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PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 1
Ericsson HDS8000
server platform
based on intel’s...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 2
The Advent of HyperScale Computing:
Google. Amazo...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 3
Challenges and opportunities
Infrastructure silos...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 4
Hyperscale
Datacenter
OperationalEfficiency
Asset...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 5
Cloud giants has taken the
next quantum leap
60%
...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 6
Hyperscale datacenter metrics
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 7
The Problem:
HyperSCale Vendors (google, amazon,
...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 8
HOW DO WE DO THIS?
Ericsson HDS8000
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 9
› HW infrastructure: HW and management
› Software...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 10
Intel and Ericsson - realizing the software defi...
Intel Confidential 11
Data Center Challenges
Infrastructure has not kept up with increasing business demands
Business Need...
Intel Confidential 12
Today’s Architecture
• Proprietary and preconfigured
• Upgrade as a system
• Limited flexibility
Intel Confidential 13
Intel ® Rack Scale Design: The Framework
1. Pooled systems 2. Pod management 3. Network fabric 4. Po...
Intel Confidential 14
Intel® Rack Scale Design
INCREASED
PERFORMANCE/$
HYPERSCALE
AGILITY
• Black Box to Glass box
• Avoid...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 15
15
HW disaggregation
Current server
›CPU, Disc, ...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 16
High resource utilization
Today’s servers
CPU Me...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 17
Memory
Pool
Storage
Pool
Networking
Pool
Acceler...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 18
Software defined infrastructure
(SDI)
Disaggrega...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 19
SDI resource management
HDS Command Center
HDS 8...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 20
Software defined infrastructure
lean and agile d...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 21
Bare metal as a service
Customer A
Customer C
Cu...
PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 22
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PLNOG 17 - Piotr Jasiniewski, Przemek Papużyński - Ericsson HDS 8000 Server platform based on Intel’s RSD – zmierzch klasycznych architektur serwerowych

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Prelegenci przedstawią w skrócie charakterystykę rozwiązania serwerowego HDS 8000 firmy ERICSSON, opartego o architekturę Rack Scale Design (RSD).

Poruszone zostaną zagadnienia związanie z przewagami takiego rozwiązania (zarówno od strony technicznej jak i użytkowej) nad dotychczas dostępnymi, i korzyściami które są tego efektem.

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PLNOG 17 - Piotr Jasiniewski, Przemek Papużyński - Ericsson HDS 8000 Server platform based on Intel’s RSD – zmierzch klasycznych architektur serwerowych

