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Jason WaxmanGeneral Manager
High Density Compute DivisionData Center Group
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Today 2015
~80% of Internet connected
devices are computers &
phones3
Only 25% of the world is Internet
connected today1
New technologies will connect over 1
billion additional users to the cloud2
Cars, TVs, households, etc. to increase
connected devices 2.5x to >10 billion
globally3
8X network, 16X storage & 20x compute
capacity needed7
2.5B photos on Facebook4
30B videos viewed/mos5
Google indexes >1T pages6
Econom
ies o
f S
cale
Serv
er
Consolid
ation
Manageabili
ty
High
Low
“The cloud consumes
1-2% of the
world’s energy” 11
FBI seizes servers supporting 350
hosting customers due to criminal
investigation of a
few customers12
“No specific standards currently exist for
enabling interoperability between private
clouds or public
cloud providers“14
“IT will spend ~$2T on deployment &
operations unless smarter infrastructure
radically simplifies management of
virtualized environments.“13
**Estimated savings based on hypothetical optimization case studies.
Actual results from such optimizations may vary considerably based on the complexity and numerous variables involved.
Acceleration
Throughput
Performance
Efficiency
Virtualization
Security
Power
Network & Storage
Parallelism
Scalability
Configurations
Manageability
Airflow
Voltage Regulation
Rack Density
Cable Management
Floor Plan
Aisle Layout
Integration
Operating Conditions
\
Dense 1S/2S ServersOptimized RacksContainers Micro Servers
3. Power saving:
Workload based power-tuning
4. Power Conservation:
Prolong operation during DC outage
1. Power Monitoring: Real-time power consumption
Avoid datacenter hotspots
Thermal / Power aware scheduling
2. Increase rack density:
Enable higher density with power capping
Pre outage Post outage
Mass policy push of lower power state
PowerWorkload Pre cap
powerPre cap perf Post Cap
powerPost cap perf
Cpu intsv
Io intsv
Memory
Mix / real
Workload
characterization
1 Values estimated based on a hypothetical 50K server deployment and savings over 3 years to end user, source: Intel , Sept ’08
See backup for details
Platform
Optimization
Power
Management
Software
Optimization
Datacenter
Efficiency
up to
$6M
With 10%
VR efficiency
gain1
up to
$8M
With 30W reduced
/ system1
up to
$20M
With 10% code
efficiency upside1
up to
$1M
With 10% PUE
improvement1
Open Cirrus™,
UC Berkley RADLab,
Universities
Intel Cloud Test Bed
POCs and Joint Labs
Cloud Reference
Architectures,
Tools and Training
Deployment
Best Practices
Building
Optimized Clouds
Advanced
Cloud Research
0
400
800
1200
1600
Xeon® 5470 Xeon® 5570
We
b T
ran
sa
ctio
ns p
er
Se
co
nd
*Source: Parallels, see backup for details
Visit our booth for a demo!
up to 2X
• Increased sales by 10% in Six Weeks
• 30% improvement in Energy Efficiency
• Increased Application Manageability
• 60% increase in revenue per server
• 77% decrease in IT administration cost
• Up to 100 VPS on Quad core server
• 30% improvement in Energy Efficiency
• Reduced Server count, increased new applications through Virtualization
Cloud deployments offer a great economic opportunity to Hosting Service Providers
Servers are a critical part of a cloud infrastructure –choose a Real Server
The Data Center offers many opportunities for optimization
Follow well known methods to reduce your TCO and accelerate your service deployments
Accelerate your Cloud Deployments with help from Intel
Legal Disclaimer• Intel may make changes to specifications and product descriptions at any time, without notice.
• Performance tests and ratings are measured using specific computer systems and/or components and reflect the approximate performance of Intel products as measured by those tests. Any difference in system hardware or software design or configuration may affect actual performance. Buyers should consult other sources of information to evaluate the performance of systems or components they are considering purchasing. For more information on performance tests and on the performance of Intel products, visit Intel Performance Benchmark Limitations
• Intel does not control or audit the design or implementation of third party benchmarks or Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmarks are reported and confirm whether the referenced benchmarks are accurate and reflect performance of systems available for purchase.
