Capacity Management in a Cloud Computing World


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  • Capacity Management in a Cloud Computing World

    1. 1. David S. Linthicum Twitter: @DavidLinthicumCapacity Management in a Cloud Computing World
    3. 3. FYI• Slides will be on on by tonight. – Slides are open source• You can reach me with further questions at
    4. 4. 4 Myths of Capacity Management and Cloud Computing• Myth 1: I don’t need capacity management when leveraging cloud computing.• Myth 2: Clouds are “elastic.”• Myth 3: Costs are always lower.• Myth 4: Architecture and planning less important.
    5. 5. The Big Picture
    6. 6. Datacenters Emerge1940 1950 Rise of Timesharing 1960 Distributed 1970 Computing Grids 1980 1990 Rise of the PC 2000 Rise of Client/Server Rise of the Web 2010 Rise of “The Cloud”
    7. 7. Hardware/Software/Infrastructure On-Demand2010 2012 IT On-Demand Rise of “Big Data” 2014 Rise ofRise of “IT In-a-Box”“Home 2016Clouds” Distributed Rise of Service Sharing “Commodity 2018 Data Services” The “Big 2020 Migration” Begins Rise of Shared Enterprise Business 2022 Services 2024
    8. 8. Where we are now with cloud computing.
    9. 9. Reflecting on the Hype!• Gartner - Cloud computing revenue will soar faster than expected and will exceed $150 billion within five years.• Forrester - Cloud-Based Email Is Often Cheaper Than On-Premise Email• Vivek Kundra, CTO of Obama Government: “Growing adoption of cloud computing could improve data sharing and promote collaboration among federal, state and local governments.” E.g:• Merrill Lynch: “By 2011 the volume of cloud computing market opportunity would amount to $160bn, including $95bn in business and productivity apps (email, office, CRM, etc.) and $65bn in online advertising.”• IDC: “Spending on IT cloud services will triple in the next 5 years, reaching $42 billion and capturing 25% of IT spending growth in 2012.” 9 Sources: and
    10. 10. Latest News
    11. 11. Emerging cloud computing trends.
    12. 12. • Moving from talking cloud to doing cloud.• Government entering the cloud computing game now.• Security continues to be a priority.• Little or no expertise in corporate and government IT.• Moving to IaaS and then PaaS.• Leading with private clouds, but public clouds are the destination.• Rise of “Big Data.”
    13. 13. Why the focus on the data?
    14. 14. Big Data Trends• Data aggregation in the cloud for common analytics within verticals.• Combining enterprise data into common data sets.• Critical BI.
    15. 15. Where we are now with cloud computing.
    16. 16. NIST defines cloud computing as a set of characteristics, deliverymodels, and deployment models 5 Characteristics On-demand self-service Ubiquitous network access 3 Delivery Models  Software as a Service (SaaS) Resource pooling  Platform as a Service (PaaS) 4 Deployment Models Rapid elasticity  Private Cloud  Infrastructure as a Service Pay per use (IaaS)  Community Cloud  Public Cloud  Hybrid Cloud
    17. 17. Delivery Models Morphing• Software as a Service (SaaS) – Applications as a Service – Utilities as a Service – Connected and Disconnected• Platform as a Service (PaaS) – Design as a Service – Process as a Service – Testing as a Service• Infrastructure as a Service (IaaS) – Database as a Service – Management as a Service – Middleware as a Service – Integration as a Service – Information as a Service …and more.
    18. 18. New Stack Emerging Testing-as-a-Service Management/Governance-as-a-Service Integration-as-a-Service Application-as-a-ServiceSecurity-as-a-Service Process-as-a-Service Platform-as-a-Service Information-as-a-Service Database-as-a-Service Storage-as-a-Service Infrastructure-as-a-Service
    19. 19. • Buzzword “cloud computing” is absorbed into computing.• Focus on fit and function, and not the hype.• Security moves to “centralized trust” models.• Centralized data becomes a key strategic advantage.• Mobile devices become more powerful, but thin.• The rise of the “composite cloud.”
    20. 20. Cloud computing and capacity management.
    21. 21. 4 Myths of Capacity Management and Cloud Computing• Myth 1: I don’t need capacity management when leveraging cloud computing.• Myth 2: Clouds are “elastic.”• Myth 3: Costs are always lower.• Myth 4: Architecture and planning less important.
    22. 22. No Surprise
    23. 23. So, What’s Changed?• We can no longer assume that computing capacity is dedicated to a group of users or a group of processes. – Everything in a cloud computing environment is shared using some sort of multitenant model. – This makes capacity modeling and planning much more complex.• Auto provisioning makes some aspects of capacity planning not as important since capacity can be allocated when needed. – However, considering that cost is a core driver for leveraging cloud computing, using capacity that’s not needed reduces the value of cloud computing.• We now have the option to leverage cloud computing systems as needed to cost effectively provide temporary capacity. – Called “cloud bursting,” this type of architecture was difficult to cost justify until cloud computing provided us with a cheaper “public” option.
    24. 24. So, What’s the Same?• What has not changed in the world of cloud computing is that it’s still computing. – Many in the emerging cloud computing space have a tendency to define cloud computing as the “new disruptive model” that will change the way we do computing from now on.• While many would argue that cloud computing does not require as much planning as traditional systems, including capacity modeling and management, the more enterprises leverage clouds, the opposite is proving to be true. – Indeed, the core value of cloud computing is the effective and efficient use of resources.
    25. 25. CM/Cloud computing best practices.
    26. 26. Best Practice One• Model capacity should consider the characteristics of a multi-tenant platform. – We’ve been here before with traditional multi- user, but the emerging cloud-based systems are a bit different animal. – Clouds typically offer up services or APIs to access very fine-grained and primitive resources (e.g., storage). – APIs call back to physical resources, typically virtualized servers that many other tenants share.
    27. 27. Best Practice Two• Make sure to account for distribution. – Cloud providers typically don’t centralize your processing in a single physical data center unless you specify that in the agreement (at an additional fee). – Thus, your request for 100 server instances to support processing may mean that some virtualized servers are allocated in a primary center, but dozens of others could be allocated to remote data centers, some perhaps out of the country.
    28. 28. Best Practice Three• Focus on understanding, modeling, and monitoring services, not systems. – Most cloud computing implementations leverage core patterns of SOA, including the decomposition and use of services to create and recreate solutions. – Thus, when creating a capacity plan where cloud computing systems are in play, the most productive approach is to focus on the services (APIs to the resources), and how they behave under dynamic loading versus modeling a system holistically.
    29. 29. Centralized Monitoring Systems Become More Important
    30. 30. • Focus on the reorganization and outplacement of data.• Focus on PaaS, and service companies that are good at PaaS.• Focus on centralized trust, including moving to identity management models.• SOA patterns and technology find new value in the cloud.• Continued focus on mobile computing.• Home clouds (e.g., iCloud) create a new track of application and appliance development.• Rise of the “cloud aggregator.”
    31. 31. Q&A