StorPool Storage presenting at Storage Field Day 25pdf

Introduction to StorPool Storage
Boyan Ivanov, CEO

1960s and on
Mainframes
Client-Server with
Local Storage

1980s and on
Client-Server with
Local Storage

Shared-Disk
Storage Software
1990s and on
Dual-Controller
Shared-Disk Arrays

2000s and on
Shared-Disk
Storage Software
1990s and on

2010s and on
Software-Defined
Storage

SIMPLE & PREDICTABLE
1-3 RACKS
THEY
WORK…

Dynamic
provisioning
?
Self-service
?
Parallel service for
1,000s of workloads
?
Always-on
?
99.999% with
maint. included
?
No single point
of failure
?
Consistent low
latency
?
Linear
Scalability
?
BUT WHAT IF
YOU NEED

AT SCALE
10
racks
200
compute servers
1PB
saved data
5,000
VMs

200+
compute servers
4,000+
10+
racks
200+
1PB+
saved data
20 PB+
5,000+
100,000+
VMs
АТ LARGE SCALE = SCALE × 20

People are
simply unhappy
No automation,
ticket-based
IT gets in the
way of “the
business”
Slow to address
changes in
market dynamics
Squeezed profit
margins due to
org & tech debt
ROAD
TO

Mainframes Dual-Controller
Shared-Disk
Storage
Shared-Disk
Storage Software
Software-Defined
Storage
Client-Server with
Local Storage

Mainframes Dual-Controller
Shared-Disk
Storage
Shared-Disk
Storage Software
Software-Defined
Storage
Client-Server with
Local Storage
IS A RADICAL DEPARTURE
StorPool Storage

AGENDA
Introduction to
StorPool Storage
Boyan Ivanov, CEO
Tech Excellence
Boyan Krosnov, CTO
Helping IT Teams
Do Storage Right
Alex Ivanov, Product Lead

StorPool IN A NUTSHELL
Of Flash Storage
Deployments
100s
Countries of
Deployment
30+ Profitable
& Growing

RUNNING WORKLOADS FOR
CUSTOMERS

Dynamic provisioning
Self-service
Parallel service for 1000s of workloads
Always-on
99.999% with maintenance included
Consistent low latency
Linear scalability (no data silos)
No single point of failure

HOW DID WE MAKE THIS?
Dynamic provisioning
Self-service
Parallel service for 1000s of workloads
Always-on
99.999% with maintenance included
Consistent low latency
Linear scalability (no data silos)
No single point of failure

SILICON VALLEY
OF THE EASTERN
BLOCK

KNOW-HOW
FROM A DIFFERENT
DOMAIN
High Performance
Networking
IT Service Providers

StorPool Storage
Metrics & Analytics Alerts & Reporting VolumeCare CMP Integrations

FULLY MANAGED SOLUTION
Parallel multi-node
shared-nothing
architecture
Always-on:
Non-disruptive
everything
Made for large-scale
modern IT
infrastructure
StorPool Storage
Software only,
hardware-agnostic
Linearly scalable
Block first,
some file

IT TEAMS HAVE
LONG-TERM
RELATIONSHIPS WITH
Datacenter
providers
Hardware
vendors
Distributors
and resellers

PROVIDES STORAGE AS A SERVICE UNDER
BRING YOUR OWN DEVICE TERMS

Colocation
Cooling
Electricity
Hardware
People Software
ROI
TCO
Planned
maintenance
Expansion
costs
Controller
upgrades
Drive
replacement
End-of-life
refresh
Storage
Management
Overhead
Optimize on-premises
clouds’ TCO and ROI
through data storage
Software
Licensing
Infrastructure
Management
Overhead
Costly & Complex
Datacenter Design
Compute
Servers

StorPool
Storage
Dual-Controller
Shared-Disk
Arrays
5-YEAR TCO
AT SCALE
Considering Scope1
TCO Factors
52%
SAVED

StorPool
Storage
Dual-Controller
Shared-Disk
Arrays
5-YEAR TCO
AT SCALE
Considering Scope 2
TCO Factors
59%
SAVED

