Kingsum Chow, Principal Engineer, Intel
Sunil Raghavan, Senior Software Engineer, Intel
Saurabh Dixit , Director, Oracle

Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessor Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information about performance and benchmark results, visit www.intel.com/benchmarks .
Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance. Consult other sources of information to evaluate performance as you consider your purchase.
For more complete information about performance and benchmark results, visit http://www.intel.com/benchmarks
Intel, the Intel logo, Intel Inside, Xeon are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others.
© 2015 Intel Corporation.
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Oracle Public Cloud
• Overview and Architecture
• IaaS, PaaS, Storage & Networking
Optimizing the Cloud
• Project Apollo
• Intel® Xeon® Processor E5-2600 v3 Product Family
• Early Results
A spoonful of analytics
• Analytics for Cloud performance optimization
6

Core OCCS
OCCS is Foundation for New Oracle
PaaS/SaaS Services
77

Secure. Reliable. Low Cost.
Storage
Elastic Storage
Compute
Dedicated Compute
Network
Software-defined Network
IaaS: General Purpose, Engineered Systems
88

Compute Lifecycle Management Secure Access
Hardware Isolation
Launch Dedicated Instances on single-tenant hardware with network isolation
Active VM Recovery
Configure HA Policies to automatically recover failed
VMs
Instance anti-affinity
Control instance placement and distribute workloads
OCPU Allocation
OCPUs not over subscribed providing predictable performance
Image Management
Provision virtual machines using pre-packaged images or build your own images
Elastic Block Storage
Store data and applications in persistent block volumes.
Maintain persistence at OS-level using Bootable storage volumes
Orchestration Management
Automate provisioning and lifecycle operations of virtual compute topologies
Metering & Quota
Management
Monitor usage and performance metrics
Secure SSH Access
Access Oracle Compute
Cloud instances from a remote host by using a secure shell
Dynamic Firewall
Control network traffic among individual instances and/or between groups of instances
Federated Identity
Integrate with MS AD/LDAP so users can login through single sign-on
Virtual Networking
Fast Connect
Bypass public internet over a private connection. Co-locate workloads and cross connect with partner exchange.
Site to Site VPN
Connect on-premise resources to dedicated compute zones in Oracle
Public Cloud
Reserved IP
Connect each instance to the internet and access Oracle
Public Cloud resources from anywhere
99

Database Java Developer Mobile Documents
Social
Network
Big Data
PaaS Service Manager
BI
IaaS API
Messaging Process Integration
Block Storage Object Storage Compute Network
10

Customer managed services
 Customer has VM access & full control over the VM,
 Can customize settings and controls patching and upgrade cycle
Oracle managed services
 Services are fully managed by Oracle
 Oracle ensures all necessary upgrades are applied
 Both options are already tuned out of box for best performance
 Self-managed, with fully automated cloud tooling for admin, LCM operations

RMAN
Storage
Gateway
OpenStack SWIFT API
Eventual Consistency
Any NAS or SAN
 Accessible
 Secure : Enterprise-grade data protection and privacy policies
 Scalable : ON-Demand Capacity
 Reliable : Redundancy policies to Data HA
 Standards-Based : OpenStack Swift compatible REST API & Java library for data management
 Hybrid storage tiers
Archive & Glacial QOS • Backup
Global Namespace  User backup, PaaS & IaaS backup target
• Archive
 Archive for long-term retention , compliance needs

IP Reservation
Persistent Public IP
Reservation for VM instances
Elastic IP
Ephemeral Public IP from shared IP Pool
IP Pool
Pool of Public IP Addresses {
192.0.2.0/24, 198.51.100.0/24,
203.0.113.0/24
}

Source list
Destination list
Security List
A group of VMs .
Security Application
Abstracts the details of protocol definitions and port number ranges. i.e. [ssh, port 22, tcp/udp]
OR Security list
A group of VMs.
Security IP List
A group of subnet /
IP addresses.
Defines how a source security or security IP list can connect to a destination security list via a specific security application or protocol. Only PERMIT rules allowed.

