HIPAS UCP HSP Openstack Sascha Oehl
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The document discusses various technologies related to cloud computing and big data including Docker, Kubernetes, OpenStack, and Hitachi's Hyper Scale-Out Platform (HSP). It provides overviews of what each technology is used for, such as Docker for containerization, Kubernetes for container orchestration, OpenStack for building cloud infrastructure, and HSP for hyperconverged scale-out infrastructure. It also includes diagrams illustrating how these technologies can work together in an enterprise environment to provide solutions for areas like data lakes, analytics, and private clouds.
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HIPAS UCP HSP Openstack Sascha Oehl
- 1. What’s up with Docker, Kubernetes, Openstack, HSP UCP Basetechnology and Sales Vision Sascha Oehl Senior Manager Presales Germany North & Public Initiative Lead UCP
- 3. Massive Change is happening
- 4. 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
- 5. 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
- 6. Yesterday’s infrastructure won’t cut it Virtualization Users Devices 2x 2x 3x 8x Data
- 7. Monitor, troubleshoot and remediate (23%) Provision, Patch and Configure (22%) Handle service requests (18%) Meetings (16%) Innovate (21%)
- 8. Change in IT – Change in Market
- 9. 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
- 10. $0 $200 $400 $600 $800 $1,000 $1,200 $1,400 2013 2014 2015 Market Size OpenStack (451 Research) Hyper-Converged (IDC) Billions$
- 11. DATA ANYWHERE, ANYTIME 1 SPEED TO MARKET VISION AND VALUE
- 12. 2 ADVISOR FACILITATE I.T. TRANSFORMATION PROVIDE NEW CAPABILITIES
- 13. AGILITY 3 AUTOMATION ENABLEMENT OF BUSINESS OUTCOMES
- 14. Bimodal IT Mode 1 Mode 2 Stability Efficiency Safety Accuracy Process Core Business Systems RUN Agility Innovation Fail Fast Speed Automation DEVOPS Budget Workload
- 15. Budget & Power is redistributed
- 16. Known Hierarchy
- 17. New Players – Changed Importance
- 19. Budget Shift to Innovation 100 100
- 22. V topology planning bonding and availability network monitoring address management routing and access control port provisioning bandwidth provisioning capacity planning firmware updates
- 23. 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
- 24. V storage provisioning & management network provisioning & management IP routing & access control image & OS deployment remote access & management firmware updates
- 25. V topology planning bonding and availability network monitoring address management routing and access control port provisioning bandwidth provisioning capacity planning firmware updates
- 26. V Simple tasks Complex coordination Slow deployment
- 27. 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
- 28. VMany flavors
- 29. V
- 30. V
- 31. How about Docker, Kubernetes, Openstack & HSP
- 34. 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
- 36. Virtual Machines Container Including Operating System TTD Minutes Static Just the Apps TTD Seconds Flexible/Agile
- 37. 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
- 38. Openstack
- 39. Driver: Cloud Ready Instance Platform
- 40. Openstack is not… A Virtualisation A out of the box solution Making crappy Hardware work
- 41. Openstack is… Cloud Framework like Service Provider use API and Management Set Easing the management of different vendors hardware
- 42. 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
- 43. 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
- 44. 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
- 45. 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
- 46. 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
- 47. 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
- 48. 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
- 49. 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
- 50. 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
- 51. 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
- 52. 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
- 53. 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
- 54. Hitachi Hyper Scale Out Platform
- 55. 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
- 56. 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
- 57. 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
- 58. 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
- 59. 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
- 60. KVMKVMKVMKVMKVMKVMKVMKVM “ACTIVE” DATA LAKE – GLOBAL NAMESPACE WEB APPS ANALYTICS APPLICATIONS Enterprise Scalable File System HSP ELASTIC VM MANAGEMENT – PERSISTENT DATA
- 61. Lose a disk, rebuild on other disks
- 62. Lose a node, rebuild on other nodes
- 63. Lose a rack, redistribute to other racks
- 64. The eScale Distributed File System Equipment Usage File System Billing File System Maintenance File System
- 65. 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
- 66. 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
- 67. 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
- 68. 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)
- 69. 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
- 70. 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
- 71. 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
- 72. Data Lake PDI PDI PDI PDI Archive Regulatory Compliance Analytics Hadoop/Spark NoSQL Elastic Search PBA
- 73. 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
- 74. 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
- 75. Anything real?
- 76. Demo Theater Hitachi Data Systems to Showcase Ongoing Commitment to OpenStack https://www.youtube.com/watch?v=NGP_0SgcUTo
- 79. What‘s now?
- 80. WE CONNECT REAL INNOVATION WITH SOCIETY
- 81. WITH WHAT’S NEXT WE CONNECT WHAT WORKS
- 82. @saschaoehl Follow me on Twitter
- 84. Thank You
Hinweis der Redaktion
- 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!
- 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
- 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.
- "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.
- 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.
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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
- Theme for Pentaho 6.0
- 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.
- 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
- 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
- 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
- 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
- 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
- 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.
- Useful innovation for the benefit of society is our vision: Social Innovation.
- We connect what works today with what you need tomorrow. A long-term vision is vital for this kind of integration over time.