Dynamic Resource Allocation for Database Servers Running ...Disk Bandwidth Want to use all resources...
Transcript of Dynamic Resource Allocation for Database Servers Running ...Disk Bandwidth Want to use all resources...
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Dynamic Resource Allocation for Database Servers Running on Virtual Storage
Gokul Soundararajan, Daniel Lupei, Saeed Ghanbari, Adrian Daniel Popescu, Jin Chen, Cristiana Amza
University of Toronto
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Multi-tier Resource Allocation
Consolidated Environment
Web Server
Application Server
Database Server
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Composed of several tiers
Application-BApplication-A
Share resources in each tier
Can lead to interference
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Storage
Database
Our Focus: Storage Hierarchy
Application-A Application-B
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Buffer Pool
StorageCache
DiskBandwidth
Want to use all resources efficiently
Disk is a bottleneck for Database Apps
Network
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State of the Art
‣ Previous work studied resources in isolation
- Memory Partitioning: MRC [ASPLOS’04]
- Disk Bandwidth: Facade [FAST’03], Argon [FAST’07], etc.
- ... and many more
‣ Want to use the storage hierarchy efficiently
‣However, performance depends on all layers
- Interdependency between resources
- E.g., Increasing buffer pool reduces number of storage accesses
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Motivating Scenario
Small Large
5
Buffer Pool
StorageCache
DiskBandwidth
Cache Friendly1 Outstanding I/O
Cache Un-Friendly10 Outstanding I/O
Using Oracle ORION I/O tool
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Motivating Scenario
0
2.5
5.0
7.5
Shared Cache Disk Cache & Disk
Small Large6
Nor
mal
ized
Lat
ency
Benefits cache-friendly workload
Avoids disk interference
Has best performance
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Contributions
‣ Build performance models dynamically
- Account for interdependencies between resources
- Lightweight but still accurate
‣Multi-level Resource Allocator
- Uses performance models to guide resource allocation
- Corrects model errors through runtime sampling
- Uses global utility (SLOs) to partition resources
- Minimize sum of application latencies
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Approach
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‣ Build performance models
- One per application
- Derive function to predict application latency given configuration
‣ Find resource partitioning setting
- Minimize sum of application latencies
- Find best setting using hill climbing
Lavg = f(ρc, ρs, ρd)
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Outline
‣ Online Performance Models
- What are they?
- Why are they hard to build?
‣Multi-level Resource Allocator
‣ Prototype Implementation
‣ Experimental Results
‣Conclusions
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One-Level Cache Model
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Rd(A)
Cache size
Avg.
Lat
ency
Allocate in 32MB chunks
32 64 512
m=CacheSize/ChunkSize=1GB/32MB=32 choices
1G
32 choices
Choose 512MB
... ...
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MRC Cache Model
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Rd(A)
Cache size
Mis
s-Ra
tio
32 64 512 1G... ...
Computes miss-ratio
given an I/O trace
Multiply by I/O latency gets Avg. Latency
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Two-Level Cache Model
‣ Performance affected by
- DB Buffer Pool Size (m choices)
- Storage Cache (n choices)
‣ Performance model
- Needs to consider all parameters (m*n choices)
- 1GB caches allocated in 32MB chunks
- m = 1GB/32MB = 32 settings
- m*n = 1024 distinct settings
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Changes the I/O trace at
storage
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Two-Level Cache Model
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0.5
1
1.5
2
2.5
3
3.5
4
256
512
768
1024
256
512
768
1024
0
2
4
Latency Surface
Buffer Pool Size (MB)
Storage Cache Size (MB)
2 caches create a 3D surface
Buffer Pool Size
Storage
Cache Size
Avg
. La
tenc
y
32x32=1024 data points!
32 data points
HighLatency
LowLatency
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Overall Performance Model
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Application
ρc
ρs
ρd
Buffer Pool
StorageCache
DiskBandwidth
Needs 32x32x10=10240
samples
15 mins/sample takes 3 months!
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Outline
‣ Online Performance Models
‣Multi-level Resource Allocator
- Building performance models
- Allocating resources using models
‣ Prototype Implementation
‣ Experimental Results
‣Conclusions
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Key Observations
‣ Known cache replacement policies
- Most cache replacement algorithms are LRU
- Only as effective as the largest cache (cache inclusiveness)
‣Disk is a closed loop system
- Rate of responses is same as rate of requests
- Performance proportional to the disk bandwidth fraction
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Cache Inclusiveness
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LRU
LRU
8 8 1 6 8 4 5 6 3 48
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I/Os: 0I/Os: 1
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Cache Inclusiveness
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LRU
LRU
8 8 1 6 8 4 5 6 3 48 8 1 6
I/Os: 6
8 4 5 6 3 4
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34 56 8
Storage cache includes data in the buffer pool
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Cache Inclusiveness
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LRU
LRU
8 8 1 6 8 4 5 6 3 48 8 1 6
I/Os: 6
8 4 5 6 3 4
3 5
34 56 8Buffer pool
includes data in the storage cache
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Approximate Single Cache Model (LRU)
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8 8 1 6 8 4 5 6 3 48 8 1 6 8 4 5 6 3 4
I/Os: 6
Same Number of I/Os
LRU
LRU34
34 56 8
Mc(max[ρc, ρs])
34 56 8 LRU
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Cache Model (DEMOTE)
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‣ Maintain cache exclusiveness
- E.g., using DEMOTEs [USENIX’02]
- Every block brought into buffer pool is not cached below
- Only evictions from buffer pool cached in storage cache