Project Apollo

- Deliver predictable high performance for applications running on Oracle
Cloud
- Characterize the cloud using Oracle
Cloud workloads
- Optimize the cloud to deliver maximum performance for the workload
- Innovate, develop new technologies
- Generate blueprint of an optimized data center

Application
Developer
Java
Application
Server
Administrator
VMs
Virtual
Storage
Network
Load
Balancer
Security
Service Provider
Servers Storage Switches Racks
Database Platform
Monitoring Infrastructure
Power and
Thermal
Hardware
17

Application
Developer
Application performance – Compute,
Storage and Network,
Scalability…
Administrator
Multi-tenancy, Predictable performance,
Security, Elasticity,
Composability, High Availability …
Service Provider
Optimal resource usage,
Power, Space, Cooling …
18

Infrastructure, Power and Cooling
Cloud Incubator at Intel
2000 compute cores
27 TB of RAM
100 TB of Storage
Java as a Service
Intel Xeon E5 – 2699 v3
Database as a Service
Oracle ZFS Storage
Appliance
Operating system /
Hypervisor
Oracle Cloud
Platform
19

Future plans to evaluate Intel Cloud technologies in Memory, Storage,
Network, Security, Power….
Reference1: http://newsroom.intel.com/community/intel_newsroom/blog/2015/07/28/intel-and-micron-produce-breakthrough-memory-technology
Reference2: http://www.intel.com/content/www/us/en/architecture-and-technology/intel-rack-scale-architecture.html

Intel® Xeon® Processor E5-2600 v3 Product Family

• New Haswell microarchitecture with Intel® AVX 2.0
• Up to 50% more cores and cache for up to 50% performance increase 1
• New capabilities in the areas of virtualization, security and power efficiency
• DDR4 memory support to scale performance
Feature
Cores/Threads per socket
Last-level Cache (LLC)
Max Memory Speed (MT/s)
QPI Speed (GT/s)
Max DIMM Capacity
PCIe* Lanes /
Controllers/Speed
TDP (W)
AVX
Xeon ® processor E5-2600 v2 product family
Up to 12 Cores / 24 Threads
Up to 30 MB
Xeon ® processor E5-2600 v3 product family
Up to
Up to 1866
2x QPI 1.1 channels 6.4, 7.2, 8.0
Up to 12 Slots/Processor
130, 115, 95W
AVX 1.0
18 Cores /
Up to 45 MB
Up to 2133
2x QPI 1.1 channels 6.4, 8.0, 9.6
Up to 40 / 10 / PCIe* 3.0 (2.5, 5, 8 GT/s)
36 Threads
135, 120, 105W
AVX 1.0, AVX 2.0
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. NOTE: 1 See benchmark published results on this slide and configurations on slide following. For more information go to http://www.intel.com/performance/datacenter

IMPROVED
NEW
50% MORE
36% LESS
Faster
Memory
Virtualization features
Cores &
Threads
Idle power
Integrated IO (PCIe
3.0)
Security features
Last-level cache
Source: http://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/xeon-e5-brief.pdf
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. NOTE: 1 See benchmark published results on this slide and configurations on slide following. For more information go to http://www.intel.com/performance/datacenter

Early Results

• Model Oracle Cloud workloads with multiple simultaneous applications
• Composed of:
• JaaS & DBaaS application workload
• CPU , IO & Network stress
• Real-time data gathered from across the stack
• Application performance
• Software logs – Java, Application Server, Database
• Cloud Platform / OS
• Statistics from VMs, Hypervisor
• Intel Performance Counters
• Storage appliance
• Switches
25

We can achieve significant performance gains from our early optimization efforts of Oracle Cloud for Intel Xeon 2699 v3.
 Up to 1.5x for response time sensitive apps*
 Up to 1.2x
for throughput apps*
 Up to 10x better predictability*
– Less than 3% variation against up to 29% before optimization
1,6
1,4
1,2
1
0,8
0,6
0,4
0,2
0
Latency sensitive workload
Before optimization
Throughput sensitive workload
After optimization
* Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and
MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products.
* For more information go to http://www.intel.com/performance/datacenter .

