Best Practices with Ceph as Distributed, Intelligent, Unified Cloud Storage - Ceph Day Frankfurt

0 © Fujitsu 2014
Best practice with Ceph as Distributed
Intelligent Unified Cloud Storage
Dieter Kasper
CTO Data Center Infrastructure and Services
Technology Office, International Business
2014-02-27 v4

1 © Fujitsu 2014
Agenda
 Introduction
 Hardware / Software layout
 Performance test cases
 Hints and Challenges for Ceph
 Summary

2 © Fujitsu 2014
Challenges of a Storage Subsystem
 Transparency
 User has the impression of a single global File System / Storage Space
 Scalable Performance,Elasticity
 No degradation of performance as the number of users or volume of data increases
 Intelligent rebalancing on capacity enhancements
 Offer same high performance for all volumes
 Availability, Reliability and Consistency
 User can access the same file system / Block Storage from different locations at the sametime
 User can access the file system at any time
 Highest MTTDL (mean time to data loss)
 Fault tolerance
 System can identify and recover from failure
 Lowest Degradation during rebuild time
 Shortest Rebuild times
 Manageability & ease of use

3 © Fujitsu 2014
 Central allocation tables
 File systems
– Access requires lookup
– Hard to scale table size
+ Stable mapping
+ Expansion trivial
 Hash functions
 Web caching, Storage Virtualization
+ Calculate location
+ No tables
– Unstable mapping
– Expansion reshuffles
Conventional data placement

4 © Fujitsu 2014
Controller 1 Controller 2
 Dual redundancy
 1 node/controllercan fail
 2 drives can fail (RAID 6)
 HA only inside a pair
access network
interconnect
Conventional vs. distributed model
node1
node2
node3
node4
…
 N-way resilience, e.g.
 Multiple nodes can fail
 Multiple drives can fail
 Nodes coordinate replication,
and recovery

5 © Fujitsu 2014
 While traditional storage systems distribute Volumes across sub-sets of Spindles
Scale-Out systems use algorithms to distribute Volumes across all/many Spindles
and provide maximum utilization of all system resources
 Offer same high performance for all volumes and shortest rebuild times
Conventional vs. distributed model

6 © Fujitsu 2014
A model for dynamic “clouds” in nature
Swarm of birds or fishes
Source: w ikipedia

7 © Fujitsu 2014
Distributed intelligence
 Swarm intelligence [Wikipedia]
 (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial.
 Swarm behavior [Wikipedia]
 Swarm behavior, or swarming, is a collective behavior exhibited by animals of similar size
(…) moving en masse or migrating in some direction.
 From a more abstract point of view, swarm behavior is the collective motion of a large
number of self-propelled entities.
 From the perspective of the mathematical modeler, it is an (…)
behavior arising from simple rules that are followed by individuals and does not
involve any central coordination.

8 © Fujitsu 2014
Ceph Key Design Goals
 The system is inherently dynamic:
 Decouples data and metadata
 Eliminates object list for naming and lookup by a hash-like distribution function –
CRUSH (Controlled Replication Under Scalable Hashing)
 Delegates responsibility of data migration, replication, failure detection and recovery to the OSD
(Object Storage Daemon) cluster
 Node failures are the norm, rather than an exception
 Changes in the storage cluster size (up to 10k nodes) cause automatic and fast failure recovery and
rebalancing of data with no interruption
 The characters of workloads are constantly shifting over time
 The Hierarchy is dynamically redistributed over 10s of MDSs (Meta Data Services) by Dynamic
Subtree Partitioning with near-linear scalability
 The system is inevitably built incrementally
 FS can be seamlessly expanded by simply adding storage nodes (OSDs)
 Proactively migrates data to new devices -> balanced distribution of data
 Utilizes all available disk bandwidth and avoids data hot spots

