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Improving I/O Resource Sharing of Linux Cgroup for NVMe SSDs on Multi-core Systems
USENIX HotStorage 2016
Sungyong Ahn*, Kwanghyun La*, Jihong Kim**
*Memory Business, Samsung Electronics Co., Ltd.
**Seoul National University
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Introduction
Motivation
Contributions
Weight-based dynamic throttling (WDT) scheme
Experimental Results
Conclusion
Outline
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Introduction
Motivation
Contributions
Weight-based dynamic throttling (WDT) scheme
Experimental Results
Conclusion
Outline
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Multiple isolated instances (containers) running on a single host.
OS-level Virtualization
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Multiple isolated instances (containers) running on a single host.
• Hardware resources should be isolated and allocated to containers
OS-level Virtualization
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Multiple isolated instances (containers) running on a single host.
• Hardware resources should be isolated and allocated to containers
• Different resource requirements should be satisfied
OS-level Virtualization
I have CPU-bound jobs
I have I/O-bound jobs
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Kernel-level resource manager of Linux
Linux Control Groups (Cgroups)
I have I/O-bound jobs
I have CPU-bound jobs
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I/O bandwidth is shared according to I/O weights
Proportional I/O scheme in Linux Cgroups
10
5
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1
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No
rma
lize
d I
/O b
an
dw
idth C(10) C(5)
C(2.5) C(1)
Ideal proportional I/O sharing
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Introduction
Motivation
Contributions
Weight-based dynamic throttling (WDT) scheme
Experimental Results
Conclusion
Outline
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Questions
When Linux Cgroups work with NVMe SSD,
1. Can they share the I/O resource in proportion to I/O weights? 2. Is it scalable?
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Questions
When Linux Cgroups work with NVMe SSD,
1. Can they share the I/O resource in proportion to I/O weights? 2. Is it scalable?
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1.5 1.10.5 1.0
0
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BASELINEN
orm
aliz
ed
I/O
ban
dw
idth C(10) C(5)
C(2.5) C(1)
Existing Cgroups cannot support the proportional I/O to NVMe SSDs
Proportional I/O with NVMe SSDs
10
5
2.5 1.0
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NVMe SSDs have different I/O stack from SATA storage
Because...
SATA I/O stack
NVMe I/O stack
Existing proportional I/O scheme is implemented in single queue block layer
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First Attempt: Using the Existing Static Throttling
Upper limit of I/O bandwidth
• Limit the maximum number of bytes or I/O requests for particular time interval (throttling window)
1 2 3
Throttling Window
4 5 6 7 8 9 10
throttled
Container A upper limit=10
Time
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0
2
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Static throttling is not enough to support the proportional I/O
First Attempt: Using the Existing Static Throttling
9.9
2.21.2 1.0
0
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Static Throttling
No
rmal
ize
d I/
O b
and
wid
th C(10) C(5)
C(2.5) C(1)
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Because...
I/O workloads fluctuate with time
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I/O workloads fluctuate with time
Because...
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Introduction
Motivation
Contributions
Weight-based Dynamic throttling (WDT) scheme
Experimental Results
Conclusion
Outline
![Page 19: Improving I/O Resource Sharing of Linux Cgroup for NVMe ......Improving I/O Resource Sharing of Linux Cgroup for NVMe SSDs on Multi-core Systems USENIX HotStorage 2016 Sungyong Ahn*,](https://reader034.vdocuments.net/reader034/viewer/2022050610/5fb1422e9c38cb084467ef29/html5/thumbnails/19.jpg)
Contributions
We achieved the proportional I/O for NVMe SSDs.
We achieved the scalable performance of Linux Cgroups.
