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Cloud Economics in Training and Simulation

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This document discusses a presentation on cloud economics in training and simulation. It begins with defining cloud computing and outlining its essential characteristics like on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Some postulated use cases for cloud computing are then discussed, including training and education. Real-world data is then presented from a course that utilized Amazon Web Services, analyzing costs, cost drivers, and server usage. The findings provide insights into the economics of educational cloud usage.

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Nane Kratzke CLOUD ECONOMICS IN TRAINING AND SIMULATION Prof. Dr. rer. nat. Nane Kratzke 1 Computer Science and Business Information Systems
The next 25 minutes are about ... •  What is cloud computing? •  (Economical) characteristics of cloud computing •  Postulated use cases for cloud computing •  Some data from real world •  Decision making is not always obvious => How to decide? •  Findings Prof. Dr. rer. nat. Nane Kratzke 2 Computer Science and Business Information Systems
What is a cloud computing (definition) „Cloud computing is a model for enabling ubiquitous, convenient, on- demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.“ National Institute of Standards and Technology, NIST: „The NIST definition of cloud computing“; Peter Mell, Timothy Grance, 2011 http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf Prof. Dr. rer. nat. Nane Kratzke 3 Computer Science and Business Information Systems
Essential Characteristics of Clouds •  No human •  Remote access via •  Resources are interaction thin or fat client pooled to serve necessary platforms multiple consumers •  Programmable data •  No physical access •  Little control or center knowledge over exact location On-demand Network Resource self-service access pooling m able •  Rapid provisioning am Pay-per-use oare r •  Resourcemodel •  Autoscaling g business r •  P r eusage entmonitored, •  Ressources Da t C can be and virtually unlimited a controlled, reported Rapid Measured elasticity Service Prof. Dr. rer. nat. Nane Kratzke 4 Praktische Informatik und betriebliche Informationssysteme
Business Characteristics Fixed costs become Pay as you go variable Cost are associative Business gains •  100 servers for one hour flexibility •  1 server for 100 hours •  no long-term financial •  Same price commitment to resources Prof. Dr. rer. nat. Nane Kratzke 5 Praktische Informatik und betriebliche Informationssysteme
Economical Cloud Usage Patterns have to do with peak loads „In other words, even if cloud services cost, say, twice as much, a pure cloud solution makes sense for those demand curves where the peak-to- average ratio is two-to-one or higher.“ Weinman, Mathematical Proof of the Inevitability of Cloud Computing, 2011 http://www.joeweinman.com/Resources/Joe_Weinman_Inevitability_Of_Cloud.pdf Peak loads are cloud economics best friends Prof. Dr. rer. nat. Nane Kratzke 6 Praktische Informatik und betriebliche Informationssysteme
Postulated use cases These use cases (among others) are postulated to be cloud compatible: data storage, support software short-term system disaster recovery hosting websites development cycles demonstrations and business continuity overflow processing Training and media processing or large-scale simulation education and rendering scientific data processing •  Research shows that cost advantages of cloud computing are deeply use case specific •  Be aware of comparing non comparable use cases •  This contribution presents some data of educational use cases (similar usage characteristics of simulational use cases) Prof. Dr. rer. nat. Nane Kratzke 7 Computer Science and Business Information Systems
Analyzed use case •  Web technology lecture/practical course for computer science students (bachelor) in summer 2011 and summer/winter 2012. •  Projects: Development of web information systems (Drupal based) •  All groups were assigned cloud service accounts provided by Amazon Web Services (AWS). •  Analysis of billing as well as usage data provided by AWS. Prof. Dr. rer. nat. Nane Kratzke 8 Computer Science and Business Information Systems
