Embedded System Lab.

Jung YoungJincyj4369@gmail.com

Janus: Optimal Flash Provisioning for Cloud Storage Workloads

C. Albrecht, A. Merchant, M. Stokely, M. Waliji, F. Labelle, N. Coehlo, X. Shi, and C. E. Schrock.

In Proceedings of the annual conference on USENIX Annual Technical Conference, ATC ’13, Berkeley, CA, USA, 2013. USENIX Association.

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Introduction System description Workload characterization Economics and provisioning Optimizing the flash allocation for

workloads Optimization with bounded write rates Evaluation Conclusion

Contents

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HDD & SSD Disks are slow, even as their capacities grow We can compensate for this by adding flash storage

Large cloud environment Many user Many workload Distributing the available flash capacity

uniformly between the workloads is not ideal

Introduction

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Janus? Provides flash storage allocation recommendations for

workloads in a distributed file system Used in distributed file system, GFS, Colosus …

Google workload Analyzed workload characterizations

Most I/O accesses -> recently created files 28% of read operations -> 1% data

Files are placed in the flash upon creation

Introduction

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Recommendation Runs periodically to adjust Many read operation -> flash storage Key input

Age of data Read rate of the data by age

Janus work step Collect age of data and characterization of how cacheable

each workload Allocate flash amongst the workloads Coordination with the distributed file system

System description

HybridStorage

Colosus orGFS

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Workload A large application have many job

Need to define a metric that lets us compare how many read operations would be served

Workload characterization

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Cacheability functions FIFO eviction instance

How much data there is of a given age How many reads there are to files of a given age

LRU eviction instance Amount of data with a given temporal locality Rate of reads to files with that temporal locality(time gap)

Workload characterization

SIGELMAN, B. H., ET AL. Dapper, a large-scale distributed systems tracing infrastructure. rep., Google, Inc., 2010.

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Obtaining instance From file system metadata From trace sample

Function input/output Input : size of data Output : the number of read operations

Workload characterization

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Peak IOPS and capacity requirementsEconomics and provisioning

Cost effective to put workloads

entirely in flashCost effective to hot portions ofthe data on flash

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Determine the best flash allocation for each workload

Primary goal Find maximize the aggregate rate of read operations

Instance Workloads with cacheability function Total flash capacity

Task Allocate flash to workloads to maximize the weighted flash

read rate

Optimizing the flash allocation for workloads

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Secondary goal Bound the flash write rate to reduce flash wear

Instance Workloads with cacheability function and write rate Bound on the flash write rate Total flash capacity

Task Allocate flash to workloads and determine write probability

for each workload to maximize the flash read rate

Optimization with bounded write rates

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Flash hit rate during trainingEvaluation

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Flash usage and flash read rate for one workload over time

Evaluation

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Comparison of flash hit rates for alternative allocation methods

Evaluation

Allocation MethodCell A

(low workload variance)

Cell B(high workload

variance)Optimized 28% 74%

Proportional to read rate 26% 64%

Single FIFO 19% 42%Proportional to data size 14% 15%

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2013 USENIX Annual Technical Conference, Presentation video

Reference

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Q & A