• Intel processor numbers are not a measure of performance. Processor numbers differentiate features within each processor family, not across different processor families. See www.intel.com/products/processor_number for details.
• Intel, processors, chipsets, and desktop boards may contain design defects or errors known as errata, which may cause the product to deviate from published specifications. Current characterized errata are available on request.
• Intel Virtualization Technology requires a computer system with a processor, chipset, BIOS, virtual machine monitor (VMM) and applications enabled for virtualization technology. Functionality, performance or other virtualization technology benefits will vary depending on hardware and software configurations. Virtualization technology-enabled BIOS and VMM applications are currently in development.
• 64-bit computing on Intel architecture requires a computer system with a processor, chipset, BIOS, operating system, device drivers and applications enabled for Intel® 64 architecture. Performance will vary depending on your hardware and software configurations. Consult with your system vendor for more information.
• Intel, Intel Xeon, Intel Core microarchitecture, and the Intel logo are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries.
• © 2008 Standard Performance Evaluation Corporation (SPEC) logo is reprinted with permission
Xeon 5570-based and Xeon 5470-based Test configurations
– 3 XEON 5570 web-servers (h2eb1, h2eb2, h2eb3)
– 2 x Intel Xeon 5570 CPUs, 16 GB RAM each
– 3 XEON 5470 web-servers (al01, al02, al03)
– 2 x Intel Xeon 5470 CPUs, 16 GB RAM each
– 30,000 sites on IPs 10.22.1.100 – 10.22.1.129
– 1000 sites per one IP
– Each site contains several static pages, Wordpress, Joomla and phpBB
– 1 Load Balancer node (h2eb10)
– Linux Open source load balancer LVS, included in Linux kernel 2.6.28-rc3 or later
– 4 MySQL servers for site’s databases (h2eb10, h2es4, h2es5, h2es6)
– 2 x Intel Xeon 5570 CPUs, 16 GB RAM each
– 4 servers are used as HTTP clients (h2es* and h2eb8)
– Operating system used: CentOS ver 5.4 (Cloud Linux*)
Backup for TCO Comparison of Various Optimizations**Processor Selection:
• For IPDCs deploying a 5K server installation, by selecting servers with greater performance and equivalent power consumption, if you are able to reduce footprint by 20% versus plan, the savings could be ~$4M
• Intel Xeon 5500 versus Intel Xeon 5400 example: 2.5X better performance on SpecWeb 2005, handling far more HTTP requests / second, offering greater work at same energy profile
• 20% fewer servers:
• If 5K servers planned for purchase, 20% fewer = 1000 servers avoided
• CAPEX savings
• $3M based on $3K / server acquisition cost
• OPEX savings:
• Electricity avoidance:
• 1000 servers at 275W per server running at 60% utilization
• 3.5 year life span, $.10 / kWhr electricity cost, savings = $500K
• Cooling avoidance
• With PUE of 2 = ~$500K cooling cost
• Total OPEX savings $1 M over 3.5 years
Optimizing Power Supplies
• Optimizing on power supply efficiency for 5K servers:
• Assume going from 80 percent efficient to 90 percent
• Assume this is prospective, just like the server performance example
• A 10% efficiency improvement on a 250W server = 25W
• Multiplied over 5000 servers
• Assume $.10 / kWhr
• Savings in direct power: ~400K
• Savings in cooling at PUE of 2: ~400K
• Total savings $800K
Improving PUE by 10%
• Saving 10% PUE in a 5000 server facility
• Assume the energy consumed is 250W x 5000 servers
• Assume 65% utilization
• Assume $.10 / kWhr
• Energy cost over 3 years = ~$2.5M
• Cooling cost at PUE of 2 = ~$2.5M
• Saving 10% of total energy cost = $500k
**Estimated savings based on hypothetical optimization case studies.
Actual results from such optimizations may vary considerably based on the complexity and numerous variables involved.