficient pipeline parallelism: A cache-optimized concurrent lock-free queue”, Proc. of PPoPP’08, New York, NY, USA, February 2008, pp.43-52
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Outline
Introduction Weaknesses of HTB Parallel HTB Experiments
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Bandwidth Allocation
2 Scenarios: 1Gbps bandwidth & 2Gbps bandwidth The number of users of Scenario 2 are 2 times of that of Scenario 1 Bandwidth for a user is 0.5Mbps/1Mbps and 2Mbps/12Mbps, for common service(require low band) and special service(require high band) Trace files are used in the experiments … … … … … … … … … … …
1G/1G 125 125M/650 650M*8 2.5M/13 13M*50 50 0.5M/1M 2M/12 12M TOTAL L BANDW NDWIDT DTH US USER R GRO ROUP UP US USER APPLI LICA CATION
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Results
Exp.1 ~ Exp.4: 1Gbps. Exp.5 ~ Exp.6: 2Gbps Exp.1: all users have traffics. Exp.2: 2/3 of users have traffics Exp.3 ~ Exp.4: 64B pkt len. Exp.3: use parallel HTB, Exp.4: use HTB Exp.5 :all users have traffics. Exp.6: 2/3 of users have traffics FILE #Packets #Pkt Len. #Max Len. #Min Len. #Traffic File-1 2,397,696 782 1500 64 800 File-2 2,397,696 782 1500 64 533 File-3 9,765,925 64 64 64 800 File-4 4,795,392 782 1500 64 1600 File-5 4,795,392 782 1500 64 1067 Exp. #Trace #MPPS #Mbps #Enq. #Deq. 1 File-1 1.29 1008 0.39 0.54 2 File-2 1.29 1006 0.39 0.57 3 File-3 14.1 941 0.39 0.53 4 File-3 6.7 427 0.64 1.11 5 File-4 2.60 2033 0.39 0.54 6 File-5 2.59 2026 0.39 0.58 Parallel HTB can reach 2Gbps for common packet lengths, 300% improvement of the traditional HTB
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Results
Output traffic rate of the total traffic Output traffic rate of a selected user
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