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BlueSky: A Cloud-Backed File System for the Enterprise
Michael Vrable Stefan Savage Geoffrey M. Voelker
University of California, San DiegoComputer Science and Engineering Department
February 16, 2012
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Computing Services for the Enterprise
I Our work is focused primarily on small/medium-sized organizations
I These organizations run a number of computing services, such ase-mail and shared file systems
I Often brings significant cost:I Purchasing hardwareI Operating hardwareI Managing services
I Outsourcing these services to the cloud offers the possibility to lowercosts
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. . . Migrated to the Cloud
Some services are already migrating to the cloud. . .
Network file systems have not yet migrated, but still have potentialbenefits:
I File system size entirely elastic: simpler provisioning
I Cloud provides durability for file system data
I Hardware reliability less important
I Integration with cloud backup
We build and analyze a prototype system, BlueSky, to investigate how todo so
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. . . Migrated to the Cloud
Some services are already migrating to the cloud. . .
Network file systems have not yet migrated, but still have potentialbenefits:
I File system size entirely elastic: simpler provisioning
I Cloud provides durability for file system data
I Hardware reliability less important
I Integration with cloud backup
We build and analyze a prototype system, BlueSky, to investigate how todo so
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Cloud Computing Offerings
Spectrum of service models:
I Software-as-a-Service: Complete integrated service from a provider
I Platform/Infrastructure-as-a-Service: Building blocks for customapplications
In both cases:
I Infrastructure moved within network
I Reduce/eliminate need for hardware maintenance
I Reduce need for ahead-of-time capacity planning
SaaS: Easy to set upPaaS/IaaS: More choice among service providers, potentially lower cost
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Cloud Computing Offerings
Spectrum of service models:
I Software-as-a-Service: Complete integrated service from a provider
I Platform/Infrastructure-as-a-Service: Building blocks for customapplications
In both cases:
I Infrastructure moved within network
I Reduce/eliminate need for hardware maintenance
I Reduce need for ahead-of-time capacity planning
SaaS: Easy to set upPaaS/IaaS: More choice among service providers, potentially lower cost
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Cloud Computing Offerings
Spectrum of service models:
I Software-as-a-Service: Complete integrated service from a provider
I Platform/Infrastructure-as-a-Service: Building blocks for customapplications
In both cases:
I Infrastructure moved within network
I Reduce/eliminate need for hardware maintenance
I Reduce need for ahead-of-time capacity planning
SaaS: Easy to set upPaaS/IaaS: More choice among service providers, potentially lower cost
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Cloud Computing Offerings
Spectrum of service models:
I Software-as-a-Service: Complete integrated service from a provider
I Platform/Infrastructure-as-a-Service: Building blocks for customapplications
In both cases:
I Infrastructure moved within network
I Reduce/eliminate need for hardware maintenance
I Reduce need for ahead-of-time capacity planning
SaaS: Easy to set upPaaS/IaaS: More choice among service providers, potentially lower cost
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Challenges
Cloud storage (e.g., Amazon S3) acts much like another level in thestorage hierarchy but brings new design constraints:
I New interfaceI Only supports writing complete objectsI Does support random read access
I PerformanceI High latency from network round tripsI Random access adds little penalty
I SecurityI Data privacy is a concern
I CostI Cost is very explicitI Unlimited capacity, but need to delete to save money
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BlueSky: Approach
I For ease of deployment, do not changesoftware stack on clients
I Clients simply pointed at a new server,continue to speak NFS/CIFS
I Deploy a local proxy to translate requestsbefore sending to the cloud
I Provides lower-latency responses toclients when possible by caching data
I Implements write-back cachingI Encrypts data before storage to cloud