StorPool
Storage
Dual-Controller
Shared-Disk
Arrays
5-YEAR TCO
AT SCALE
Considering Scope 3
TCO Factors
60%
SAVED

WE NEED TO CHANGE THE WAY WE
THINK ABOUT DATA STORAGE
@StorPool
StorPool.com StorPool Storage

Tech Excellence
Boyan Krosnov, CTO

The Old Way:
Dual-Controller
Shared-Disk
Arrays
Base array
Disk shelf
Disk shelf

HOW DO SHARED-DISK ARRAYS
FIT INTO THE TYPICAL DATACENTER
ENVIRONMENT?
ToR
SWITCH
SPINE
SWITCH

Slow adoption
of innovations
X
High-cost system
lifecycle
X
Multi-rack availability
requirements
X
Bottlenecks at
the controllers
X
Always-on
operating model
X
Datacenter-scale
Ethernet network
X
Building block
concept
X
BUT WHY IS THE OLD MODEL
NOT WORKING?
Hard to manage
at scale
X

Uses commodity
servers
Fits datacenter
designs
Fits customer
workflows
Addresses demanding
applications
Extracts near-100%
of HW potential
Has high-speed
data efficiency
Scales linearly Fades into the
background
THE IDEAL PRIMARY
STORAGE PLATFORM

StorPool Storage
IS THAT
RADICAL DEPARTURE

StorPool Storage
(up to 4,032 nodes)
Network – 2x/4x 10-100 GbE
per storage node
Storage Node Storage Node Storage Node
Parallel NVMe/TCP Service
Parallel iSCSI Service
Highly Available NFS Service
Server Server
SSD
Server
SSD
SSD
SSD
SSD
SSD
SSD
SSD
SSD
iSCSI,
NFS
NVMe/TCP,
iSCSI, NFS
NVMe/TCP,
iSCSI, SP Block,
NFS
NVMe/TCP, iSCSI
SP Block, NFS
VMware Hyper-V
block
block
block
KVM Hypervisor
StorPool Client
block device driver
block
block
block
Linux Bare-Metal
StorPool Client
block device driver

Parallelism and
shared nothing
architecture
Exceptional latency
and throughput
Up to 64 sub-clusters
managed as a unified
large-scale system
Linearly scalable large
sub-clusters of capacity
& performance
Datacenter-scale
deployments
Data management,
incl. backup and DR
StorPool Storage
ARCHITECTURE AND
HOW IT WORKS

StorPool Framework
upstream drivers:
- block device
- vhost
hardware threads, tasks,
scheduling, loader
memory management huge
pages, shm, allocator
network protocols:
- Ethernet
- TCP/IP (ARP, ICMP, TCP) & BGP
downstream drivers:
- pci device
- nvme, linux block
- ixgbe, i40e, mlx4, mlx5, bnx2x,
bnxt, virtio-net, linux net
- rdma
- ioat
- hsleep, ksleep
Linux,
KVM
integrations
StorPool Protocol
on-disk
format
Block Client NVMe/TCP
iSCSI
Bridge mgmt
Server
Beacon
service discovery
service monitoring
quorum
PMem
NVMe
SATA/SAS
VMware
Hyper-V
Oracle
LVM
CLI
GUI
ARCHITECTURE
DEEP DIVE

LINEARLY SCALABLE CAPACITY
AND PERFORMANCE
453
823
773
1,686
75
106
71
106
4k qd 7x64 70/30[kIOPS]
16k qd 7x64 70/30[kIOPS]
2,000
1,500
1,000
500
0
3 servers,
12 NVMes
4 servers,
16 NVMes
5 servers,
20 NVMes
6 servers,
24 NVMes
7 servers,
28 NVMes
3 servers,
12 NVMes
4 servers,
16 NVMes
5 servers,
20 NVMes
6 servers,
24 NVMes
7 servers,
28 NVMes
4k qd 7x1 100% read[us]
4k qd 7x1 100% write[us]
100
75
50
25
0
125