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Introduction
Motivation
Contributions
Weight-based Dynamic throttling (WDT) scheme
Experimental Results
Conclusion
Outline
![Page 21: Improving I/O Resource Sharing of Linux Cgroup for NVMe ......Improving I/O Resource Sharing of Linux Cgroup for NVMe SSDs on Multi-core Systems USENIX HotStorage 2016 Sungyong Ahn*,](https://reader034.vdocuments.net/reader034/viewer/2022050610/5fb1422e9c38cb084467ef29/html5/thumbnails/21.jpg)
Overview of WDT Scheme
Container
Block Layer
Container Container
weight w1 weight w2 weight w3 Credit allocation
CPM Monitoring
Data flow
Future I/O Demand
Predictor
Budget Distributor
TotalCredit Updater
Residual Credits
Carryover
TotalCredit ℬ1𝑗+ ℛ1
𝑗
> 𝒰1𝑗
Monitoring
𝐶𝑃𝑀1
ℬ2𝑗+ ℛ2
𝑗
> 𝒰2𝑗
ℬ3𝑗+ ℛ3
𝑗
> 𝒰3𝑗
Monitoring
𝐶𝑃𝑀2
Monitoring
𝐶𝑃𝑀3
𝒞3 𝒞2 𝒞1
WDT
To update TotalCredit, future I/O demand is predicted
Distributing the credits to containers according to I/O weights
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All containers are allocated credits in proportion to their I/O weight.
Budget Distributor
Throttling Window
Container A I/O weight=10
Time
Container B I/O weight=5
Throttling Window
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
TotalCredit = 15
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All containers are allocated credits in proportion to their I/O weight.
Credits are replenished periodically.
Budget Distributor
Throttling Window
Container A I/O weight=10
Time
Container B I/O weight=5
Throttling Window
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
TotalCredit = 15 Replenishment of Credit Replenishment of Credit
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
TotalCredit = 15
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All containers are allocated credits in proportion to their I/O weight.
Credits are replenished periodically.
If a container has no available credit, it is throttled.
Budget Distributor
Throttling Window
Container A I/O weight=10
Time
Container B I/O weight=5
Throttling Window
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
TotalCredit = 15 Replenishment of Credit Replenishment of Credit
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
TotalCredit = 15
1 2 3 4 5 6 7 8 9 10
throttled
1 2 3 4 5 6 7 8 9 10
throttled
1 2 3 4 5
throttled
1 2 3 4 5
throttled
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All containers are allocated credits in proportion to their I/O weight.
Credits are replenished periodically.
If a container has no available credit, it is throttled.
Budget Distributor
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In order to remove storage idle time, TotalCredit is adjusted.
TotalCredit Updater
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Technical Challenge
How to predict TotalCredit required for the next interval?
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Technical Challenge
How to predict future I/O demand for the next interval?
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Monitoring I/O demand of each container for every interval
• Prediction of the future I/O demand from cumulative distribution function
– 80th percentile of a cumulative distribution of I/O demand (assuming normal distribution)
Future I/O Demand Predictor
Container
Block Layer
Container Container
weight w1 weight w2 weight w3 Credit allocation
CPM Monitoring
Data flow
Future I/O Demand
Predictor
Budget Distributor
TotalCredit Updater
Residual Credits
Carryover
TotalCredit ℬ1𝑗+ ℛ1
𝑗
> 𝒰1𝑗
Monitoring
𝐶𝑃𝑀1
ℬ2𝑗+ ℛ2
𝑗
> 𝒰2𝑗
ℬ3𝑗+ ℛ3
𝑗
> 𝒰3𝑗
Monitoring
𝐶𝑃𝑀2
Monitoring
𝐶𝑃𝑀3
𝒞3 𝒞2 𝒞1
WDT
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Questions
When Linux Cgroups work with NVMe SSD,
1. Can they share the I/O resource in proportion to I/O weights? 2. Is it scalable?
![Page 31: Improving I/O Resource Sharing of Linux Cgroup for NVMe ......Improving I/O Resource Sharing of Linux Cgroup for NVMe SSDs on Multi-core Systems USENIX HotStorage 2016 Sungyong Ahn*,](https://reader034.vdocuments.net/reader034/viewer/2022050610/5fb1422e9c38cb084467ef29/html5/thumbnails/31.jpg)
Scalability problem of the existing Cgroups throttling layer
Scalability of the Existing Cgroups on NUMA
0
100
200
300
400
500
600
1 node 2 nodes 3 nodes 4 nodes
KIO
PS
The number of NUMA nodes
C1 C2
C3 C4
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All containers share a single request_queue lock across NUMA nodes
Because...