(A) Costs per Month (aligned to Weeks) 500 Cost analysis 400 Costs in USD 300 200 Total costs: 846.99 $ 100 Total students: 49 Cost per student: 17.28 $ 0 CW 13 CW 14 – CW 17 CW 18 – CW 21 CW 22 – CW 25 Calendar Weeks (CW) (B) Main Cost Drivers instancehour (62%) Main identified cost drivers: (1)  Server uptime (2/3) datatransfer (0%) adressing (3%) (2)  Data storage (1/3) datastorage (34%) Prof. Dr. rer. nat. Nane Kratzke 9 Computer Science and Business Information Systems
Server Usage Analysis (A) Maximum and Average Box Usage Training 50 Average Box Usage Maximum Box Usage in an hour 40 Used Server Boxes 30 Project 24x7 Migration 20 10 0 13 14 15 16 17 18 19 20 21 22 23 24 25 Calendar Week Prof. Dr. rer. nat. Nane Kratzke 10 Computer Science and Business Information Systems (B)
0 13 14 15 16 17 18 19 20 21 22 23 24 25 Average to Peak Ratio per Week Calendar Week (C) Average Box to Maximum Box Ratio according to Weinman 1.0 Cloud computing is economical not reasonable Avg to Max Box Usage Ratio 0.8 Cloud computing 0.6 might be reasonable 0.4 Cloud computing is 0.2 economical reasonable 0.0 14 16 18 20 22 24 Calendar Week Prof. Dr. rer. nat. Nane Kratzke 11 Computer Science and Business Information Systems
Economical Decision Analysis A four step process to decide for or against cloud based solutions (A) Determine your atp Maximum and Average Box Usage 50 ratio Average Box Usage Maximum Box Usage in an hour 40 Used Server Boxes 30 Determine your 20 dedicated costs 10 0 13 14 15 16 17 18 19 20 21 22 23 24 25 Calendar Week Determine your (B) maximal cloud costs Max instances: 49 2000 Accumulated Processing Hours per Week Processing hours: 7612 1500 Processing Hours Determine appropriate Average: 7612 / (26 * 7 * 24) = 1.74 1000 cloud ressources Overall atp ratio: 1.74 / 49 = 0.035 500 Prof. Dr. rer. nat. Nane Kratzke 12 0 Computer Science and Business Information Systems 13 14 15 16 17 18 19 20 21 22 23 24 25
Economical Decision Analysis A four step process to decide for or against cloud based solutions Determine your atp „In other words, even if cloud services cost, ratio say, twice as much, a pure cloud solution makes sense for those demand curves where the peak-to-average ratio is two-to-one or higher.“ Determine your dedicated costs Weinman, Mathematical Proof of the Inevitability of Cloud Computing, 2011 Determine your Example Server: 500 US Dollar maximal cloud costs Amortization: 3 years 500$ Determine appropriate d3years (500$) = = 0.019 $ h cloud ressources 3 • 365 • 24h Prof. Dr. rer. nat. Nane Kratzke 13 Computer Science and Business Information Systems
Economical Decision Analysis A four step process to decide for or against cloud based solutions According to Weinman the peak-to-average Determine your atp ratio ratio should be greater than the ratio between the variable costs c and your (assumed) dedicated costs d: Determine your dedicated costs Determine your maximal cloud costs Determine appropriate 0.019 $ h $ cloud ressources c Max = = 0.54 0.035 h Prof. Dr. rer. nat. Nane Kratzke 14 Computer Science and Business Information Systems
Economical Decision Analysis A four step process to decide for or against cloud based solutions Determine your atp 0.019 $ h $ ratio c Max = ≈ 0.54 0.035 h Pricings for EU region, 19th March, 2012 Determine your € Example: Amazon Web Services EC2- dedicated costs Determine your maximal cloud costs Determine appropriate cloud ressources Prof. Dr. rer. nat. Nane Kratzke 15 Computer Science and Business Information Systems
Economical Decision Analysis A four step process to decide for or against cloud based virtual labs The measured ATP ratio of 0.035 means in fact a 1/0.035 == 28.57 times cost advantage. This means for the presented use case: A cloud based solution provides a more than 25 times cost advantage. Compared to necessary investment efforts for a classical dedicated system implementation. Prof. Dr. rer. nat. Nane Kratzke 16 Computer Science and Business Information Systems
Why this big cost advantage? (A) How to dimensionize the data center? Maximum and Average Box Usage peak load 50 Average Box Usage And the delta? Maximum Box Usage in an hour 40 Used Server Boxes 30 Measures the overdimension of a data center 20 average 10 load 0 13 14 15 16 17 18 19 20 21 22 23 24 25 Calendar Week What is the need? Prof. Dr. rer. nat. Nane Kratzke 17 Computer Science and Business Information Systems (B)