NVMe SSD
KVM Virtual Machine
KVM Virtual Machine
NVMe SSD
NVMe SSD
NVMe SSD
NVMe SSD
NVMe SSD
NIC
server instance
1CPU thread
2-4 GB RAM
server instance
1CPU thread
2-4 GB RAM
server instance
1CPU thread
2-4 G RAM
block instance
1CPU thread
1 GB RAM
25GbE
25GbE
Kernel
bypass
Kernel
bypass

DATA
PROTECTION
Storage Node
SSD SSD
SSD SSD
Client
Volume/LUN
Storage Node
SSD SSD
SSD SSD
Storage Node
SSD SSD
SSD SSD
Storage Node
SSD SSD
SSD SSD
Storage Node
SSD SSD
SSD SSD
Distributed
storage and
processing
Synchronous
replication across
storage nodes

PERFORMANCE CONSISTENCY,
FOR REAL!
Average 95th 99th 99.9th
Read latency 133µs 224µs 358µs 498µs
Write latency 66µs 81µs 99µs 293µs
NEW APP ADDED:
Transactional
workload with
4KB read/write
SYSTEM LOAD:
50,000 IOPS
per TB Usable
4KB 70/30
read/write
500,000
IOPS total

MULTI-RACK SETUP
StorPool Storage can be deployed “horizontally” in multiple racks
StorPool Cluster

HARDWARE REFRESH CYCLE
Without service
interruption
Replace servers
one-by-one
No maintenance
windows
No complex data
migrations

Integrations:
Kubernetes CSI, OpenStack Cinder,
CloudStack, OpenNebula, OnApp
StorPool VolumeCare
(enables snapshot-based
backup and DR services)
StorPool API
Kubernetes
OpenStack
CloudStack
OpenNebula
OnApp
Bespoke/custom
integrations
INTEGRATIONS & AUTOMATIONS

SCOPE OF MANAGED SERVICES
Continuous Service Improvement
DESIGN DEPLOY FINE-TUNE
MONITOR
MAINTAIN

WHAT ENABLES OUR MANAGED
SERVICES AT SCALE
Мetrics, analytics Еvents and alarms,
multi-channel alerting
Аutomation
tooling
Unification of
everything possible Cross-immunization
Hyperscaler-like approach
to infrastructure

LESS HASSLE &
FEWER PEOPLE
INVOLVED
Higher overall cloud
efficiency
Higher performance
Higher density
Fewer servers
=>
=>
=>

&
60% more
applications per rack
Fewer compute
servers needed for
applications
60% less electricity
consumption
They're paying less $ even though electricity prices went up!
USA electricity prices are 2x higher.

Parallel, always-on, no silos, automated.
No toil, no hassle.
@StorPool

Helping IT Teams
Do Storage Right
Alex Ivanov, Product Lead

“The hyperscalers have long proven
that commodity hardware is good
enough to run industrial-grade
workloads. The trick is in applying
the right software layer to get the
most out of the available hardware.”

Issues with performance
consistency
Infrastructure
sprawl
ENTER THE STORAGE GAME
Complexity Endless cycles
High-cost
workarounds

HARD & EXPENSIVE SYSTEM LIFECYCLE MANAGEMENT
License tied to
specific hardware
Systems rarely add
performance during
lifecycle
Modernize, and you
need a new license
Time-consuming HW
modernization
Fixed overhead
Perfect for prophets
Pay-as-you-go?
Typically a fixed
monthly cost
Hiked MSRPs
Enable fictional
60% - 90% discounts
Support and drives
Tagged as % of MSRP
ZOOMING INTO THE
STORAGE GAME

VENDORS NEED ROI FOR EACH
SYSTEM THEY DEVELOP
WHY?

