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What’s up with Docker, Kubernetes, Openstack, HSP
UCP Basetechnology and Sales Vision
Sascha Oehl
Senior Manager Presales Germany North & Public
Initiative Lead UCP
What‘s the driver?
Massive Change is happening
Accenture-Study: Insurance sales via the internet will double in
Europe by 2016 to 25 billion euro revenue.
Nearly two-third of the executives interviewed (64 percent)
believe that this development will be driven by companies not in
this business today, like or the e-commerce giant
Source: https://de.nachrichten.yahoo.com/accenture-studie-versicherungsabsatz-%C3%BCber-das-internet-verdoppelt-sich-000000482.html
PEOPLE
Customers, Employees
THINGS
Machines, Industrial
Infrastructure, Mobile
TRANSACTIONS
Business Applications
TX TXTX
B.I. and
Reporting
PAST
Real-Time
PRESENT
Predictive
NEAR FUTURE
Prescriptive
FAR FUTURE
Yesterday’s infrastructure won’t cut it
Virtualization
Users
Devices
2x
2x
3x
8x
Data
Monitor, troubleshoot
and remediate
(23%)
Provision, Patch and Configure
(22%)
Handle service requests
(18%)
Meetings (16%)
Innovate (21%)
Change in IT – Change in Market
Compound average growth rate (CAGR)
US$7.4 Billion
in 2013
US$17.9 Billion
in 2018
20% CAGR
In Converged
<1 % CAGR
Non Integrated
Equipment
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
2013 2014 2015
Market Size
OpenStack (451 Research)
Hyper-Converged (IDC)
Billions$
DATA ANYWHERE, ANYTIME
1 SPEED TO MARKET
VISION AND VALUE
2
ADVISOR
FACILITATE I.T. TRANSFORMATION
PROVIDE NEW CAPABILITIES
AGILITY
3 AUTOMATION
ENABLEMENT OF
BUSINESS OUTCOMES
Bimodal IT
Mode 1 Mode 2
Stability
Efficiency
Safety
Accuracy
Process
Core Business Systems
RUN
Agility
Innovation
Fail Fast
Speed
Automation
DEVOPS
Budget
Workload
Budget & Power is redistributed
Known Hierarchy
New Players – Changed Importance
Budget Control
100
100
Budget Shift to Innovation
100
100
Covered Decision Makers
Uncovered Decision Makers
V
topology planning
bonding and availability
network monitoring
address management
routing and access control
port provisioning
bandwidth provisioning
capacity planning
firmware updates
V
SAN zoning
RAID grouping
LUN carving
performance tuning
storage monitoring
topology planning
multi-pathing and availability
port provisioning
bandwidth provisioning
capacity planning
firmware updates
V
storage provisioning &
management
network provisioning &
management
IP routing & access control
image & OS deployment
remote access & management
firmware updates
V
topology planning
bonding and availability
network monitoring
address management
routing and access control
port provisioning
bandwidth provisioning
capacity planning
firmware updates
V
Simple tasks
Complex coordination
Slow deployment
add new components
replace failed component
update component firmw
provision new hosts
provision new VMs
create new clusters
add hosts to clusters
deploy applications
monitor & identify issues
troubleshoot & resolve is
VOne Solution
VMany flavors
V
V
How about Docker, Kubernetes, Openstack & HSP
Container
Docker Kubernetes
Driver:
Fast Service Deployment
Infrastructure
Host Operating System
Hypervisor
Guest
OS
Bins/Libs
App1
Guest
OS
Bins/Libs
App2
Guest
OS
Bins/Libs
App3
Infrastructure
Host Operating System
Bins/Libs
App1
Bins/Libs
App2
Bins/Libs
App3
Docker
Virtual Machines Container
Including Operating System
TTD Minutes
Static
Just the Apps
TTD Seconds
Flexible/Agile
Recipe
Ingredients:
Linux Instance – on eg. UCP Vmware, UCP HyperV, CB500, CB2500
Diskspace – like eg. VSP G200-G1000
Linux – RedHat, CentOS, …
Docker Engine
Directions:
https://www.hds.com/assets/pdf/kubernetes-on-hitachi-ucp-whitepaper.pdf
Openstack
Driver:
Cloud Ready Instance Platform
Openstack is not…
A Virtualisation
A out of the box solution
Making crappy Hardware work
Openstack is…
Cloud Framework like Service Provider use
API and Management Set
Easing the management of different vendors hardware
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Horizon
The
Management
Interface / GUI
to provide new
Instances
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Instance
The „virtual
machine“
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Keystone
The User
Database and
authentication
instance
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Glance
The Image
Repository
Stores and
Distribute
Images
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Nova
The Compute
Service
Runs
Instances on
hypervisors
like
Hitachi LPAR,
KVM,
Vmware,
HyperV...
