1. 1 Copyright 2014 MPSTOR LTD. All rights reserved. Getting
performance & scalability on standard platforms, the Object vs
Block storage debate Speakers William Oppermann, CEO, MPSTOR Mo
Hassine, Director of Product and Marketing, MPSTOR
2. 2 Copyright 2014 MPSTOR LTD. All rights reserved. Storage
requirements in the datacenter Wide range of application workloads
provisioned at scale cost effectively Provisioning ALL the
Datacenter consumers Virtual machines Tenant spaces Storage centric
services Consumer nodes Opex Complexity & management Automation
of provisioning Volume services management (snapshot, thin volumes,
replication, backup) Capex Use of Open Platforms Proprietary
platforms Storage Efficiency (the cost of redundancy in storage)
Availibility&Resiliency Component Redundancy Data
Consistency& Integrity IDA (Information Dispersal Algorithms)
Scalability Scale up Scale scale Reducing the Impact of failures
through IDA Performance per workload type BW performance IO
performance Caching acceleration Fabrics Tiering SLA/QOS
3. 3 Copyright 2014 MPSTOR LTD. All rights reserved. The BIG 6
issues Storage technologies for the cloud struggle in the
datacenter because there is a BIG 6 requirements list that is very
difficult to fulfil. 1) Storage must be resilient to component
failure 2) Storage must be scalable (addition of capacity and
amount of data stored) 3) Storage must cater for a wide range of
application workloads 4) Storage provisioning to a wide range of
storage consumer types Virtual machines Tenant spaces Storage
centric services Consumer nodes 5) Storage must deliver 1,2,3,4 at
a CAPEX and OPEX compatible with Utility cloud computing. Open
platforms Highly automated provisioning End user provisioning tools
Simple and easy to administer by the cloud operator 6) Storage must
be secure
4. 4 Copyright 2014 MPSTOR LTD. All rights reserved. The BIG 6
storage challenges RESILIENCY SCALABILITY WORKLOADS CONSUMER TYPES
TCO (CAPEX and OPEX) DATA Security
5. 5 Copyright 2014 MPSTOR LTD. All rights reserved. BIG 6
ranking out of 10 for Block and Object 0 2 4 6 8 10 12 RESILIENCY
DIVERSE WORKLOADS TCO (Cloud CAPEX and OPEX) STORAGE SECURITY
SCALABILITY MULTIPLE CONSUMER TYPES Block Object
6. 6 Copyright 2014 MPSTOR LTD. All rights reserved. ISSUES
BLOCK OBJECT ? COMMENTS RESILIENCY Object has large windows of
failure & poor storage efficiency DIVERSE WORKLOADS Object
suitable for narrow range of workload types TCO (Cloud CAPEX and
OPEX) Block storage high cost to Administer STORAGE SECURITY Both
object and block storage needs managed encryption and security
access SCALABILITY BLOCK storage very difficult to manage for scale
out multi fabric, multi tier storage. MULTIPLE CONSUMER TYPES
Missing paradigm in both Block , File and Object storage for many
of the datacenter consumer types Full Support Partial Support Poor
Support One paradigm does not fit all
7. 7 Copyright 2014 MPSTOR LTD. All rights reserved. Object
& Block storage Object storage is resilient to component
failures, scalable and can be delivered on open hardware platforms
cost effectively for some workload types. Strong point is cost,
scalability and managing the impact of disk failures but object
storage has poor performance in workloads requiring high IO &
low latency => It can deliver partially on the BIG 6 issues
Block storage is a resilient & somewhat less scalable
technology that is usually delivered on proprietary platforms that
supports all workload types. Strong point is very good mixed work
load performance, support for different media types & fabrics.
Scaling can be difficult and the recovery time from failures in
large silos can prohibitive. => It can deliver partially on the
BIG 6 issues
8. 8 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#1 (using object storage) Storage needs to be resilient to
component failure Object storage keeps multiple copies of data
objects within its storage pool (3 copies => storage efficiency
= 100/3 = 33%) Each copy is replicated over time T, during this T
minutes the data is not protected, T can be long multiples of
minutes and is not acceptable in many mission critical enterprise
class configurations. If a disk fails the object store notes which
objects were dependent on that disk and makes new copies over time
of the lost objects. => At the cost of low storage efficiency
and a wide window in time of non protection object storage delivers
resiliency. For the designed for USE CASE (upload/download outside
the datacenter of digital media (photos, videos, documents) this
can be acceptable
9. 9 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#2 (using object storage) Storage needs to be scalable
(Scalable in terms of overall capacity and amount of data stored)
Adding storage capacity is relatively simple but usually requires a
major re- balancing operation when storage is added to the pool
(all the data gets reshuffled between its disks) Data objects are
stored using data base technology. Each object has a unique ID, the
ID is used as a database KEY, as the object store grows the lookup
cost of keys grows with the number of objects Object storage has a
high overhead every time the object is accessed. This overhead
grows with the number of stored objects. Object storage delivers
partially the requirement of increasing the pool capacity size at
the cost of rebalancing itself when capacity is added. Object
storage struggles with the ever increasing number of objects it has
to store.