THE IMPORTANCE OF BLOCK
STORAGE PERFORMANCE
Slower block
storage
performance
Slower
application
response
times
Reduced
user
productivity
Delayed
time-to-
market
Reduced
revenues
and margins

PRODUCTIVITY PLUMMETS WITH
SLOW APP RESPONSE TIMES
80%+
for experts
75%+
for average
95%+
for novices
Transaction
rate
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0
0.25 0.5 0,75 1.0 1.25 1.5
Expert
Average
Novice
Application response time (seconds)

SLOW APP RESPONSE TIMES COST
MONEY, ESPECIALLY AT SCALE
$150k
per month
for 50 users
$300k
per month
for 100 users
$9 million
per month
for 3,000 users
1 2 3
Potential monthly savings from rapid application response
for varying numbers of simultaneous users
10
9
8
7
6
5
4
3
2
1
0
0
300
Users
200
Users
100
Users
50
Users
X
$100,000
per
month

IMPACT OF FASTER APP RESPONSE
TIMES AT SCALE?
Faster
response
times
More
productivity
More
savings
1 2 3
Potential monthly savings from rapid application response
for varying numbers of simultaneous users
10
9
8
7
6
5
4
3
2
1
0
0
300
Users
200
Users
100
Users
50
Users
X
$100,000
per
month

CULPRITS FOR BLOCK STORAGE
PERFORMANCE DEGRADATION
Usable capacity
utilization ≥ 50%
Storage drive failures,
large drive rebuilds
Data migration
between data silos
Inefficient storage
software stack
Cleanup processes
Synchronous
replication
Write penalties due
to write algorithms
Network/system
interconnect mismatch

TRADITIONAL APP RESPONSE TIME
WORKAROUNDS
Faster, bigger direct-attach storage
SSDs, NVMe SSDs, Persistent Memory, etc.
Faster, better server-storage interconnect
Higher bandwidth, NVMe-oF
Application splitting & database sharding
On-prem or in the cloud
More, faster, newer storage systems
Come with faster drives and interconnect
At large scale? Not so much...
Easy to do at small scale
Storage, servers, software, network, racks, etc.
Leads to infrastructure sprawl
Leads to orphaned storage, a.k.a. siloed storage
App instances require separate I/O
Splitting, patching, troubleshooting,
updating, load balancing...
Waste costs across the board

TRADITIONAL APP RESPONSE TIME
WORKAROUNDS LEAD TO
EXCESSIVE 2X TO 3X MORE
EXPENSIVE INFRASTRUCTURE TCO!

MORE, FASTER, NEWER STORAGE
ARRAYS HELP, TO A POINT
Faster and bigger
SSDs
More arrays enable
more app splitting &
DB sharding
Better overall
performance @
premium cost
Some scale-out
options with active-
active pairs

BUT, WHAT HAPPENS AT SCALE,
ESPECIALLY AT LARGE SCALE?

WHEN YOU HAVE TENS OR
HUNDREDS OF ARRAYS?
USABLE AND PERFORMANCE CAPACITY CHAOS
Continuous workload
monitoring
Constant rebalancing
between data silos
Performance
degradation due to
capacity consumption
Labor-intensive data
migrations for system
lifecycle management

Why? Vendors need ROI for each system they develop.
IN SHORT, ENTER THE STORAGE GAME
License tied to
specific hardware
Systems rarely add
performance during
lifecycle
Modernize, and you
need a new license
Time-consuming HW
modernization
Fixed overhead
Pay-as-you-go?
Typically a fixed
monthly cost
Hiked MSRPs
Enable fictional
60%-90% discounts
Support and drives
Tagged as % of MSRP

ENDING THE STORAGE GAME
Variable cost that
follows revenue
Easy for business
Pay-per-use
Pricing aligns with
end-user offerings
No pricing fugazis
With usage-based volume
discounts
24/7 support
Included with the StorPool
license
EASY, NON-DISRUPTIVE SYSTEM LIFECYCLE MANAGEMENT
Why?
Systems built with cheap
read-intensive
storage drives
Adding drives or servers
scales capacity and
performance
Modernize as vendors
introduce new server
generations
Add new hardware
generations
non-disruptively

WOULDN'T BE POSSIBLE WITHOUT...
Consistent
performance
even under
massively
parallel I/O
Easy
workload
consolidation
with less
infrastructure