9 © Fujitsu 2014
Cluster
monitors
Clients
OSDsMDSs
File, Block,
ObjectIO
Metadata ops
Metadata IO
RADOS
Architecture: Ceph + RADOS (1)
 Clients
 Standard Interface to use the data
(POSIX, Block Device, S3)
 Transparent for Applications
 Metadata Server Cluster
(MDSs)
 Namespace Management
 Metadata operations (open, stat,
rename, …)
 Ensure Security
 ObjectStorage Cluster (OSDs)
 Stores all data and metadata
 Organizes data into flexible-sized
containers, called objects

10 © Fujitsu 2014
Architecture: Ceph + RADOS (2)
Cluster Monitors
<10
cluster membership
authentication
cluster state
cluster map
Topology +
Authentication
Meta Data Server (MDS)
10s
for POSIX only
Namespace mgmt.
Metadata ops (open, stat, rename, …)
POSIX
meta data only
Object Storage Daemons (OSD)
10,000s
stores all data / metadata
organises all data in flexibly sized containers
Clients
bulk data traffic
block file object

11 © Fujitsu 2014
CRUSH Algorithm Data Placement
 No Hotspots / Less Central Lookups:
 x → [osd12, osd34] is pseudo-random, uniform (weighted) distribution
 Stable: adding devices remaps few x’s
 Storage Topology Aware
 Placement based on physical infrastructure
 e.g., devices, servers, cabinets, rows, DCs, etc.
 Easy and Flexible Rules: describe placement in terms of tree
 ”three replicas, different cabinets, same row”
 Hierarchical Cluster Map
 Storage devices are assigned weights to control the amount of data they are responsible for
storing
 Distributes data uniformly among weighted devices
 Buckets can be composed arbitrarily to construct storage hierarchy
 Data is placed in the hierarchy by recursively selecting nested bucket items via pseudo-random
hash like functions

12 © Fujitsu 2014
Data placement with CRUSH
 Files/bdevs striped over objects
 4 MB objects by default
 Objects mapped to
placementgroups (PGs)
 pgid = hash(object) & mask
 PGs mapped to sets of OSDs
 crush(cluster, rule, pgid) = [osd2, osd3]
 Pseudo-random, statistically uniform
distribution
 ~100 PGs per node
 Fast: O(log n) calculation, no lookups
 Reliable: replicas span failure domains
 Stable: adding/removing OSDs moves few PGs
 A deterministic pseudo-random hash like function that distributes data uniformly among OSDs
 Relies on compact cluster description for new storage target w/o consulting a central allocator
…
… … …
…
OSDs
(grouped by
f ailure domain)
Objects
PGs
…File / Block

13 © Fujitsu 2014
Ceph is the most comprehensive implementation of Unified Storage
Unified Storage for Cloud based on Ceph –
Architecture and Principles
The Ceph difference
Ceph’s CRUSH algorithm
liberates storage clusters
from the scalability and
performance limitations
imposed by centralized data table
mapping. It replicates and re-
balance data within the cluster
dynamically - eliminating this tedious
task for administrators, while
delivering high-performance and
infinite scalability.
http://ceph.com/ceph-storage
http://www.inktank.com
Librados
A library allow ing
apps to directly
access RADOS,
w ith support for
C, C++, Java,
Python, Ruby,
and PHP
Ceph Object
Gateway
(RGW)
A bucket-based
REST gatew ay,
compatible w ith
S3 and Sw ift
Ceph Block
Device
(RBD)
A reliable and fully-
distributed block
device, w ith a Linux
kernel client and a
QEMU/KVM driver
Ceph File
System
(CephFS)
A POSIX-compliant
distributed file
system, w ith a Linux
kernel client and
support for FUSE
App App
Object
Host / VM
Virtual Disk
Client
Files & Dirs
Ceph Storage Cluster (RADOS)
A reliable, autonomous, distributed object store comprised
of self-healing, self-managing, intelligent storage nodes

