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FAWN: A Fast Array of Wimpy Nodes Authors: David G. Andersen et al. Offence: Jaime Espinosa Chunjing Xiao.

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Presentation on theme: "FAWN: A Fast Array of Wimpy Nodes Authors: David G. Andersen et al. Offence: Jaime Espinosa Chunjing Xiao."— Presentation transcript:

1 FAWN: A Fast Array of Wimpy Nodes Authors: David G. Andersen et al. Offence: Jaime Espinosa Chunjing Xiao

2 Why FAWN Not Increasing CPU-I/O Gap CPU power consumption grows super-linearly with speed. Dynamic power scaling on traditional systems is surprisingly inefficient 2 A lot of research in parallel I/O They focus on workloads that are I/O, not computation, intensive. Electric cars consumes less power, but why you don’t buy it?

3 Poor scaling characteristics 3 The system includes a number of relatively high powered front-end systems Analysis has shown that for data-intensive workloads, large wimpy node clusters suffer from poor scaleup effects, –Because they are more affected by a diminishing return scaleup effect than a smaller traditional cluster* *Wimpy Node Clusters: What About Non-Wimpy Workloads (3.5.4 Discussion)

4 Limitations(1) Only focus on read-mostly workloads (simple key-value workloads). They can not provide complex processing workload and it is bad for write-most workloads. 4

5 Limitations(2) Works only for small data and small CPU work-loads Conclusions from author: not going to replace current data-center, does not work for real- time applications (ie. gaming) Does not have ACID property that is desired in data bases (Atomicity Consistency Isolation Durability) 5

6 Reliability problems More nodes & hardware components leads to more failures –less memory per node than traditional systems –conversely more nodes are required for the same capacity. Communication, link and switch failure not considered

7 Flash Problems (cost) Why did they only examine 3-year total cost of ownership (TCO) in Section 5? flash storage has short lifetime –Flash is 15-20 times more expensive than HDD.* –the smaller flash cells are less reliable and less durable.** *http://www.genomeweb.com/informatics/no-flash-panhttp://www.genomeweb.com/informatics/no-flash-pan ** RETHINKING FLASH IN THE DATA CENTER

8 Flash Problems (Size) The amount of physical space per megabyte is a problem –Thermodynamically requires more energy It takes longer to heat a large room than a small one –Environmental foot-print is relative to area needed * RETHINKING FLASH IN THE DATA CENTER

9 Flash Problems (translation layer) Through heroic engineering and daunting complexity, the flash translation layer masks these problems, but its performance impact can be significant. –Intel’s Extreme SSDs have a read latency of 85 ms, but the flash chips the drive uses internally have a read latency of just 25 to 35 ms.* –Flash translation layer is part of the flash controller and is embedded in flash chips and drives * RETHINKING FLASH IN THE DATA CENTER

10 Race Conditions Another study* from CMU found that the system leads to race conditions *dBug: Systematic evaluation of Distributed Systems

11 Conclusion It is a great system for quickly finding tiny amounts of data provided you have a lot of real-estate and don’t mind the high probability of failure.

12 Thank You


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