Intelligent System Supervision - Cloud Computing

Intelligent System Supervision - Cloud Computing

1. Cloud computing: is a model for enabling ubiquitous convenient, on-demand network access to a shared pool of configurable computing resources, that can be rapidly provisioned and released with minimal management effort or service provide interaction.

   • Ubiquitous / network access o Not only the Internet  On-demand self-service  „Resource pooling” o „Multi-tenant model”: the same resource pool is used to serve many tenants o Dynamic allocation of resources & services to tenants o Tenant control of that: rather/very/extremely limited 
   •   Elastic scale-out and scale-in o Seemingly infinite amount of resources, o that can be rented (and released) at any time    Metered/utility-like services o Some notion of „service usage” metric („how much service is used”) is introduced o Payment is at least partially based on „actual usage” • N.B. modulo time granularity (hour, day, month...) 

2. SaaS

   •  Capability: using the applications of the provider o Access: usually thin client (browser!) o In all ernesty, not a new concept  Examples o Google Apps o Salesforce CRM o LotusLive o Microsoft Business Productivity Online Suite (BPOS)  Hugely successful in some domains: collaboration, accounting, CRM, ERP, HRM, CM, PM, ...

3. PaaS

   •   Capability: deploying a custom application into a „rented” runtime environment (usually app. server container) o Predefined platform services o Predefined languages and APIs o The environment can be configured to some extent o The application model may be constrained • E.g. „no direct access to the file system”, „no direct access to OS networking”, ...  Google AppEngine  Microsoft Windows Azure Platform  Amazon Beanstalk 

4. IaaS

   •  Capability: access to fundamental computational resources o Most fundamental manifestation: „renting virtual machines” o You run what you like on the OS you get o Resources are usually either logical or virtualized o Meaning no direct access to the hypervisor & HW level  Amazon Elastic Compute Cloud (EC2) o Xen-based virtualization o Full ecosystem o Note: as a rule, general purpose cloud service

5. Amazon Web Services

   • Amazon Elastic Compute Cloud - EC2
     • For building something bigger: e.g. AWS CloudFormation. Amazon Elastic Compute Cloud is a central part of Amazon.com's cloud computing platform, Amazon Web Services. EC2 allows users to rent virtual computers on which to run their own computer applications.    

6. Gartner IaaS MQ 2014

7. When do I have to / should I / should I not / must I not use clouds?

   • When don't use
     •   Constant load  Legacy systems  Security, legal obligations and regulatory compliance   
   • Demand and Capacity
   • Deployment models
     • Private: for a single organization with internal tenants. May be locally managed and owned by the organization.           Community: a cross-organizational community shares the cloud resources (can be closed) o Gorvernment, healthcare, finance, education, ...             Public: open (!= free (as in beer)) access. Operator owns the service infrastructure  Hybrid: composition of two or more clouds with data and application portability 
   • Cost optimization
     •  There are costs involved with... o Demand left unserved o Overcapacity  The unit price unit price of a cloud can be higher than investing in your own infrastructure, but...  ... the same goes for hiring a car or a hotel room!  Hybrid clouds: private – base load, public: varying o Nontrivial optimization problem o Still have to guess predict future demand 

8. Demand and Capacity

   • Scaling resources
     • "Scale out” o Parallelizability? o "webscale” technologies o Clustering and replication o (Mongo DB Is Web Scale) 
     • Capacity planning
       •   Too low capacity: unserved demand o E.g. planning for average load   Too high capacity: not economical (?) o E.g. planning for maximum load   Physical („in-house”) capacity is usually slow and costly to increase...o ... and decrease!   (One-time) investment and commitment   Demand? 
       • ...surges that can’t be predicted   The cloud is scalable, let's use it!    For larger businesses with existing internal data centers, well-managed virtualized infrastructure and efficient IT operations teams, IaaS for steady-state workloads is often no less expensive, and may be more expensive, than an internal private cloud.” 

9. Cloud deployment models

10. Free time / free speedup

    • Paralellizable loads
      • More and more embarrassingly parallel problems and „scale-out” applications  NYT TimesMachine [12]: public domain archive o Conversion for the web [13]: Hadoop, few hundred VMs, 36 hours  Due to usage based pricing costs approx. the one VM  In a sense, „speedup for free” 

11. IaaS performance

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