14 © Fujitsu 2014
Redundant Cluster Interconnect (IP based)
Redundant Client Interconnect (IP based)
Client Client Client Client
Ceph principles
Distributed Redundant Storage
 Intelligent data Distribution across
all nodes and spindles = wide
striping (64KB – 16MB)
 Redundancy with replica=2, 3 … 8
 Thin provisioning
 Fast distributed rebuild
 Availability, Fault tolerance
 Disk, Node, Interconnect
 Automatic rebuild
 Distributed HotSpare Space
 Transparent Block, File access
 Reliability and Consistency
 Scalable Performance
 Pure PCIe-SSD for extreme
Transaction processing
Stor
Node
SAS
SAS
SAS
PCI
SSD
Stor
Node
SAS
SAS
SAS
PCI
SSD
Stor
Node
SAS
SAS
SAS
PCI
SSD
Stor
Node
SAS
SAS
SAS
PCI
SSD
Stor
Node
SAS
SAS
SAS
PCI
SSD
Stor
Node
SAS
SAS
SAS
PCI
SSD
Stor
Node
SAS
SAS
SAS
PCI
SSD
Stor
Node
SAS
SAS
SAS
PCI
SSD
SAS SAS SAS SAS SAS SAS SAS SAS
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
librbd
VMs
Block
App
Object
App
File
App

15 © Fujitsu 2014
Redundant Cluster Interconnect (IP based)
Redundant Client Interconnect (IP based)
Client Client Client Client
Ceph processes
Stor
Node
OSD
OSD
OSD
OSD
Stor
Node
OSD
OSD
OSD
OSD
Stor
Node
OSD
OSD
OSD
OSD
Stor
Node
OSD
OSD
OSD
OSD
Stor
Node
OSD
OSD
OSD
OSD
Stor
Node
OSD
OSD
OSD
OSD
Stor
Node
OSD
OSD
OSD
OSD
Stor
Node
OSD
OSD
OSD
OSD
OSD OSD OSD OSD OSD OSD OSD OSD
OSD OSD OSD OSD OSD OSD OSD OSD
librbd
VMs
Block
App
Object
App
File
App
MON MON MON
Client
Client
Client
Client
Client
Client
Client
Client
Client
Client
Client
Client
Client
Client
Client
Client
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
J
MDS MDS
Distributed Redundant Storage
 Intelligent data Distribution across
all nodes and spindles = wide
striping (64KB – 16MB)
 Redundancy with replica=2, 3 … 8
 Thin provisioning
 Fast distributed rebuild
 Availability, Fault tolerance
 Disk, Node, Interconnect
 Automatic rebuild
 Distributed HotSpare Space
 Transparent Block, File access
 Reliability and Consistency
 Scalable Performance
 Pure PCIe-SSD for extreme
Transaction processing

16 © Fujitsu 2014
Agenda
 Introduction
 Summary

17 © Fujitsu 2014
rx37-3
Hardware test configuration
rx37-4 rx37-5
PCI
SSD
rx37-6 rx37-7 rx37-8
rx37-1
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
SAS
16x
SAS
16x
SAS
16x
SAS
16x
SAS
16x
SAS
16x
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
 rx37-[3-8]: Fujitsu 2U Server RX300
 2x Intel(R) Xeon(R) E5-2630 @ 2.30GHz
 128GB RAM
 2x 1GbE onboard
 2x 10GbE Intel 82599EB
 1x 40GbIB Intel TrueScale IBA7322 QDR InfiniBand
HCA
 2x 56GbIB Mellanox MT27500 Family (configurable
as 40GbE, too)
 3x Intel PCIe-SSD 910 Series 800GB
 16x SAS 6G 300GB HDD through
 LSI MegaRAID SAS 2108 [Liberator]
 rx37-[12]: same as above, but
 1x Intel(R) Xeon(R) E5-2630 @ 2.30GHz
 64GB RAM
 No SSDs, 2x SAS drives
56 / 40 GbIB, 40 / 10 / 1 GbE Cluster Interconnect
56 / 40 GbIB , 40 / 10 / 1GbE Client Interconnect
RBD
fio
CephFS
rx37-2
RBD
fio
CephFS
MON MON MON
MDS MDS

