Ospc for openstack forum sep 2011 v2

Intel DCSG, Yan Li Solution ArchitectIntel IT, Tonny Liu

Aug 2011

IT Research and Development

Open Source Private Cloud 2011

PoC for Blade, Rack, and Container DC

Agenda

• OSPC POC for Blade, Rack, Container DC

• Background

• What’s the focus, the product or research area introduction

• Open Source Cloud Solution Biz value

• Propose for next steps (POC, etc) for cooperation

• Demo

OSPC PoC for Blade, Rack, Container Data Centers

Slide 3

IMS

Container DC

HDC Rack

Intel R&D SSG, DCSG, IT collaboration in ZZICB ecosystem enabling

OSPC (open source private cloud)• OSPC is an open source private cloud based on

OpenStack that Intel IT has developed. OSPC demonstrates how enterprises can secure and manage a private cloud based on open source software by taking advantage of Intel technologies such as TXT and Power Node Manager. In addition to enabling OpenStackexploit the advanced capabilities of Intel-based platforms, OSPC provides a web-based interface that allows users/administrators manage a virtualization environment based on multiple hypervisors including Xen and ESX

Research Approach

• Open Source: Use open source software to build completely functional private Cloud

• Broad Collaboration: Work together with 4 Intel platform groups and leverage server and DC component solutions from other groups

• Test Bed and Research: Build a test bed, focus on innovations, and validate research and innovative ideas on test bed

• Useful Prototype: Implement a full open source private cloud with extensive features, ready for real user trials

• Enterprise POCs and Usage Studies: Evaluate Open Source Private Cloud, study potential IT usages of OSPC, and carry out real user trials

Private Cloud Architecture/Overall Scope

Previous work summary• Build open source based Private Cloud based on OpenStack framework

• User control with different user groups, AD/LDAP authentication integration

• NM/TXT integration

• Policy template

• Implement OpenStack object storage solution (Glance and Swift)

• Enterprise guest OS image provisioning and build (Win 2003, 2008, Suse)

• Low level physical indicator through IPMI channel

• Third party monitoring solutions integration (Ganglia, Nagios)

• VM lease

• VM snapshot and migration

• Deployed OSPC 1.0 to FM DC

• Deployment streamline with PXE

Slide 8

Open Source Cloud Solution Biz value

• Unleash full potential of IA products

• Leverage Open source community wisdom

• Customize to meet customer requirements

• Avoid vender lock in

• Free or limited cost compare with other commercial Cloud solutions

Next steps

Slide 10

• Support KVM and Vmware

• Implement enterprise policy/logic in resource mgmt

• Integrate more IA manageability features

• VM collection/template

• Contribute OpenStack community.

Presenter

Presentation Notes

Research Areas: Positioning and Registration Creating 3D representation of real world Multi-modal sensing in indoor/outdoor Work in complex local network, scenarios Vision-based mapping between real/virtual worlds Landmark detection and recognition 2) Navigation and Guidance Retrieve navigation metadata from back-end servers Augment directional text and image information Generate a see-through dynamic content Attentive interaction for hand-held devices Photo-realistic 3D rendering and browsing Core Problems to be solved: Robust feature to represent the 3D characteristics of objects, buildings, street, etc., under complex indoor or outdoor scenarios Adaptive fusion strategy for multi-modal sensors when the location context is ambiguous and full of clutters Reliable quality assessment given little prior knowledge about the positioning accuracy of various sensors and circumstance assumptions Stable 3D tracking and camera pose estimation algorithms considering the runtime performance and power efficiency limits Accurate correspondence initialization for real/mirror worlds and being able to re-start reasonably when lose to track or position or registration Robust 3D feature sets Adaptive sensor fusion Stable 3D tracking and camera pose alg Restart and Retrack Challenges The positioning accuracy is below fine-scale navigation requirement, and positioning sensors for indoor and outdoor are different solutions. The geo-tagging content are ambiguous which can not be parsed by current Artificial Intelligence technologies Vision based techniques are constrained, while multi-modal sensing techniques are limited accurate, there is no unified combination strategy The input and output during the user interaction has great constraints due to the small screened mobile devices. Algorithm and communication constraints considering the power efficiency for mobile devices Complexity – fusing multiple sources of information to derive the context which is dynamically changing in real time. Fine scale nav reqts/ large grain positioning accuracy Ambiguous geo tagged content Limited accuracy multi-modal sensors Low power and compute constraints Complexity Core Problems Location metadata parsing and indexing and incremental geo-tagged information updating and maintenance Efficient query and intelligent matching for multi-modal inputs such as image, video, location or key word Tangible interface for flexible user interaction such as gesture recognition, attentiveness recognition and relevance feedback Personalized generation of directional guidance information by integrating vision and graphics technologies Active managing the computation resources between client and server considering the power and communication limits

Ospc for openstack forum sep 2011 v2

Technology

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