for confidentiality
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BlueSky: Approach
I BlueSky adopts a log-structured designI Each log segment uploaded all at onceI Random access allowed for downloads
I Log cleaner can be run in the cloud (e.g.,on Amazon EC2) for faster, cheaperaccess to storage
I Log cleaner can run concurrently withactive proxy
I Cleaner not given full access to filesystem data
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File System Design
Checkpoint
Last segments seen: cleaner: 3 proxy: 12Inode maps: [0, 4095] [4096, 8191]
Inode map [0, 4095]
235611200
Inode 6
Type: regular fileOwner: rootSize: 48 KBData blocks: 0 1
Data Block
Inode number: 6Length: 32 KB
Data Block
Inode number: 6Length: 16 KB
Unencrypted Objects Encrypted Objects
Cloud Log Directories:
Proxy:
Cleaner:
Segment #11 #12
Segment #2 #3 #4
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Architecture
SegmentWrites
RangeReads
Disk J ournalWrites
Disk CacheReads
ClientRequests
ClientResponses
NFS
CIFS
S3
WASEncryption
Disk
Network
Memory
FrontEnds
BackEnds
ResourceManagers
Proxy
I Proxy internally buffers updates briefly in memory
I File system updates are serialized and journaled to local disk
I File system is periodically checkpointed: log items are aggregated intosegments and stored to cloud
I On cache miss, log items fetched back from cloud and stored on localdisk
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Cloud Storage Performance
I We are assuming that users will have fast connectivity to cloudproviders (if not now, then in the near future)
I Latency is a fundamental problem (unless cloud data centers builtnear to customers)
0.0001
0.001
0.01
0.1
1
10
100
1000
1 100 10000 1e+06 1e+08
Eff
ect
ive
Up
loa
d B
an
dw
idth
(M
bp
s)
Object Size (bytes)
1248
1632
I Network RTT: 30 ms tostandard (US-East) S3region, 12 ms to US-Westregion
I Proxy can fully utilizebandwidth to cloud
I Results argue for largerobjects, parallel uploads
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Application Performance
Simple benchmark: unpack Linux kernel sources, checksum kernel sources,compile a kernel
Unpack Check Compile(write) (read) (R/W)
Local NFS server 10:50 0:26 4:23NFS server in EC2
65:39 26:26 74:11
BlueSky/S3-Westwarm proxy cache
5:10 0:33 5:50
cold proxy cache
26:12 7:10
full segment prefetch
1:49 6:45
BlueSky/S3-Eastwarm proxy
5:08 0:35 5:53
cold proxy cache
57:26 8:35
full segment prefetch
3:50 8:07
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Application Performance
Simple benchmark: unpack Linux kernel sources, checksum kernel sources,compile a kernel
Unpack Check Compile(write) (read) (R/W)
Local NFS server 10:50 0:26 4:23NFS server in EC2 65:39 26:26 74:11BlueSky/S3-West
warm proxy cache
5:10 0:33 5:50
cold proxy cache
26:12 7:10
full segment prefetch
1:49 6:45
BlueSky/S3-Eastwarm proxy
5:08 0:35 5:53
cold proxy cache
57:26 8:35
full segment prefetch
3:50 8:07
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Application Performance
Simple benchmark: unpack Linux kernel sources, checksum kernel sources,compile a kernel
Unpack Check Compile(write) (read) (R/W)
Local NFS server 10:50 0:26 4:23NFS server in EC2 65:39 26:26 74:11BlueSky/S3-West
warm proxy cache 5:10 0:33 5:50cold proxy cache
26:12 7:10
full segment prefetch
1:49 6:45
BlueSky/S3-Eastwarm proxy
5:08 0:35 5:53
cold proxy cache
57:26 8:35
full segment prefetch
3:50 8:07
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Application Performance
Simple benchmark: unpack Linux kernel sources, checksum kernel sources,compile a kernel
Unpack Check Compile(write) (read) (R/W)
Local NFS server 10:50 0:26 4:23NFS server in EC2 65:39 26:26 74:11BlueSky/S3-West
warm proxy cache 5:10 0:33 5:50cold proxy cache 26:12 7:10full segment prefetch
1:49 6:45
BlueSky/S3-Eastwarm proxy
5:08 0:35 5:53
cold proxy cache
57:26 8:35
full segment prefetch
3:50 8:07
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Application Performance
Simple benchmark: unpack Linux kernel sources, checksum kernel sources,compile a kernel
Unpack Check Compile(write) (read) (R/W)
Local NFS server 10:50 0:26 4:23NFS server in EC2 65:39 26:26 74:11BlueSky/S3-West
warm proxy cache 5:10 0:33 5:50cold proxy cache 26:12 7:10full segment prefetch 1:49 6:45
BlueSky/S3-Eastwarm proxy
5:08 0:35 5:53
cold proxy cache
57:26 8:35
full segment prefetch
3:50 8:07
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Application Performance
Simple benchmark: unpack Linux kernel sources, checksum kernel sources,compile a kernel