18 © Fujitsu 2014
rx37-3 rx37-4 rx37-5
PCI
SSD
rx37-6 rx37-7 rx37-8
rx37-1
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
SAS
16x
SAS
16x
SAS
16x
SAS
16x
SAS
16x
SAS
16x
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
PCI
SSD
(1)Frontend interface: ceph.ko, rbd.ko, ceph-
fuse, rbd-wrapper in user land
(2)OSD object size of data: 64k, 4m
(3)Block Device options for /dev/rbdX, /dev/sdY:
scheduler, rq_affinity, rotational,
read_ahead_kb
(4)Interconnect: 1 / 10 / 40 GbE, 40 / 56 GbIB
CM/DG
(5)Network parameter
(6)Journal: RAM-Disk, SSD
(7)OSD File System: xfs, btrfs
(8)OSD disk type: SAS, SSD
56 / 40 GbIB, 40 / 10 / 1 GbE Cluster Interconnect
56 / 40 GbIB , 40 / 10 / 1GbE Client Interconnect
RBD
fio
CephFS
rx37-2
RBD
fio
CephFS
MON MON MON
MDS MDS
Which parameter to change, tune
1
3
2
4 5
4 5
6
6
7
7
8
3

19 © Fujitsu 2014
Software test configuration
 CentOS 6.4
 With vanilla Kernel 3.8.13
 fio-2.0.13
 ceph version 0.61.7 cuttlefish
1GbE: eth0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500
40Gb: ib0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 65520
# fdisk -l /dev/sdq
Device Boot Start End Blocks Id System
/dev/sdq1 1 22754 182767616 83 Linux
/dev/sdq2 22754 24322 12591320 f Ext'd
/dev/sdq5 22755 23277 4194304 83 Linux
/dev/sdq6 23277 23799 4194304 83 Linux
/dev/sdq7 23799 24322 4194304 83 Linux
#--- 3x Journals on each SSD
# lsscsi
[0:2:0:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sda
[0:2:1:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdb
[0:2:2:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdc
[0:2:3:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdd
[0:2:4:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sde
[0:2:5:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdf
[0:2:6:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdg
[0:2:7:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdh
[0:2:8:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdi
[0:2:9:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdj
[0:2:10:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdk
[0:2:11:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdl
[0:2:12:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdm
[0:2:13:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdn
[0:2:14:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdo
[0:2:15:0] disk LSI RAID 5/6 SAS 6G 2.12 /dev/sdp
[1:0:0:0] disk INTEL(R) SSD 910 200GB a411 /dev/sdq
[1:0:1:0] disk INTEL(R) SSD 910 200GB a411 /dev/sdr
[1:0:2:0] disk INTEL(R) SSD 910 200GB a411 /dev/sds
[1:0:3:0] disk INTEL(R) SSD 910 200GB a411 /dev/sdt
[2:0:0:0] disk INTEL(R) SSD 910 200GB a411 /dev/sdu
[2:0:1:0] disk INTEL(R) SSD 910 200GB a411 /dev/sdv
[2:0:2:0] disk INTEL(R) SSD 910 200GB a411 /dev/sdw
[2:0:3:0] disk INTEL(R) SSD 910 200GB a411 /dev/sdx
[3:0:0:0] disk INTEL(R) SSD 910 200GB a40D /dev/sdy
[3:0:1:0] disk INTEL(R) SSD 910 200GB a40D /dev/sdz
[3:0:2:0] disk INTEL(R) SSD 910 200GB a40D /dev/sdaa
[3:0:3:0] disk INTEL(R) SSD 910 200GB a40D /dev/sdab

20 © Fujitsu 2014
Agenda
 Introduction
 Summary

21 © Fujitsu 2014
Performance test tools
 qperf $IP_ADDR -t 10 -oo msg_size:1:8M:*2 –v
tcp_lat | tcp_bw | rc_rdma_write_lat | rc_rdma_write_bw |
rc_rdma_write_poll_lat
 fio --filename=$RBD|--directory=$MDIR --direct=1 --rw=$io
--bs=$bs --size=10G --numjobs=$threads --runtime=60 --
group_reporting --name=file1 --
output=fio_${io}_${bs}_${threads}
 RBD=/dev/rbdX, MDIR=/cephfs/fio-test-dir
 io=write,randwrite,read,randread
 bs=4k,8k,4m,8m
 threads=1,64,128