- By enhancing resource allocation mechanism we can achieve:
- More predictable performance*
- Linear scaling* - Linear increase in performance with increase in
OCPUs
700
600
500
400
300
200
100
0
0 5 10
OCPUs
15 20
* Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and
MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products.
* For more information go to http://www.intel.com/performance/datacenter .

Use of analytics for cloud performance optimization

Apps
App 1
App 2
…
Mid-Tier
Common
Common 1
…
 WLS Application Servers in the Cloud
 Simulated Users
 Data collected from Simulated Users and Servers
29

Raw data
Raw data
Python
Multiple Platforms for Processing
R
Tidy Data Format
Tidy Data Format
Merge Data sets over Time
Check Processing
Quality
Compute Statistics
Posterior Analysis

*
**
* OATS – Oracle Application Testing Suite. Testing solution for web applications using Oracle Applications and Oracle Database.
** EMON – Intel internal tool for logging event counters against a time base

2015-07-24T13:53:13.141-0700: 75.604: [GC [PSYoungGen:
1133359K->165347K(1223680K)] 1133447K->165470K(4020224K),
0.1085510 secs] [Times: user=0.59 sys=0.08, real=0.11 secs]
2015-07-24T13:53:22.445-0700: 84.909: [GC [PSYoungGen:
1214435K->168469K(1223680K)] 1214558K->168672K(4020224K),
0.1442510 secs] [Times: user=0.97 sys=0.14, real=0.14 secs]
2015-07-24T13:53:31.495-0700: 93.959: [GC [PSYoungGen:
1217557K->149712K(1199104K)] 1217760K->149923K(3995648K),
0.1272560 secs] [Times: user=0.75 sys=0.01, real=0.13 secs]
2015-07-24T13:53:35.700-0700: 98.163: [GC [PSYoungGen:
1198800K->145280K(1185792K)] 1199011K->145499K(3982336K),
0.0946850 secs] [Times: user=0.78 sys=0.02, real=0.10 secs]
2015-07-24T13:53:41.997-0700: 104.460: [GC [PSYoungGen:
1131904K->88361K(1192448K)] 1132123K->146072K(3988992K),
0.1296750 secs] [Times: user=1.03 sys=0.14, real=0.13 secs]
2015-07-24T13:53:51.739-0700: 114.203: [GC [PSYoungGen:
1074985K->118373K(1202176K)] 1132696K->228993K(3998720K),
0.2367950 secs] [Times: user=1.00 sys=0.09, real=0.24 secs]
2015-07-24T13:53:59.035-0700: 121.498: [GC [PSYoungGen:
1116261K->145330K(1193984K)] 1226881K->266899K(3990528K),
0.2270100 secs] [Times: user=0.59 sys=0.02, real=0.23 secs]
2015-07-24T13:54:03.826-0700: 126.289: [GC [PSYoungGen:
1143218K->53006K(1190912K)] 1264787K->233618K(3987456K),
0.0936990 secs] [Times: user=0.56 sys=0.09, real=0.10 secs]
Every application server has its own GC Log,
Many files in the cloud

• Column headers are values, not variable names.
• Multiple variables are stored in one column.
• Variables are stored in both rows and columns.
• Multiple types of observational units are stored in the same table.
• A single observational unit is stored in multiple tables.
References http://vita.had.co.nz/papers/tidy-data.pdf

Data Processing is the most essential part of data analysis. It encompasses activities like outlier detection, data parsing, missing value imputation, etc.
Hadley’s contribution to the Data Analytics society:
 Proposed a guideline for processed data-> “tidy data format”.
 Developed packages in R, that would make data processing easier.

