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Cloud Provisioning: Building an efficient system in the Cloud - Michael Zunke
 

Cloud Provisioning: Building an efficient system in the Cloud - Michael Zunke

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Today still a lot of service offerings are sold by a flat-rate "all you can eat" subscription model. That’s simple to understand and simple to implement. But is it the right way to sell services? ...

Today still a lot of service offerings are sold by a flat-rate "all you can eat" subscription model. That’s simple to understand and simple to implement. But is it the right way to sell services? Especially if you could split off add-ons? When you watch companies growing in the cloud you see that tweaking your offering and finding the right business model becomes vital for building a successful business. We have enabled companies in on-premise scenarios to achieve this during the last 25 years. As we have been moving into the cloud and started offering this flexibility to cloud companies (or yet to become cloud companies) we have gone through rebuilding our system for the cloud in the right way. In this talk we want to share with you lessons learnt from building our cloud service. We’ll start off with some fundamental properties of distributed systems and will analyze the impact on the offering. We’ll look at the requirements for such system and will build an architecture to these requirements by including the fundamental mechanics of the cloud. By taking care of the CAP-theorem we’ll find the right decision to adapt the classical scenarios to the cloud.

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    Cloud Provisioning: Building an efficient system in the Cloud - Michael Zunke Cloud Provisioning: Building an efficient system in the Cloud - Michael Zunke Presentation Transcript

    • Cloud ProvisioningBuilding an efficient System in the CloudMichael “MiZu” ZunkeCTO SRM, Safenet
    • AgendaWhat is the thing to move to the cloud?Cloud – What‘s different? – Design CriteriaOur FrameworkDetailsRecap: Lessons learnt
    • Software Monetization – Classic Product On-Premise Copy Protection Products Orders / Entitlement / Production / Activation Licensing Tracking Reporting End-User Admin
    • Parallels On-Premise Cloud Copy Protection Authentication Products Service Catalog Orders / Entitlement / Provisioning, Contract, Production / Activation & Entitlements Licensing Authorization Usage Tracking & Tracking Billing Mediation Reporting Monitoring & Reporting End-User Monitoring End-User Admin & Management
    • Some backgroundThe CAP Theorem Consistency Partitioning Availability Tolerance
    • Eventually Consistent“Real internet systems are a careful mixture ofConsistency and Availability subsystems”- Dr. Eric BrewerProfessor, UC Berkley, Co- founder Inktomi “Eventually Consistent - Building reliable distributed systems at a worldwide scale demands trade-offs between consistency and availability” - Werner Vogels, CTO Amazon.com
    • Checking Cloud CompatibilityLicense Models / Authorization• Category 1 • Quasi stateless / autonomous - harmless • Perpetual • Expiry date• Category 2 • Asynchronous - mostly harmless • Post paid• Category 3 • Synchronous - strict consistency!! • Max Concurrent Use • Depleating
    • Conclusions for our Problem DomainSome Classic License Models do not SCALE - Problem: require a globally limited Resource - This inherently requires STRICT CONSISTENCY!Classic case for „we always did it like this“ meets cloud.Going cloud is not only a technical challenge!It can require change in offering because of technology
    • So what did we do?Work with business owner to find the right solutionApply what we learnt before – explore how weak consistency can help.
    • Converting… Map to Eventually Consistent ApproachNo sharp Cut-Off in distributed license resource data!• Eventually consistent means no strict limits can get guaranteed. Some ‘over usage’ might happen.• In case strict limits are required, you will have to accept slower response times and greater impact on disconnects. Strict consistency leads to reduced QoS for the end-user.
    • Paradigms of our Cloud ArchitectureNo Delay! speed, bandwidth & reaction time of service must not suffer just because the service is licensed/tracked.Effects of “slowness” Amazon: 100 ms delay caused a 1% drop in revenue. Google: 400 ms delay caused a 0.59% decrease in search requests per user. Yahoo!: 400 ms delay caused a 5-9% decrease in traffic. see e.g. http://goo.gl/ADQuR Autonomous nodes - Local intelligent caching!
    • Paradigms of our Cloud ArchitectureScale with the customer!No additional infrastructure because of licensing system. Stateless modules Plug into each platform!
    • Sentinel™ Cloud Services in action
    • Sentinel™ Cloud Services in action
    • Sentinel™ Cloud Services in action
    • Sentinel™ Cloud Services in action
    • Sentinel™ Cloud Services in action
    • Sentinel™ Cloud Services in action
    • in Detail Client Node- Feature X, TTLx - Login Event- Feature Y, TTLy - Logout Event Authenticate AuthMap Session Directory EMS SCC Service Data Engine Provision Contract Data Store Data Store Amazon EC2
    • Key Take AwaysDistributed systems require different thinking • Connections are not granted • Autonomous components helpMoving to the cloud might have impact on what you can deliver • Your cloud offering might have to be different from the classic • Work with your business owner to get this straight earlyExisting Customers might need to be educated • Customer expectations are driven by what they know • You might have to lead them thru the same process
    • Questions? http://sentinelcloud.com
    • ReferencesCAP Theorem http://goo.gl/37dms - Nice intro from Julian BrowneEventually consistent http://goo.gl/yTCpW - Good starting point for consistency considerations. Blogpost from W. Vogels, CTO AmazonGoogle’s timing experiments / WPO http://goo.gl/ADQuR
    • Cloud ProvisioningBuilding an efficient System in the CloudMichael “MiZu” ZunkeCTO SRM, Safenet