22 © Fujitsu 2014
qperf - latency
2,00
4,00
8,00
16,00
32,00
64,00
128,00
256,00
512,00
1024,00
2048,00
4096,00
8192,00
16384,00
32768,00
65536,00
131072,00
tcp_lat (µs : IO_bytes)
1GbE
10GbE
40GbE-1500
40GbE-9126
40IPoIB_CM
56IPoIB_CM
56IB_wr_lat
56IB_wr_poll_lat

23 © Fujitsu 2014
qperf - bandwidth
0
1000
2000
3000
4000
5000
6000
7000
tcp_bw (MB/s : IO_bytes)
1GbE
10GbE
40GbE-1500
40GbE-9126
40IPoIB_CM
56IPoIB_CM
56IB_wr_lat

24 © Fujitsu 2014
qperf - explanation
 tcp_lat almost the same for blocks <= 128 bytes
 tcp_lat very similar between 40GbE,40Gb IPoIB and 56Gb IPoIB
 Significant difference between1 / 10GbE only for blocks >= 4k
 Better latency on IB can only be achieved with rdma_write / rdma_write_poll
 Bandwidth on 1 / 10GbE very stable on possible maximum
 40GbE implementation (MLX) with unexpected fluctuation
 Under IB only with RDMA the maximum transfer rate can be achieved
 Use Socketover RDMA
 Options without big code changes are: SDP, rsocket,SMC-R, accelio

25 © Fujitsu 2014
Frontend
Interfaces
OSD-FS
xfs
OSD object
size 4m
Journal
SSD
56 Gb
IPoIB_CM
OSD-DISK
SSD
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
IOPS
ceph-ko
ceph-fuse
rbd-ko
rbd-wrap

26 © Fujitsu 2014
0
500
1000
1500
2000
2500
3000
3500
4000
MB/s
ceph-ko
ceph-fuse
rbd-ko
rbd-wrap
Frontend
Interfaces
OSD-FS
xfs
OSD object
size 4m
Journal
SSD
56 Gb
IPoIB_CM
OSD-DISK
SSD

27 © Fujitsu 2014
 Ceph-fuse seems not to support multiple I/Os in parallel with O_DIRECT
which drops the performance significantly
 RBD-wrap (= rbd in user land) shows some advantages on IOPS, but not
sufficient enough to replace rbd.ko
 Ceph.ko is excellent on sequential IOPS reads, presumable because of
the read (ahead) of complete 4m blocks
 Stay with the official interfaces ceph.ko / rbd.ko to give fio the needed
access to File and Block
 rbd.ko has some room for performance improvement for IOPS
Frontend
Interfaces
OSD-FS
xfs
OSD object
size 4m
Journal
SSD
56 Gb
IPoIB_CM
OSD-DISK
SSD

28 © Fujitsu 2014
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
IOPS
xfs ceph-ko
btrfs ceph-ko
xfs rbd-ko
btrfs rbd-ko
ceph-ko
rbd-ko
OSD-FS
xfs / btrfs
OSD object
size 4m
Journal
SSD
56 Gb
IPoIB_CM
OSD-DISK
SSD

29 © Fujitsu 2014
0
500
1000
1500
2000
2500
3000
3500
4000
4500
MB/s
xfs ceph-ko
btrfs ceph-ko
xfs rbd-ko
btrfs rbd-ko
ceph-ko
rbd-ko
OSD-FS
xfs / btrfs
OSD object
size 4m
Journal
SSD
56 Gb
IPoIB_CM
OSD-DISK
SSD