10. 10 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#3 (using object storage) Storage needs to cater for a wide
range of end user workloads The SEMANTICS of Object storage make it
suitable for only a restricted (but important) set of workloads,
its semantics also make provisioning an end user task which
improves the provisioning OPEX cost. Storing
photos/videos/documents in object storage works well, taking
seconds to load a photo/video is acceptable if once loaded the data
can stream. For data processing workloads the object storage
look-up costs make it unusable. Adding BLOCK interfaces on OBJECT
stores only makes the problems worse in terms of performance but
also increases the complexity of the solution.
11. 11 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#4 (using object storage) Storage delivery to a wide range of
storage consumer types Object storage uses a very specific API
making it useless at provisioning multiple consumer types which all
need block storage to run/boot but can in some cases use object
storage when in operation for certain workloads.
12. 12 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#5 (using object storage) Storage needs to deliver 1,2,3,4 at
a CAPEX and OPEX compatible with Utility cloud computing pricing.
Storing photos/videos/documents using object storage works well.
Simple interface, end user can provision, storage looks like a big
pool => Object storage on low cost media & open hardware
platforms even with low storage efficiency due to its multiple
copies works well for upload/download of large media files. Object
storage is poor at transaction data processing, low latency and
high IO type workloads Object storage partially delivers on the
big5 requirements !
13. 13 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#1 (using Block storage) Storage needs to be resilient to
component failure Block storage uses RAID or ERASURE code
technology to store data resiliently. Block storage uses many
techniques to improve resiliency to failures such as multipathing,
dual controllers, cache coherency techniques which store data with
a ZERO time window of non redundancy. Data is secured and
consistent, i.e a block storage controller can die mid flight of an
IO and the system will store work. This very complex hardware and
software is a considerable technical challenge and is reflected in
the high cost and proprietary nature of block storage.
RAID&ERASURE techniques require special consideration so that
the BUILD and REBUILD recovery times stay within defined
limits.
14. 14 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#2 (using Block storage) Storage needs to be scalable
(Scalable in terms of overall capacity and amount of data stored)
Adding storage usually requires a new RAID, a RAID ADD cost depends
on the size of the RAID and the RAID level. Disks sizes are now
getting so large the RAID build time is becoming a major problem to
admins, during a RAID rebuild the raid is not fully redundant and
if enough disks are lost due to failure the entire dataset can be
lost. Block storage has been designed for a high setup cost and a
very low transaction cost during operation. Unlike object storage
the transaction cost of accessing data scales perfectly with the
amount of data stored since the transaction cost is a FIXED cost.
Block storage works very efficiently when managing a storage SILO
(SCALE-UP by adding more storage to the SILO), SCALE-OUT (i,e
adding more storage SILOS in parallel) is difficult. This scale out
issue in contrast to OBJECT storage puts BLOCK storage into an "all
the eggs in one basket" type technology. The basket may be
resilient and redundant but there is only one basket.
15. 15 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#3 (using object storage) Storage needs to cater for a wide
range of end user workloads Block storage has inbuilt into its
semantics and implementation the ability to cover all workload
types. This is BLOCK storages strongest capability and is one of
its major advantage over Object storage. Additional advantages are
its ability to transition across multiple high speed fabrics and
provide the raw building blocks for other protocols such as File
and Object storage iself.
16. 16 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#4 (using Block storage) Storage delivery to a wide range of
storage consumer types Block storage does not in it self provision
all the consumer types in the datacenter, as a base technology its
far easier and higher a performing base to develop tools that can
provision all the consumer types of Virtual machines Tenant spaces
Storage centric services Consumer nodes
17. 17 Copyright 2014 MPSTOR LTD. All rights reserved. BIG5
Issue#5 (using Block storage) Storage needs to deliver 1,2,3,4 at a
CAPEX and OPEX compatible with Utility cloud computing pricing.
Block storage scales both in capacity and quantity of data stored.
Block scales up very easily, scales out with difficulty, manages
all workloads very well and is very resilient to failures with a
ZERO window of time when the data is not redundant. Block supports
wide range of FABRICS and protocols (SAS, FC, IB, FCoE, Eth) in
comparison to Object storage (Eth, IP). Block storage has good
support for media tiering and accelerated caching using SSD
technology. Block storage is complex and in most cases uses
proprietary hardware. Block storage is more difficult to administer
than object storage.
18. 18 Copyright 2014 MPSTOR LTD. All rights reserved. Block ?
Object, Both? or something else ? Is that the end of the debate?