Works with
Hitachi
CB500,
CB2500 HSP
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Neutron
The Network
Service
Manages
Networks
Private and
Shared
Gateways
Firewalls
...
Works with
e.g. Brocade
IP, Cisco IP
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Cinder
The Block
Storage
Service
Manage and
Provision
Volumes on
e.g. Hitachi
HNAS, Hitachi
VSP G200-
1000
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Swift
The Object
Storage
Service
Manage and
Provision
Object Store
Space on e.g.
Hitachi HCP
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Manila
The NAS
Storage
Service
Manage and
Provision
Network Store
Space on e.g.
Hitachi HNAS
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
2015.2 Liberty
Nova CinderNeutron
Horizon
SwiftGlance
Instance
Manila
Instance
Keystone
Instance
2016.1 Mitaka2015.1 Kilo2014.2 Juno 2016.2 Newton 201
Openstack Architecture
Recipe
Ingredients:
Nova - Physical machines – eg. CB500, CB2500, HSP
Cinder – Diskspace - eg. VSP G200-1000, HSP
Neutron – Network
Optional: Swift – Object Storage – eg. HCP
Directions:
https://www.hds.com/assets/pdf/configure-hitachi-compute-blade-500-and-hus-in-
openstack-environment-tech-note.pdf https://www.hds.com/solutions/cloud/openstack.html
Hitachi Hyper Scale Out Platform
Hadoop developer kit to build analytics apps, distributed file
system with global namespace
Pre-built analytics apps and solutions for targeted verticals
Hyper-converged scale-out platform, ideal for private
compute clouds in HSP+
Scale-Out
ELEVATES sales discussion from IT to business
Data warehouse
optimization
Offload data to Hadoop
on HSP
COST REDUCTION
SCALABILITY
Streamlined data
refinery
Hadoop on HSP for data
pre-processing
BETTER INSIGHT
Customer 360
T
X
Hitachi Content Platform
(HCP) as an insight
platform
BETTER INSIGHT
SERVICES DRIVER for both Hitachi Data Systems and Partner
PROVEN GTM models
COMPELLING ROI for customers
Compute Node
Batch App 2
Compute Node
Batch App 2Compute Node
Batch App 2
HDFS
Compute Node
Batch App 1Compute Node
Batch App 1Compute Node
Batch App 1
HDFS
NAS Filer
Object Store
Hadoop − Bring Data to Apps
Durable Enterprise-
Class Storage
Unstructured
Data
Scale: Spin up compute jobs where the data is, vs. moving terabytes of Hadoop data
Posix
NFS Serving
HSP − Bring Apps to Data
Durable Enterprise-Class Data Lake
Fast Data
Ingest
Unstructured Data
Virtual Machine
Streaming AppVirtual Machine
Streaming AppVirtual Machine
Streaming App
Virtual Machine
Batch AppVirtual Machine
Batch AppVirtual Machine
Batch App
Virtual Machine
App3
Real-Time
Analytics
Unstructured
Data Analytics
Structured
Data
Analytics
Blended
Data Analytics
Data Lake
IT Infrastructure
Log and Data
Collectors
Business Data
Collectors
IoT Data
Collectors
KVMKVMKVMKVMKVMKVMKVMKVM
“ACTIVE” DATA LAKE – GLOBAL NAMESPACE
WEB
APPS
ANALYTICS
APPLICATIONS
Enterprise Scalable File System
HSP
ELASTIC VM MANAGEMENT – PERSISTENT DATA
Lose a disk, rebuild on other disks
Lose a node, rebuild on other nodes
Lose a rack, redistribute to other racks
The eScale Distributed File System
Equipment Usage
File System
Billing File System
Maintenance
File System
Distributed Appliance Management System
VMs
Running
MARS
and
HSDP
VM
VM
VM
VMVMs
Running
Hadoop
with
Lucene
and Solr
VM
VM
VM
VM
OpenStack
Management Client
VMs
Running
Hadoop
with
HBASE
VM
VM
VM
VM
Access (NFS, HTTP, etc.)