30 © Fujitsu 2014
 6 months ago with kernel 3.0.x btrfs reveal some weaknesses in writes
 With kernel 3.6 some essential enhancements were made to the btrfs
code, so almost no differences could be identified in our kernel 3.8.13
 Use btrfs in the next test cases, because btrfs has the more promising
storage features: compression, data deduplication
ceph-ko
rbd-ko
OSD-FS
xfs / btrfs
OSD object
size 4m
Journal
SSD
56 Gb
IPoIB_CM
OSD-DISK
SSD

31 © Fujitsu 2014
0
10000
20000
30000
40000
50000
60000
70000
80000
IOPS
64k
4m
0
500
1000
1500
2000
2500
3000
3500
MB/s
64k
4m
rbd-ko
OSD-FS
btrfs
OSD object
64k / 4m
Journal
RAM
56 Gb
IPoIB_CM
OSD-DISK
SSD

32 © Fujitsu 2014
 Small chunks of 64k can especially increase 4k/8k sequential IOPS for
reads as well as for writes
 For random IO 4m chunks are as good as 64k chunks
 But, the usage of 64k chunks will result in very low bandwidth for 4m/8m
blocks
 Create each volume with the appropriate OSD object size: ~64k if small
sequential IOPS are used, otherwise stay with ~4m
rbd-ko
OSD-FS
btrfs
OSD object
64k / 4m
Journal
RAM
56 Gb
IPoIB_CM
OSD-DISK
SSD

33 © Fujitsu 2014
0
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80000
90000
IOPS
j-RAM ceph-ko
j-SSD ceph-ko
j-RAM rbd-ko
j-SSD rbd-ko
ceph-ko
rbd-ko
OSD-FS
btrfs
OSD object
size 4m
Journal
RAM / SSD
56 Gb
IPoIB_CM
OSD-DISK
SSD

34 © Fujitsu 2014
0
500
1000
1500
2000
2500
3000
3500
4000
4500
MB/s
j-RAM ceph-ko
j-SSD ceph-ko
j-RAM rbd-ko
j-SSD rbd-ko
ceph-ko
rbd-ko
OSD-FS
btrfs
OSD object
size 4m
Journal
RAM / SSD
56 Gb
IPoIB_CM
OSD-DISK
SSD

35 © Fujitsu 2014
 Only on heavy write bandwidth requests RAM can show its predominance
used as a journal.
 The SSD has to accomplish twice the load when the journal and the data
is written to it.
ceph-ko
rbd-ko
OSD-FS
btrfs
OSD object
size 4m
Journal
RAM / SSD
56 Gb
IPoIB_CM
OSD-DISK
SSD

36 © Fujitsu 2014
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80000
90000
IOPS
1GbE
10GbE
40GbE
40GbIB_CM
56GbIB_CM
ceph-ko
OSD-FS
btrfs
OSD object
size 4m
Journal
SSD
Network
OSD-DISK
SSD

37 © Fujitsu 2014
0
500
1000
1500
2000
2500
3000
3500
4000
4500
MB/s
1GbE
10GbE
40GbE
40GbIB_CM
56GbIB_CM
ceph-ko
OSD-FS
btrfs
OSD object
size 4m
Journal
SSD
Network
OSD-DISK
SSD

38 © Fujitsu 2014
 In case of write IOPS 1GbE is doing extremely well
 On sequential read IOPS there is nearly no difference between10GbE and 56Gb
IPoIB
 On the bandwidth side with reads the measured performance is close in sync with
the possible speedof the network. Only the TrueScale IB has some weaknesses,
because it was designed for HPC and not for Storage/Streaming.
 If you only look or IOPS 10GbE is a good choice
 If throughput is relevant for your use case you should go for 56GbIB
ceph-ko
OSD-FS
btrfs
OSD object
size 4m
Journal
SSD
Network
OSD-DISK
SSD

39 © Fujitsu 2014
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
IOPS
SAS ceph-ko
SSD ceph-ko
SAS rbd-ko
SSD rbd-ko
ceph-ko
rbd-ko
OSD-FS
btrfs
OSD object
size 4m
Journal
SSD
56 Gb
IPoIB_CM
OSD-DISK
SAS / SSD