2015-07-16T10:27:20.150-0700 55.874 PSYoungGen1572864 81116 1835008 1572864 81196 3932160 0.07887
2015-07-16T10:27:32.854-0700 68.578 PSYoungGen1653980 131313 1835008 1654060 131409 3932160 0.132623
2015-07-16T10:27:49.518-0700 85.242 PSYoungGen1704177 220893 1835008 1704273 220997 3932160 0.222767
2015-07-16T10:27:59.456-0700 95.18 PSYoungGen1793757 159853 1835008 1793861 159965 3932160 0.157783
2015-07-16T10:28:08.902-0700 104.626 PSYoungGen1732717 169137 1835008 1732829 169321 3932160 0.129966
2015-07-16T10:28:20.469-0700 116.193 PSYoungGen1742001 230370 1719296 1742185 305359 3816448 0.341499
2015-07-16T10:28:26.412-0700 122.135 PSYoungGen1719266 160491 1793024 1794255 320956 3890176 0.189678
2015-07-16T10:28:31.324-0700 127.048 PSYoungGen1649387 162008 1818624 1809852 356704 3915776 0.117959
2015-07-16T10:28:38.325-0700 134.049 PSYoungGen1690840 51971 1807360 1885536 350114 3904512 0.162245
2015-07-16T10:28:49.314-0700 145.038 PSYoungGen1580803 113382 1812992 1878946 432032 3910144 0.101396
2015-07-16T10:28:56.206-0700 151.929 PSYoungGen1637094
2015-07-16T10:29:08.559-0700 164.283 PSYoungGen1566486
42774
44481
1566720
1802752
1955744
1982849
459137
493787
3663872 0.121342
3899904 0.09115
2015-07-16T10:29:53.981-0700 209.705 PSYoungGen1556929
2015-07-16T10:47:12.381-07001248.105 PSYoungGen1579052
2015-07-16T11:09:06.062-07002561.785 PSYoungGen1544331
2015-07-16T11:09:11.093-07002566.817 PSYoungGen1563726
2015-07-16T11:09:15.712-07002571.436 PSYoungGen1561650
66604
25739
45134
29234
37478
1804800
1806848
1807872
1816576
1812992
2006235
2058499
2061547
2091549
2131177
546051
542955
572957
598761
616113
3901952 0.120864
3904000 0.093274
3905024 0.052096
3913728 0.081865
3910144 0.050888
2015-07-16T11:09:17.518-07002573.242 PSYoungGen1569894 13784 1827328 2148529 611324 3924480 0.043828
2015-07-16T11:09:21.669-07002577.393 PSYoungGen1567192 47785 1823232 2164732 654786 3920384 0.059617
Tidy Data Format !!!
0.71
0.39
0.52
0.32
0.35
0.29
0.2
0.34
0.16
0.25
0.43
0.63
0.61
0.66
0.95
0.92
0.72
0.15
0.2
0.16
0.1
0.12
0.09
0.12
0.09
0.05
0.08
0.05
0.08
0.13
0.23
0.16
0.13
0.34
0.19
0.12
0.04
0.05
0.09
0
0.1
0.03
0.03
0.04
0
0.03
0
0.03
0.1
0.04
0.01
0
0.09
0.06
0.04
0.01
0

Analysis made possible.
Data visualization becomes handy.
Easy correlation among the various metrics on different systems.
Comparison of trends of metrics across systems.

Oracle Cloud
Project Apollo: Joint Intel-Oracle Collaboration
Cloud Performance Analysis for Your Applications
• Data Collection
• Data Cleansing
• Analytics

I will conduct the demo and hope to answer more questions tomorrow.
Kingsum Chow
2:30pm-4:00pm (Tuesday Oct 27)
Big Data Showcase, Moscone South, Booth 2223.
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