Namespace (File system)
Storage Services (OSD)
Storage Access
(Erasure coding, redundancy)
DLM
NODE
File System
Billing File
System
Maintenance
File System
Equipment Usage
File System
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Cell
ClientsClientsClientClientClient
P
A
X
O
S
C
R
U
S
H
Cluster
File System
Billing File
System
Maintenance
File System
Equipment Usage
File System
Global Name Space
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Cell
ClientsClientsClientClientClient
P
A
X
O
S
C
R
U
S
H
Cluster
File System
Billing File
System
Maintenance
File System
Equipment Usage
File System
Distributed Data: DLM MDS, config (patented)
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Cell
ClientsClientsClientClientClient
P
A
X
O
S
C
R
U
S
H
Cluster
File System
Billing File
System
Maintenance
File System
Equipment Usage
File System
Focus: Availability, recoverability, massive scalability and performance
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Cell
ClientsClientsClientClientClient
P
A
X
O
S
C
R
U
S
H
Cluster
File System
Billing File
System
Maintenance
File System
Equipment Usage
File System
Self-Configure Self-Manage Self-Balance Self-Repair
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Access / NFS
Namespace (file
system)
Storage Services
(OSD)
Storage Access
DLM
NODE
Cell
ClientsClientsClientClientClient
P
A
X
O
S
C
R
U
S
H
Cluster
File System
Billing File
System
Maintenance
File System
Equipment Usage
File System
Efficient (re)distribution of data across nodes (CRUSH)
Quick and easy to add nodes -Very efficient failure recovery
Data Lake
PDI
PDI
PDI
PDI  Archive
 Regulatory
Compliance
 Analytics
 Hadoop/Spark
 NoSQL
 Elastic Search
PBA
Unified Compute Platform
API’s (RestFUL, OpenStack)
Hyper Scale-Out Platform
Purpose Built COTSHW Design
AnalyticsOLTP Use Cases
Enterprise JBOD/DASStorage
Integrated Hyper- converged
Scale-out
Architecture
VMware, HyperV, KVM KVM
Dramatically
reduce setup
time
Automated and
self-managed
Data-in-place
processing
Better workflow =
accelerated time to results
Run virtualized apps
at the data source
Scale-Out Hyper-Converged Modern Workloads
Speed
data
ingest
Glance
& Nova
APIs
Anything real?
Demo Theater Hitachi Data Systems to
Showcase Ongoing Commitment to OpenStack
https://www.youtube.com/watch?v=NGP_0SgcUTo
https://www.openstack.org/summit/vancouver-
2015/summit-videos/presentation/hitachi-data-
systems-a-real-world-multivendor-example-of-an-
openstack-based-managed-solution
https://www.openstack.org/summit/tokyo-
2015/videos/presentation/hitachi-iaas-solution-for-
the-enterprise-with-openstack-using-esx-kvm-
ironic-lpar
What‘s now?