40 © Fujitsu 2014
0
500
1000
1500
2000
2500
3000
3500
4000
4500
MB/s
SAS ceph-ko
SSD ceph-ko
SAS rbd-ko
SSD rbd-ko
ceph-ko
rbd-ko
OSD-FS
btrfs
OSD object
size 4m
Journal
SSD
56 Gb
IPoIB_CM
OSD-DISK
SAS / SSD

41 © Fujitsu 2014
rbd-ko
OSD-FS
btrfs
OSD object
64k / 4m
Journal
SSD
56 GbIPoIB
CM / DG
OSD-DISK
SAS / SSD
0
10000
20000
30000
40000
50000
60000
70000
IOPS
SAS 4m CM
SSD 4m CM
SAS 64k DG
SSD 64k DG

42 © Fujitsu 2014
rbd-ko
OSD-FS
btrfs
OSD object
64k / 4m
Journal
SSD
56 GbIPoIB
CM / DG
OSD-DISK
SAS / SSD
0
500
1000
1500
2000
2500
3000
3500
4000
MB/s
SAS 4m CM
SSD 4m CM
SAS 64k DG
SSD 64k DG

43 © Fujitsu 2014
 SAS drives are doing much better than expected. Only on small random
writes they are significantly slower than SSD
 In this comparison is has to be taken into account, that the SSD is getting
twice as much writes (journal + data)
 2.5” 10k 6G SAS drives seems to be an attractive alternative in
combination with SSD for the journal
ceph-ko
rbd-ko
OSD-FS
btrfs
OSD object
64k / 4m
Journal
SSD
56 GbIPoIB
CM / DG
OSD-DISK
SAS / SSD

45 © Fujitsu 2014
Stor
Node
SAS
SAS
SAS
SSD
Stor
Node
SAS
SAS
SAS
SSD
SAS SAS
SSD SSD
Stor
Node
SAS
SAS
SAS
SSD
Stor
Node
SAS
SAS
SAS
SSD
SAS SAS
SSD SSD
Ceph HA/DR Design
Stor
Node
SAS
SAS
SAS
SSD
Stor
Node
SAS
SAS
SAS
SSD
SAS SAS
SSD SSD
Stor
Node
SAS
SAS
SAS
SSD
Stor
Node
SAS
SAS
SAS
SSD
SAS SAS
SSD SSD
Redundant Client Interconnect (IP)
PY PY
File
App
Block
App
PY PY
File
App’
Block
App’
Redundant Cluster Interconnect (IP) Redundant Cluster Interconnect (IP)
multiple
Storage
Nodes
multiple
Storage
Nodes
 2 replica will be placed locally
 The 3rd replica will go to the remote site
 On a site failure, all data will be available at the
remote site to minimize the RTO Recovery Time
Objective

46 © Fujitsu 2014
Silver Pool
VolB-1
primary
VolB-2
primary
VolB-3
primary VolB-3
2nd
VolB-2
2nd
VolB-1
2nd
…
VolB-3
3rd
VolB-2
3rd
VolB-1
3rd
VolB-3
4th
VolB-2
4th
VolB-1
4th
Ceph HA/DR Design
PY PY
File
App
Block
App
PY PY
File
App’
Block
App’… …
Gold Pool
VolA-1
primary
VolA-2
primary
VolA-3
primary
… VolA-3
2nd
VolA-2
2nd
VolA-1
2nd
VolA-3
3rd
VolA-2
3rd
VolA-1
3rd
Bronze Pool
VolC-3
3rd
VolC-2
3rd
VolC-1
3rd
VolC-1
primary
VolC-2
primary
VolC-3
primary VolC-3
2nd
VolC-2
2nd
VolC-1
2nd
…
VolC-3
3rd
VolC-2
3rd
VolC-1
3rd
Class repli=2 repli=3 repli=4 ECode
SSD Gold
SAS Steel Silver
SATA Bronze