  1. 1. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 1 Ericsson HDS8000 server platform based on intel’s rsd zmierzch klasycznych architektur serwerowych September 26th, KRAKÓW
  2. 2. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 2 The Advent of HyperScale Computing: Google. Amazon. Facebook. designed to provide a single, massively scalable compute architecture start small, infrastructure will expand as demand grows
  3. 3. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 3 Challenges and opportunities Infrastructure silos with low utilization DC as a differentiator - Web scale flexibility, performance and economics Workloads and data growth is sky rocketing Low function velocity – slow Time To Market Weeks / months
  4. 4. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 4 Hyperscale Datacenter OperationalEfficiency Asset Efficiency Operator Operator Google Amazon Facebook Operators Operator The Advent of HyperScale Computing
  5. 5. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 5 Cloud giants has taken the next quantum leap 60% 30% 15% Data center operators have led an efficiency revolution through the use of virtualization solutions Cloud giants have jumped to a new level of efficiency with hyper-scale data center operations reaching utilization as high as 65% Traditional Server Hyperscale Strategic Data Center Virtualized Data Center Enterprisedatacenterutilization
  6. 6. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 6 Hyperscale datacenter metrics
  7. 7. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 7 The Problem: HyperSCale Vendors (google, amazon, Facebook) are Not Selling Their Secrets The answer: Industrialized hyperscale datacenter: Intel® rack scale design Ericsson HDS8000
  8. 8. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 8 HOW DO WE DO THIS? Ericsson HDS8000
  9. 9. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 9 › HW infrastructure: HW and management › Software-defined HW for all workloads › NFV/Telco, IT and OSS/BSS workloads › Using Intel RSD technology Hyperscale Datacenter System 8000
  10. 10. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 10 Intel and Ericsson - realizing the software defined infrastructure vision based on Intel RSD Ericsson Make hyperscale available for every industry with Cloud Giants economics Ericsson and Intel CEOs at MWC 2013
  11. 11. Intel Confidential 11 Data Center Challenges Infrastructure has not kept up with increasing business demands Business Needs • Reduce operational and capital expenses. • Deliver new services in minutes, not months. • Optimize data center based on real-time analytics. • Address application workload needs with agility. • Scale capacity without interruption. Less than 50% server utilization2 Inefficiency Data growth doubles every 18 months1 Growth Agility New services can take a week or more to provision1 1 Worldwide and Regional Public IT Cloud Services 2013–2017 Forecast. IDC (August 2013) idc.com/getdoc.jsp?containerId=242464 2 IDC’s Digital Universe Study, sponsored by EMC, December 2012
  12. 12. Intel Confidential 12 Today’s Architecture • Proprietary and preconfigured • Upgrade as a system • Limited flexibility
  13. 13. Intel Confidential 13 Intel ® Rack Scale Design: The Framework 1. Pooled systems 2. Pod management 3. Network fabric 4. Pod-wide storage Modular scalable management architecture Pod-wide Management Scalability Pod wide storage Network fabric
  14. 14. Intel Confidential 14 Intel® Rack Scale Design INCREASED PERFORMANCE/$ HYPERSCALE AGILITY • Black Box to Glass box • Avoid outages, over subscription and under performance through HW specific insights and telemetry • Eliminates stranded resources • Software configurable capacity coupled with high performance interconnects to increase utilization and reduce TCO • Self Allocating Storage provides IT with improved performance/$ IMPROVES DATA CENTER OPERATIONS • Software Defined Infrastructure: Shape shift capacity and capability with modular, open architecture to meet the application • Tailor performance to meet application SLAs by selecting from pooled compute, storage & network resources • Faster Time to Market Industry standards based hyperscale solution for CSPs and the Telecommunications industry
  15. 15. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 15 15 HW disaggregation Current server ›CPU, Disc, Ram and NIC (>80% of server cost) on same card in same chassis ›Server has a fixed configuration – need to fit all workloads ›Whole server need to be changed at the same time even though different components have different lifecycles Future server ›CPU, Disc, RAM and NIC on different sleds ›CPU, Disc, RAM, and NIC can be changed according to individual lifecycles ›HW can be configured dynamically for better utilization and performance CPU RAM Disc NIC CPU RAM Disc NIC
  16. 16. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 16 High resource utilization Today’s servers CPU Mem Disks NIC Acc CPU Mem Disks NIC Acc Disaggregated Datacenter ~80% of HW CAPEX & power consumption Workload usage Un-used hardware CPUs Disks Memory NICs Accelerators x86 x86x86 x86
  17. 17. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 17 Memory Pool Storage Pool Networking Pool Accelerator Pool CPU Pool RESOURCE POOLING Private CloudTelecom Cloud Commercial Cloud HDS Command Center & RSD Network functions OSS/BSS, Media & IT functions Commercial XaaS offerings Workload optimized, dynamically provisioned datacenter hardware Optical Backplane
  18. 18. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 18 Software defined infrastructure (SDI) Disaggregate and pool (RSD) Software defined composition Command center Optical interconnect CPUs NICs RAMs Disks
  19. 19. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 19 SDI resource management HDS Command Center HDS 8000 Command Center Independent of virtualization environment vPOD 1 vPOD 2 Seamless management and integration of Ericsson and 3PP infrastructure Manage vPODs across 3PP and Ericsson HW vPOD vPOD vPOD VIM VIM VIM Complete analytical platform, monitoring and recording every aspect of the infrastructure Real-time analytics and off-line data mining VM VM VM
  20. 20. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 20 Software defined infrastructure lean and agile data center
  21. 21. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 21 Bare metal as a service Customer A Customer C Customer B › Next phase of cloud computing › Leverage RSD and hardware orchestration to fully customize servers. › Move both legacy & performance critical workloads to the cloud. › Cloud economics for bare-metal infrastructure › Consolidate internal legacy silos
  22. 22. PLNOG 2016 INTEL and ERICSSON | © Ericsson AB 2016 | 2016-09-26 | Page 22

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