WE CONNECT
REAL
INNOVATION WITH SOCIETY
WITH
WHAT’S NEXT
WE CONNECT
WHAT WORKS
@saschaoehl
Follow me on Twitter
Thank You

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HIPAS UCP HSP Openstack Sascha Oehl

Editor's Notes

  1. In many ways Big Data and The Internet of things is the result of a ‘Perfect Storm’ There’s the digital data evolution From business transactional data in databases to human generated content – word documents, images, audio, social media and so on To the Data of the Internet of Things Massive amounts of data – doubling annually There’s the technology evolution enabling us to store manage and process that data at scale and at a price point that makes it viable to do so And finally there’s the evolution in the technology and techniques to actually analyze that data and derive value from it In the past we had a relatively limited amount of data to drive decision making and much of the analysis was limited to reporting on what had happened Technology advancements enabled us to mine more data in real time to be able to react instantaneously and utilize data to model what might happen in the future for more predictive insight But, with the ability to now mine massive amounts of granular data from all data sources our observations can now be far more accurate and enable us to unlock hidden or weak signals in the data that can give us highly accurate and early prediction of problems before they occur. In other words rather than just alerting us to problems that have occurred – prevent them occurring in the first place. Analytics has moved from forensics to a weapon of preemptive control!
  2. Talk about the massive growth in all areas, data, users, performance demands, number of devices in use. This growth will continue due to Big Data and the analytics that will be run on this data. This demands high performance with instant answers. Demands on IT are changing and growing every day, needs an infrastructure that can change and adapt without outages or down time. Todays infrastructures need to be able to cope with all this and whatever comes next
  3. Customers are using converged more and more as a standard building block for their Data Center or private cloud. According to IDC the worldwide integrated infrastructure and platforms market is increasing by 20% year over year. At HDS we like to refer to Converged Platforms: Converged Platform consists of multiple components, compute, network and capacity, into a single, optimized computing package. We have seen a strong demand for our converged platform, Unified Compute Platform.
  4. "The importance of unstructured data in the enterprise is underscored by the fact that beginning in 2015, unstructured data will surpass structured data in terms of both capacity shipped and revenue. IDC estimates that in 2017, unstructured data will account for 79.2% of capacity shipped and 57.3% of revenue," said Ashish Nadkarni, research director, Storage Systems at IDC.
  5. There are three major changes that affect businesses and IT groups around the world. First, the demands of the business prompting tremendous changes in IT. An unavoidable business demand is having seamless access to data and applications across all devices, anywhere, anytime, uninterrupted, and 24/7. Fast application development and speed to market is vital for competitive advantage. IT must be efficient and must enable revenue. If it doesn’t, then the business may go outside IT for solutions – there are many alternatives available and the business can’t wait if they are to survive.
  6. The second major shift is that the overall role of IT is changing dramatically. The CIO and IT are becoming advisors to the rest of the business on the use of technology solutions from internal and external sources. IT must also provide new capabilities such as platforms that help companies with digitization and the overall transformation of the business model. IT must facilitate it’s own transformation. IT can no longer rely on efficiency. It must move past legacy infrastructure to allow a new way to deliver and use IT – such as a cloud delivery model.
  7. The third major shift in IT is in the delivery strategy. Agility – IT needs to make a very big move from a hardware-centric infrastructure and capabilities to a software-defined approach that is nimble and flexible. An open design that allows IT to handle the business shifts much faster than waiting two years for a big technology refresh. Automation – It’s the foundation for cloud. It not only saves cost, it offers speed to market, including faster application development. Lastly, IT needs to help with business outcomes. The best CIOs on the planet are the next CEOs. Look how technology is so pervasive in every company. The reactive IT groups will fall behind. The IT people who are actually bridging the gap between technology and changing business models will be the new business executives.