47 © Fujitsu 2014
Consider balanced components (repli=3)
Storage Node
Journal /
PCIe-SSD
PY PY
File
App
Block
App
Redundant Cluster Interconnect (IP)
1GB/s
OSD / SAS
Raid Cntl
2GB/s
2GB/s
1GB/s
Memory
2GB/s
1GB/s
2GB/s Memory
(1)1GB/s writes via the Client
interconnect results in 6GB/s
bandwidth to internal disks
(2)Raid Controllers today
consist of ~ 2GB protected
DRAM inside, which allows a
pre-acknowledge of writes
(= write-back) to SAS/SATA
disks
(3)The design of using a fast
Journal in front of raw HDDs
makes sense, but today there
should be an option to
disable it

48 © Fujitsu 2014
Architecture Vision for Unified Storage
File System
File System
File System
OSDs
MDSs
NFS, CIFS
File System
File System
Client / Hosts
VMs /
Apps
VMs /
Apps
File System
File System
Object
File System
File System
Block
S3, Swift, KVS FC oE, iSCSI
File System
File System
Client / Hosts
File System
File System
Client / Hosts
vfs
Ceph
http
Rados-gw
LIO
RDB

49 © Fujitsu 2014
Challenge: How to enter existing Clouds ?
 ~ 70% of all OS are running as guests
 In ~ 90% the 1st choice is VMware-ESX, 2nd XEN + Hyper-V
 In most cases conventional shared Raid arrays through FC or iSCSI as
block storage are used
 VSAN and Storage-Spaces as SW-Defined Storage is already included
 iSCSI-Target or even FC-target in front of RBD Ceph can be attached to
existing VMs, but will enough value remain ?
 So, what is the best approach to enter existing Clouds ?
 Multi tenancy on File, Block and Object level access and administration
 Accounting & Billing

51 © Fujitsu 2014
Finding & Hints
 Ceph is the most comprehensive implementation of Unified Storage. Ceph
simulates “distributed swarm intelligence” which arise from simple rules
that are followed by individual processes and does not involve any central
coordination.
 The Crush algorithm acts as an enabler for a controlled, scalable,
decentralized placement of replica data.
 Choosing the right mix of Ceph & System parameter will result in a
performance push
 Keep the data bandwidth balanced between Client/Cluster Network,
Journal and OSD devices
 Likely the Ceph code has some room for improvement to achieve higher
throughput

52 © Fujitsu 2014
Ceph principles – translated to value
 Wide striping
 all available resources of storage devices (capacity, bandwidth, IOPS of SSD, SAS, SATA) and
 all available resources of the nodes (interconnect bandwidth, buffer & cache of RAID-Controller, RAM)
 are disposable to every single application (~ federated cache)
 Replication (2,3…8, EC) available on a per pool granularity. Volumes and Directories can be assigned to pools.
 Intelligent data placement based on a defined hierarchical storage topology map
 Thin provisioning: Allocate the capacity needed, then grow the space dynamically and on demand.
 Fast distributed rebuild: All storage devices will help to rebuild the lost capacity of a single device, because the
redundant data is distributed over multiple other devices. This results in shortest rebuild times (h not days)
 Auto-healing of lost redundancies (interconnect, node, disk) including auto-rebuild using distributed Hot Spare
 Ease of use
 create volume/directory size=XX class=SSD|SAS|SATAtype=IOPS|bandwidth
 resize volume size=YY
 Unified and transparent Block, File and Objectaccess using any block device: node internal special block
device, internal SCSI device from RAID-Controller or external SCSI LUNs from traditional RAID arrays
 Scalable by adding capacity and/or performance/bandwidth/IOPS including auto-rebalance

54 © Fujitsu 201454
Fujitsu
Technology
Solutions
CTO Data Center Infrastructure and Services
Dieter.Kasper@ts.fujitsu.com

Best Practices with Ceph as Distributed, Intelligent, Unified Cloud Storage - Ceph Day Frankfurt

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