‣ Approximate performance using single cache
- Mc(ρc + ρs)
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Find Best Partitioning Setting
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Find Best Resource Allocation Setting
Buffer Pool Size
Storage
Cache Size
Late
ncy
App-1
App-2
Buffer Pool Size
Storage
Cache Size
Late
ncy Minimize sum of
application latencies
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‣ Observation: Closed loop system
- Rate of responses same as rate of requests
- Use interactive response time law
‣ Performance proportional to disk bandwidth fraction
- Measure base disk latency:
- Predict latency for smaller bandwidth fractions
Disk Model
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Ld(ρd) =Ld(1)
ρd
Ld(1)
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Putting it All Together
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Application
Mc(ρc)Ms(ρc, ρs)N
Storage Cache
Buffer Pool
ApproximateSingle-Level
CacheCan now be
solved using MRC
=Mc(max[ρc, ρs])N
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Putting it all Together
Application
Hc(ρc)Lc
Mc(ρc)Hs(ρc, ρs)Lnet
Mc(ρc)Ms(ρc, ρs)Ld(ρd)
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Inaccuracies in the Model
‣ Cache Model
- Approximations to LRU, i.e., CLOCK
- Large fraction of writes in the workload
‣Disk Model
- Using Quanta-based scheduler [Wachs et. al, FAST’07]
- Interference due to disk seeks at small quanta
‣ Inaccuracies localized in known regions
- E.g., Small disk quanta
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Iterative Refinement
‣ Build model
- Use trace collected at the database buffer pool
‣ Refine the model
- Use cross-validation to measure quality
- Selectively sample where error is high
- Interpolate computed and measured samples
- Using regression (SVM)
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Virtual Storage Prototype
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StorageMySQL
Linux
NBD
CLIENT
Block Layer
SCSI
Buffer Pool
Disk
SERVER
NBD
Linux
Block Layer
SCSI
Disk
Ne
two
rk
DiskDisk
Cache Quanta
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Experimental Setup
‣ Benchmarks
- UNIFORM (microbenchmark), TPC-W and TPC-C
‣ LAMP Architecture
- Linux, Apache 1.3, MySQL/InnoDB 5.0, and PHP 5.0
‣ Cache Configuration
- MySQL buffer pool = 1GB
- Storage cache = 1GB
- Using InnoDB cache replacement in MySQL, CLOCK in storage cache
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Our Algorithms
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‣ GLOBAL
- Gather trace at the buffer pool
- Measure base disk latency
- Compute performance using performance model
‣ GLOBAL+
- Run GLOBAL
- Evaluate model accuracy
- Refine model using runtime samples
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Algorithms for Comparison
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‣ MRC
- Partition cache (independently) using miss-ratio curves
‣DISK
- Partition caches equally, determine best disk quanta
‣MRC+DISK
- Run MRC then DISK
‣ IDEAL*
- Build model with SVM using 16*16*5=1280 sampled configurations
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Roadmap of Results
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‣ Multi-level cache allocator
- Using LRU and DEMOTE cache replacement policies
‣ Multi-level cache and disk
‣ Accuracy of computed models
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Miss-Ratio Curves
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0
25
50
75
100
0 128 256 384 512 640 768 896 1024
Miss
Rat
io (%
)
Buffer Pool Size (MB)
TPC-WTPC-C
UNIFORM
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Multi-Level Caching (LRU)
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Optimal
HeatMapLighter: BetterDarker: WorseOptimal
Storage Cache Size(A)
Buf
fer
Po
ol S
ize
(A)
1G0
1G2 TPC-W Instances
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Multi-Level Caching (DEMOTE)
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Optimal
Storage Cache Size(A)
Buf
fer
Po
ol S
ize
(A)
1G0
1G2 TPC-W Instances
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Roadmap of Results
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‣ Multi-level cache allocator
‣ Multi-level cache and disk
- Using two identical applications
- Using different applications
‣ Accuracy of computed models
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UNIFORM/UNIFORM
0
1.682
3.364
5.046
6.728
8.410
GLOBAL GLOBAL+ MRC DISK MRC+DISK IDEAL*
UNIFORM UNIFORM
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Ave
rag
e La
tenc
y (m
s)
Allocate caches to 50/50
Matches GLOBAL
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TPC-W/UNIFORM
0
1.5
3.0
4.5
6.0
7.5
GLOBAL GLOBAL+ MRC DISK MRC+DISK IDEAL*
TPC-W UNIFORM
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Ave
rag
e La
tenc
y (m
s)
Allocate caches to 50/50
Not enough buffer pool to
UNIFORM Compensate for MRC settings
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TPC-W/TPC-C
0
0.16
0.32
0.48
0.64
0.80
GLOBAL GLOBAL+ MRC DISK MRC+DISK IDEAL*
TPC-W TPC-C
39
Ave
rag
e La
tenc
y (m
s)
Corrects model at runtime
Corrects imbalance in
MRC
TPC-C allocated more in both
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Roadmap of Results
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‣ Multi-level cache allocator
‣ Multi-level cache and disk
‣ Accuracy of computed models
- Cache model
- Disk model
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Cache Model Accuracy (TPC-W)
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0 128 256 384 512 640 768 896
1024
0 128 256 384 512 640 768 896 1024
Stor
age
Cach
e Si
ze (M
B)
Buffer Pool Size (MB)
0
10
20
30
40
50
Erro
r (%
)
Localized in the middle
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Disk Model Accuracy (TPC-W)
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0
10
20
30
40
50
0 0.2 0.4 0.6 0.8 1
Late
ncy
(ms)
Disk Quota
MeasuredComputed
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Conclusions
‣ Problem
- Need to consider resources on multiple tiers
- Independent cache/disk allocators are not sufficient
‣ Dynamic allocation of cache hierarchy and disk
- Build performance models dynamically
- Iteratively refine (if necessary)
- Use models for global resource partitioning
‣ Performance up to 2.9 better than single resource allocators
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Thank you.
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