  8. distributed, scale-out storage using a distributed architecture and file system with global namespace. First layer: IaaS (for NFS. We don’t do SMB) 2nd layer: Simply want a Hadoop distro supported (build and implement) 3rd layer: Want this layer, but won’t be there day 1 At the core we are a platform for scale out…. With APIs and tools for analytics and virtualized apps With the top layer being the solutions and apps that drive customer business
  9. With traditional Hadoop, you bring your data into the data center As you use the data for Hadoop, your import it into HDFS and ship the data to corresponding compute nodes And you send more data as you increase your workload to more nodes HDFS: considerable coordination, network transfers to support Hadoop jobs; compute, net & storage costs
  10. With traditional Hadoop, you bring your data into the data center As you use the data for Hadoop, your import it into HDFS and ship the data to corresponding compute nodes And you send more data as you increase your workload to more nodes HDFS: considerable coordination, network transfers to support Hadoop jobs; compute, net & storage costs
  11. With Hawaii, you bring your data into the data center Once on Hawaii it stays in place, doesn’t need an import beyond the original Posix ingest You now spin up VMs where the data is, rather than shipping the data to the compute Results in: Minimal network traffic, ability to have in-place data handling = simplified management Key is flipping Hadoop architecture from pushing data to silo’d storage to retaining data in place and leveraging central management/storage/IT
  12. With Hawaii, you bring your data into the data center Once on Hawaii it stays in place, doesn’t need an import beyond the original Posix ingest You now spin up VMs where the data is, rather than shipping the data to the compute Results in: Minimal network traffic, ability to have in-place data handling = simplified management Key is flipping Hadoop architecture from pushing data to silo’d storage to retaining data in place and leveraging central management/storage/IT
  13. Theme for Pentaho 6.0
  14. Data-in-place processing makes HSP ideal for running Hadoop. Run analytics applications side by side with Hadoop and leverage the same data. Quickly host web apps. Web apps have become popular due to the widespread availability and convenience of using a web browser as a client. Host web apps using HSP with VM templates that let you host apps and provide durable storage in a single platform. Analyze data from web apps in neighboring analytics applications for business insights.
  15. Resiliency When you lose a disk, will be rebuilt on other disks/nodes Achieved by copying another copy of the data to retain the 3 copy policy
  16. Resiliency When you lose a disk, will be rebuilt on other disks/nodes Achieved by copying another copy of the data to retain the 3 copy policy
  17. Resiliency When you lose a disk, will be rebuilt on other disks/nodes Achieved by copying another copy of the data to retain the 3 copy policy
  18. Resiliency When you lose a disk, will be rebuilt on other disks/nodes Achieved by copying another copy of the data to retain the 3 copy policy
  19. Showing the HDS “Big Picture for Big Data” where a complete portfolio demonstrates the power of our coverage: Multiple Data Sources acting as “tributary” rivers into the HSP Data Lake Pentaho Data Integration “shims” act as the ETL connection between the various Data Sources Run both PDI and BA on the HSP via KVM Run Hadoop and other data services on the HSP (Spark, Cassandra, MongoDB, Elastic Search, etc.) Archive for Regulatory Compliance to the HCP (Retention Policies, Data disposition, Chain-of-custody, Privileged Delete, Immutable data) Ensure data lineage and security
  20. The centerpiece of the Hawaii value proposition is fast time to business results. These surrounding attributes and benefits all contribute to this: Massive data ingest at high speed, at scale, for data intensive workloads like Hadoop. Data-in-place analysis, for example, refers to bringing apps to data. Running apps at the data source makes a difference when processing massive data sets and streams from multiple sources. It also avoids performance loss associated with moving data. Automated and self-managed is key because of the reduced operational time spent, resulting in OPEX savings. Speed of data ingest and proximity of apps to data make Hadoop and analytics jobs finish faster. Reduced hardware via converged compute and storage and reduced set up time save both CAPEX and OPEX by eliminating separate virtualization, server and storage. Maximized efficiency for open cloud architectures by running virtualized apps the source of the data on a single platform in the cloud.
  21. Useful innovation for the benefit of society is our vision: Social Innovation.
  22. We connect what works today with what you need tomorrow. A long-term vision is vital for this kind of integration over time.