“The StorPool product is perfect from cost,
flexibility (grow-as-you-go), and performance
point of view. Second - and for us equally
important - is the way the StorPool team
helps us to build the most advanced hosting
platform and their fast and committed
support in case of an issue."
Hink Wiersema
Product Manager
and Architect

ATOS VIRTUAL ORACLE COMPUTING HOTEL
LAUNCHED IN 2013
Hinks' goals for what
should be offered to
end customers
Flexibility
Decrease
Oracle
licensing
Low Total
Cost of
Ownership
Running
workloads
for
Banks
Insurance
companies
Hospitals
In the
Netherlands

License tied to
specific hardware
Systems rarely add
performance during
lifecycle
Modernize, and you
need a new license
Time-consuming HW
modernization
Fixed overhead
Pay-as-you-go?
Typically a fixed
monthly cost
Hiked MSRPs
Enable fictional
60%-90% discounts
Support and drives
Tagged as % of MSRP
HINK‘S GOALS
Flexibility Low Total Cost
of Ownership
Decrease
Oracle licensing

DIFFICULT SYSTEM LIFECYCLE MANAGEMENT
License tied to
specific hardware
Systems rarely add
performance during
lifecycle
Modernize, and you
need a new license
Modernize to
next-gen HW?
Migration
Fixed Overhead
Pay-as-you-go?
Typically a fixed
monthly cost
Hiked MSRPs
Enable fictional
60%-90% discounts
Support and Drives
Tagged as % of MSRP
HINK‘S GOALS
Flexibility Low Total Cost
of Ownership
Decrease
Oracle licensing
CRASHED STRAIGHT INTO THE STORAGE GAME

WORKING WITH HIS TEAM,
HE GOT TO A POINT WHERE HE FACED
5 SIGNIFICANT CHALLENGES
Maintain service
quality and
optimize prices
Align his storage
pricing with
customers’ pay-
per-use model
Remove
maintenance
windows for HW
and SW changes
Stay competitive
by continuously
modernizing
HW components
Optimize the
VOC hotel
lifecycle and cost
management

StorPool HAD A SOLUTION
FOR ALL OF THAT
Maintain service quality
and optimize prices
99.999% availability
with maintenance included
Align his storage pricing with
customers' pay-per-use model
Pay-per-use with
no cost surprises
Remove maintenance windows
for HW and SW changes
Easy, non-disruptive system
lifecycle management
Stay competitive by continuously
modernizing HW components
Easy adoption of performance and capacity
innovations non-disruptively, at any time
Optimize the VOC hotel lifecycle
and cost management
Systems built with COTS hardware
that easily support his environment

BUT THAT CREATED
A NEW CHALLENGE FOR HINK...

…HE HAD TO ADDRESS ALL THE
"WHY StorPool"
QUESTIONS NEXT…

А NEW MINDSET
FOR RUNNING THE ATOS VOC HOTEL

NEW
Datacenter
design
Always-online
operating model
Way of working -
during normal work
hours, no more 2am
and weekend work
Backend billing model
(OpEx, not CapEx)
Ways to track and
manage service P&L
Way of thinking
about hardware
modernization
(add anytime
approach)
Primary storage
partner
Atos team simply
creates and
manages volumes
Way of working with a
solution provider:
Single data pool
that is always
available
Hyperscaler-like
Principles for
deploying, managing,
monitoring, and
maintaining block
storage

AFTER PUSHING ALL THAT THROUGH,
THERE WAS A FINAL HURDLE:
THE FIRST UPDATE…

THE USUAL ORDER OF OPERATIONS
Notify all your customers that you're doing an update
Plan maintenance downtime for workloads
Plan this for a Saturday or Sunday, or during the night
Have all your people on deck
Be ready to fix everything in case things go wrong
Be ready to roll back in emergencies

THE UPDATE ROLLED THROUGH,
NODE BY NODE

KEEP WASTING INFRASTRUCTURE, TIME,
PERSONNEL, RESOURCES & MONEY

LET StorPool HELP YOU
EXIT THE STORAGE GAME
ONCE AND FOR ALL
@StorPool

StorPool Storage presenting at Storage Field Day 25pdf

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