0.6
21.8
31.738.2
0
10
20
30
40
50
1 node 2 nodes 3 nodes 4 nodesCP
U c
ach
e m
iss
rati
o (
%)
The number of NUMA nodes
Hardware
Linux
Container A Container B
Single-queue Block Layer
Container C Container D
Multi-queue Block Layer
Proportional I/O (CFQ)
Cgroup I/O throttling
LOCK
Lock contention Remote memory accesses to the lock
state Cacheline invalidations caused by
cache coherence protocol
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We adopt fine-grained per-container locks
The cache miss ratio decreases to 12.8% from 38.2%
Per-container Locks
Hardware
Linux
Container A Container B
Single-queue Block Layer
Container C Container D
Multi-queue Block Layer
Proportional I/O (CFQ)
Cgroup I/O throttling
LOCK
Hardware
Linux
Container A Container B
Single-queue Block Layer
Container C Container D
Multi-queue Block Layer
Proportional I/O (CFQ)
Cgroup I/O throttling
LOCK LOCK LOCK LOCK
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Introduction
Motivation
Contributions
Weight-based dynamic throttling (WDT) scheme
Experimental Results
Conclusion
Outline
![Page 35: Improving I/O Resource Sharing of Linux Cgroup for NVMe ......Improving I/O Resource Sharing of Linux Cgroup for NVMe SSDs on Multi-core Systems USENIX HotStorage 2016 Sungyong Ahn*,](https://reader034.vdocuments.net/reader034/viewer/2022050610/5fb1422e9c38cb084467ef29/html5/thumbnails/35.jpg)
Linux kernel 4.0.4 (modified)
Dell 4-nodes NUMA machine
Samsung NVMe SSDs
Block I/O traces replayer
• UMass trace repository
• SNIA IOTTA repository
Experiment Setup
Dell R920 Server (4-nodes NUMA machine)
Samsung XS1715 NVMe SSDs
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0
2
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6
8
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12
WDT scheme satisfies the proportional sharing requirements
Result 1: Proportional I/O Support
1.5
9.9 10.0
1.1
2.2
5.0
0.51.2
2.5
1.0 1.0 1.0
0
2
4
6
8
10
12
BASELINE Static Throttling WDT
No
rma
lize
d I
/O b
an
dw
idth
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WDT- : Using single spin lock
WDT : Using per-container locks
Result 2: Performance Scalability
1333
1762
670
881
334 440
133 176
0
200
400
600
800
1000
1200
1400
1600
1800
2000
WDT- WDT
I/O
ba
nd
wid
th (M
B/s
) C1 C2
C3 C4
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Introduction
Motivation
Contributions
Weight-based dynamic throttling (WDT) scheme
Experimental Results
Conclusion
Outline
![Page 39: Improving I/O Resource Sharing of Linux Cgroup for NVMe ......Improving I/O Resource Sharing of Linux Cgroup for NVMe SSDs on Multi-core Systems USENIX HotStorage 2016 Sungyong Ahn*,](https://reader034.vdocuments.net/reader034/viewer/2022050610/5fb1422e9c38cb084467ef29/html5/thumbnails/39.jpg)
Proposed the weight-based dynamic throttling scheme to support proportional I/O sharing for NVMe SSDs.
Proposed the per-container locks for scalable performance.
Conclusion
![Page 40: Improving I/O Resource Sharing of Linux Cgroup for NVMe ......Improving I/O Resource Sharing of Linux Cgroup for NVMe SSDs on Multi-core Systems USENIX HotStorage 2016 Sungyong Ahn*,](https://reader034.vdocuments.net/reader034/viewer/2022050610/5fb1422e9c38cb084467ef29/html5/thumbnails/40.jpg)