AAP304 Private Cloud Principles, Concepts, and Patterns
AAP304 Private Cloud Principles, Concepts, and Patterns
AAP304 Private Cloud Principles, Concepts, and Patterns
Microbiology
Hybrid Clouds
Deployment Models
Service Models
Essential Characteristics
Rapid Elasticity Measured Service Resilient Computing Geographic Distribution Service Orientation Advanced Security
Common Characteristics
Principles provide general rules and guidelines to support the evolution of a cloud infrastructure. They Perception of are enduring, seldom Infinite Capacity amended, and inform and support the way a Service Providers cloud fulfills Approachits mission. They strive to be compelling and aspirational . These principles Ubiquitous Drive Incentivize Automation Predictability Behavior form the basis on whichDesired a cloud infrastructure is planned, designed and created
Business Value Continuous Improvement Perception of Continuous Availability Optimize Resource Utilization Holistic Approach to Availability Create A Seamless User Experience
Predictability
ConceptsResiliency are over abstractions or strategies Homogenized Pool Compute Redundancy Hardware Resources that support the principles and facilitate the composition of a cloud. Virtualized Fabric Elastic Resource Partition Shared Resources Infrastructure Management Infrastructure Decay They are guided by and directly support Service Cost Consumption Security and one or more of the principles. Multitenancy Classification Transparency Based Pricing Identity
Patterns are specific, reusable ideas Physical Fault Upgrade solutions to that have been proven Domain Domain commonly occurring problems. Reserve Scale Unit Capacity Patterns are useful for enabling the cloud computing concepts and Health Model Service Class Cost Model principles.
Capacity Plan
Resource Pooling
More Reactive
MTRS Goes Up
Simplicity is Elegance
Drives predictable performance Reduces cost of acquisition Help with predicting time for new acquisition Everything should be made as
Capital Expense
Operational Expense (power, maintenance) Stateful applications increase cost Driven by stateful applications
No UPS No Generator Straight Utility Power Availability: 0.999 Failure Probability in 5 years: ~100%
UPS No Generator: Availability: 0.999 Failure Probability in 5 years: 87% With Generator: Availability: 0.999 Failure Probability in 5 years: ~50%
Economy Class
Business Class
N+1 UPS N+1 Generator Availability: 0.999999 Failure Probability in 5 years: <1%
Gulfstream
Software enabled resiliency plans for failure states and focuses on fast service restoration. More failures, but less total service downtime
Avoid hardware failure Redundant at all levels Longer MTBF More Disruption Service up 0.99999
Goal minimize service disruption Automated fault detection and response Fail often recover fast!
Redundancy Driven HA
Resiliency Driven HA
Pay only for what you use Portable apps enable cost comparison
How do failures affect the service as a whole? Create a dependency tree Determine when failure becomes disaster Detect and automate both failure and disaster responses
Manual
Slow/Error Prone
Mechanized
Faster/Predictable
Automation
Fast and Predictable
Host Location Management Physical Server Physical Server Physical Server Physical Server Management System Physical Server Physical Server Physical Server Physical Server Physical Server Physical Server
DDC Hypervisor Fabric Physical Server Physical Server Physical Server Physical Server Physical Server Physical Server Physical Server Physical Server Physical Server Physical Server Physical Server
Health State
Standardized increments Known environmental requirements Known procurement time Standardized engineering * Compute scale unit * Storage scale unit
Move away from break/fix incident model Use a pool-based maintenance model Define % of decay before maintenance
3% in decay 2% in upgrade 10% lost in fault domain 15% of total capacity lost
Takes advantage of homogeneity VMs can be relocated predictably Combines decay/fault/upgrade concepts
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