Cloud MapReduce:A MapReduce

Implementationon top of a

Cloud Operation System

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成100062229 章博

遠2011, 11th IEEE/ACM International Symposium on

Huan Liu, Dan OrbanAccenture Technology Labs

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OUTLINE

I. IntroductionII. Cloud MapReduceArchitecture & ImplementationIII. Pros & Cons of Cloud MapReduceIV. Experimental EvaluationV. Conclusions & Future WorksVI. References

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INTRODUCTION

1. What is Cloud OS ?

2. Challenges posed by a cloud OS

3. Cloud MapReduce?

4. Advantages of Cloud MapReduce

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What is Cloud OS ?

1.Managing the low level cloud resources

2.Presenting a high level interface to

the application programmers3.key difference ： scalable

圖一

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Challenges posed by a cloud OS

1.Scalability comes at a price.

2.Data consistency, system availability, and tolerance to network partition.

圖二

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Cloud MapReduce?

1.MapReduce programming model

2.horizontal scaling

3.eventual consistency

4.overcome limitations

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Advantages of Cloud MapReduce1.Incremental scalability:

Can scale incrementally in the number of computing nodes.

2.Symmetry and Decentralization:Node has the same set of responsibilities.

3.Heterogeneity:Nodes have varying computation capacity.

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Cloud MapReduceArchitecture and Implementation1.The architecture

2.Cloud challnenges

3.General solution approaches

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The Architecture

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Cloud challenges &General solution approaches

1.Long latency

2.Horizontal scaling

3.Don’t know when a queue is created for the first time

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Con’t

4.Duplicate message

5.Potential node failure

6.Indeterminstic eventual consistency windows

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Pros

●3000 lines of Java code(L.O.C) vs 285375 Hadoop L.O.C

●Large & Reliable FS

●High Bandwidth(fast read/write)

●Single point of contact(high throughput)

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Cons

●Uses only network(no local storage)

●Leads to bottleneck

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Evaluation

Almost twice as fast!

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Evaluation

● Hadoop - 385s total, network/CPU under utilized● CMR - 210s, more efficient network/CPU usage

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Evaluation

Wiki Word Count

●Combiner:Hadoop - 747sCMR - 436s

●No Combiner:Hadoop - 1733sCMR - 1247s

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Evaluation

Amazon

●Word Count -> 400GB using 100 nodes●Approx. 1hr●983,152 Requests -> $0.98

●Using SimpleDB?●3.7hrs -> $0.52

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Evaluation

Comparison

●Distributed Grep Word Count -> 13GB of data●CMR = 962 seconds●Hadoop 1047 seconds

●Results are almost the same, why?●More CPU intensive tasks

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Evaluation

12GB - 923670 HTML files

●Hadoop -> 6hrs+

●CMR -> 297 seconds

●Hadoop - High overhead from task creation

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Conclusion

●Cloud cannot be implemented on any system●Poor Performance

●CMR techniques overcome cloud limitations●0 Performance Degradation●Good to use for other systems

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REFERENCES

圖一： http://techcrunch.com/

圖二： http://blog.csdn.net/zouqingfang/article/details/7269920

http://zh.wikipedia.org/

https://code.google.com/p/cloudmapreduce/

http://searchcloudcomputing.techtarget.com/definition/MapReduce

http://myblog-maurice.blogspot.tw/2012/08/nosqlcap.html

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