Who wins Block or Object ? Do we need both ? Do we need something
else ? What are the hard limitations
19. 19 Copyright 2014 MPSTOR LTD. All rights reserved. Block
v/s Object semantics LBA@,LEN Volume SCSI CDB ByteArrayInputStream
input = new ByteArrayInputStream("Hello World!".getBytes());
conn.putObject(bucket.getName(), "hello.txt", input, new
ObjectMetadata()); S3 API
20. 20 Copyright 2014 MPSTOR LTD. All rights reserved. Block,
Object, Block over Object disk HD driver FS Mgt DB+Files API Head
Block Layer Object Layer Block over Object Layer Obj App LBA Map 1
3 2 User Land Kernel S3 Backer Rados Block Device (RDB)
22. 22 Copyright 2014 MPSTOR LTD. All rights reserved. Scale
out storage 10G/16G/40G 8PB 8PB 8PB 8PB 192TB 128 1 40G/100G Scale
out Cluster up to 10 nodes Exporting BLOCK FILE OBJECT Scale out
Storage Nodes Up to 128 nodes
23. 23 Copyright 2014 MPSTOR LTD. All rights reserved. Scale
Out Scale out allows a storage service to scale in real time
without service interruption in both capacity and performance Scale
out storage can be Block (ScaleIO, Orkestra-IDA (information
dispersal) Object (CEPH, SWIFT) File (GPFS, Gluster)
24. 24 Copyright 2014 MPSTOR LTD. All rights reserved. Object
IDA (Information Dispersal Algorithm) D#1 Proxy D#1 C#2 C#3 C#1 C#2
IO_Write Ring1 5 3 FIG 2 Zone1 Zone2 Zone3 Zone4 D 4 5 Proxy Load
Balancer 2
25. 25 Copyright 2014 MPSTOR LTD. All rights reserved. Block
IDA (Information Dispersal Algorithm) D/3 VBS D/3 D/3 D/3 D/3 D/3
IO_Write Group1 5 3 FIG 2 Zone1 Zone2 Zone3 Zone4 D 3 3 VBS2 P P 4
VBD
26. 26 Copyright 2014 MPSTOR LTD. All rights reserved. Storage
Resiliency Object storage makes copies over (large window of non
protection)across multiple storage arrays Weak real time consistent
data Scale out block storage stores data redundantly in real time
across multiple storage arrays Strong real time consistent
data
27. 27 Copyright 2014 MPSTOR LTD. All rights reserved. IDA
Volume create RG1 RG2 RG3 RG4 RG1 RG2 RG3 RG4 RG1 RG2 RG3 RG4 RG1
RG2 RG3 RG4 Space is reserved according to the RAID QOS on
independent Arrays and built into an RAID on the VBS layer Array 1
Array 2 Array 3 Array 4 IDA RAID IDA VOLVBS Node Storage Array
Nodes QoS1 QoS2 QoS3 VBD
28. 28 Copyright 2014 MPSTOR LTD. All rights reserved. Managed
pools is not Scale-Out BASIC STANDARD 1G iSCSI 10G iSCSI 6G SAS
4/8/16G FC Fabrics Storage Containers PREMIUM Orkestra SDS
Automation StoragePools ScaleoutComputeGroups Compute Containers
SDS automates the provisioning of storage per group Each GROUP has
a configurable QoS & SLA BRONZE compute GROUP SILVER compute
GROUP GOLD compute GROUP
30. 30 Copyright 2014 MPSTOR LTD. All rights reserved.
Multi-Tenancy using throttling MPSTOR Data Center Storage
Automation SOS supports Bandwidth Throttling (both IOPS and
MB/s)
31. 31 Copyright 2014 MPSTOR LTD. All rights reserved. Feature
Benefits Snapshot Allows users to take point of time copies Thin
Provisioning Allows capacity to be added as demand increases Rate
limiting Allows multi tenancy of high speed media Replication
Resilient to failure Tiered Management Allows wide range of
workloads Solid State Disk Caching Speeds up IO by caching High
Availibility No single point of failure, no loss of functionality
or data FC/FCoE SAN Automated management of high speed fabric SAS
SAN Automated management of high speed fabric iSCSI SAN TAutomated
management of high speed fabric NFS protocol File access protocol
for LINUX CIFS protocol File access protocol for Windows Storage
Features in the data-center NFS CIFS FC SAS iSCSI
32. 32 Copyright 2014 MPSTOR LTD. All rights reserved. SOFTWARE
DEFINED STORAGE Storage categories & Contenders Converged
Storage Converged Infrastructure Server SAN VSAN Block File Object
BIG IRON Evolving set of terms and definitions which are frequently
a source of confusion
33. 33 Copyright 2014 MPSTOR LTD. All rights reserved. ISSUES
BLOCK OBJECT ? COMMENTS RESILIENCY Strong or weak real time
consistency DIVERSE WORKLOADS Block performance TCO (Cloud CAPEX
and OPEX) Use standard platforms for BLOCK or OBJECT with SDS
automation STORAGE SECURITY ? Gaps exist SCALABILITY Scale out
technologies exist for both Block and Object storage MULTIPLE
CONSUMER TYPES ? Missing paradigm in both block , file and object
storage for many of the datacenter consumer types Full Support
Partial Support Poor Support One paradigm does not fit all
34. 34 Copyright 2014 MPSTOR LTD. All rights reserved. Thank
You