Unpack Check Compile(write) (read) (R/W)
Local NFS server 10:50 0:26 4:23NFS server in EC2 65:39 26:26 74:11BlueSky/S3-West
warm proxy cache 5:10 0:33 5:50cold proxy cache 26:12 7:10full segment prefetch 1:49 6:45
BlueSky/S3-Eastwarm proxy 5:08 0:35 5:53cold proxy cache 57:26 8:35full segment prefetch 3:50 8:07
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Read Performance Microbenchmark
0
50
100
150
200
250
300
350
400
0 20 40 60 80 100
ReadLatency(ms)
Proxy Cache Size (% Working Set)
Single-Client Request Stream
32 KB128 KB1024 KB
I Read performance depends on working set/cache size ratio
I At 100% hit rate, comparable to local NFS server
I Even at 50% hit rate, latency within about 2× to 3× of local case
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Write Performance Microbenchmark
0
20
40
60
80
100
120
140
0 5 10 15 20 25 30 35
Ave
rag
e W
rite
La
ten
cy (
ms/
1 M
B w
rite
)
Client Write Rate (MB/s): 2-Minute Burst
Latency vs. Write Rate with Constrained Upload
128 MB Write Buffer1 GB Write Buffer
I Configure network to constrain bandwidth to cloud at 100 Mbps
I Write performance: similar to local disk, unless write rate exceedscloud bandwidth and write-back cache fills
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Aggregate Performance: SPECsfs2008
100
200
300
400
500
600
700
800
900
1000
0 200 400 600 800 1000 1200 1400 1600
0 10 20 30 40 50
AchievedOpe
rations
perS
econ
d
Requested Operations per Second
Working Set Size (GB)
Local NFS
I Models a richer workload mix
I BlueSky is comparable to local NFS (as before, slight advantage onwrites from log-structured design)
I Performance is less predictable with a constrained network link
I Fetching full segments is a big loss with mostly random access
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Aggregate Performance: SPECsfs2008
100
200
300
400
500
600
700
800
900
1000
0 200 400 600 800 1000 1200 1400 1600
0 10 20 30 40 50
AchievedOpe
rations
perS
econ
d
Requested Operations per Second
Working Set Size (GB)
Local NFSBlueSky
I Models a richer workload mix
I BlueSky is comparable to local NFS (as before, slight advantage onwrites from log-structured design)
I Performance is less predictable with a constrained network link
I Fetching full segments is a big loss with mostly random access
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Aggregate Performance: SPECsfs2008
100
200
300
400
500
600
700
800
900
1000
0 200 400 600 800 1000 1200 1400 1600
0 10 20 30 40 50
AchievedOpe
rations
perS
econ
d
Requested Operations per Second
Working Set Size (GB)
Local NFSBlueSky
BlueSky (100 Mbps)
I Models a richer workload mix
I BlueSky is comparable to local NFS (as before, slight advantage onwrites from log-structured design)
I Performance is less predictable with a constrained network link
I Fetching full segments is a big loss with mostly random access
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Aggregate Performance: SPECsfs2008
100
200
300
400
500
600
700
800
900
1000
0 200 400 600 800 1000 1200 1400 1600
0 10 20 30 40 50
AchievedOpe
rations
perS
econ
d
Requested Operations per Second
Working Set Size (GB)
Local NFSBlueSky
BlueSky (100 Mbps)BlueSky (norange)
I Models a richer workload mix
I BlueSky is comparable to local NFS (as before, slight advantage onwrites from log-structured design)
I Performance is less predictable with a constrained network link
I Fetching full segments is a big loss with mostly random access
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Monetary Cost: SPECsfs2008
Normalized cost: cost per million SPECsfs operations(for S3 prices: $0.12/GB download, $0.01/1000–10000 ops)
Down Op Total (Up)
Log-structured baseline $0.18 $0.09 $0.27 $0.56No aggregation 0.17 2.91 3.08 0.56Full segment downloads 25.11 0.09 25.20 1.00
I Log-structured design minimizes cost for cloud storage operations
I Support for random access on reads (byte-range request) needed forlow cost
I Storage cost also an important consideration, but less sensitive tosystem design
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Conclusions
I BlueSky is a prototype file server backed by cloud storage
I Prototype supports multiple client protocols (NFS, CIFS) and storagebackends (Amazon S3, Windows Azure)
I Allows clients to transparently move to cloud-backed storage
I Performance comparable to local storage when most access hits incache
I Design is informed by cost models of current cloud providers
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