In other words ... (A) Maximum and Average Box Usage You have to finance a really big house ... 50 Average Box Usage Maximum Box Usage in an hour 40 ... knowing Used Server Boxes that you will inhabit 30 only some rooms of it. 20 10 0 13 14 15 16 17 18 19 20 21 22 23 24 25 Calendar Week Prof. Dr. rer. nat. Nane Kratzke 18 Computer Science and Business Information Systems (B)
Findings •  Cloud computing loves peak load scenarios (be happy) •  25 times cost advantage (analyzed use case) •  Cloud generated costs are use case specific (be carefull) •  Decision making must not be obvious •  Four step decision making model (to determine your ATP ratio) •  Main cost drivers are (try to minimize) •  Server uptime •  Data storage (server volumes) •  Data transfer (in communication intensive use cases) •  Uneconomical use cases (try to avoid) •  24x7 and •  constant loads •  So if you have to deal with peak load scenerios it is likely that cloud based solutions might be an economical option ... Prof. Dr. rer. nat. Nane Kratzke 19 Computer Science and Business Information Systems
Thank you for listening Find this presentation here: http://www.slideshare.net/i21aneka/itis-ws-2013 Slideshare: i21aneka XING: Nane_Kratzke LinkedIn: nanekratzke Prof. Dr. Nane Kratzke Computer Science and WEB: Business Information Systems http://praktische-informatik.fh-luebeck.de Lübeck University of Applied Sciences Mönkhofer Weg 239 Mail: Twitter: 23562 Lübeck nane.kratzke@fh-luebeck.de @nanekratzke Germany Prof. Dr. rer. nat. Nane Kratzke 20 Computer Science and Business Information Systems

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Cloud Economics in Training and Simulation

  • 1. Nane Kratzke CLOUD ECONOMICS IN TRAINING AND SIMULATION Prof. Dr. rer. nat. Nane Kratzke 1 Computer Science and Business Information Systems
  • 2. The next 25 minutes are about ... •  What is cloud computing? •  (Economical) characteristics of cloud computing •  Postulated use cases for cloud computing •  Some data from real world •  Decision making is not always obvious => How to decide? •  Findings Prof. Dr. rer. nat. Nane Kratzke 2 Computer Science and Business Information Systems
  • 3. What is a cloud computing (definition) „Cloud computing is a model for enabling ubiquitous, convenient, on- demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.“ National Institute of Standards and Technology, NIST: „The NIST definition of cloud computing“; Peter Mell, Timothy Grance, 2011 http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf Prof. Dr. rer. nat. Nane Kratzke 3 Computer Science and Business Information Systems
  • 4. Essential Characteristics of Clouds •  No human •  Remote access via •  Resources are interaction thin or fat client pooled to serve necessary platforms multiple consumers •  Programmable data •  No physical access •  Little control or center knowledge over exact location On-demand Network Resource self-service access pooling m able •  Rapid provisioning am Pay-per-use oare r •  Resourcemodel •  Autoscaling g business r •  P r eusage entmonitored, •  Ressources Da t C can be and virtually unlimited a controlled, reported Rapid Measured elasticity Service Prof. Dr. rer. nat. Nane Kratzke 4 Praktische Informatik und betriebliche Informationssysteme
  • 5. Business Characteristics Fixed costs become Pay as you go variable Cost are associative Business gains •  100 servers for one hour flexibility •  1 server for 100 hours •  no long-term financial •  Same price commitment to resources Prof. Dr. rer. nat. Nane Kratzke 5 Praktische Informatik und betriebliche Informationssysteme
  • 6. Economical Cloud Usage Patterns have to do with peak loads „In other words, even if cloud services cost, say, twice as much, a pure cloud solution makes sense for those demand curves where the peak-to- average ratio is two-to-one or higher.“ Weinman, Mathematical Proof of the Inevitability of Cloud Computing, 2011 http://www.joeweinman.com/Resources/Joe_Weinman_Inevitability_Of_Cloud.pdf Peak loads are cloud economics best friends Prof. Dr. rer. nat. Nane Kratzke 6 Praktische Informatik und betriebliche Informationssysteme
  • 7. Postulated use cases These use cases (among others) are postulated to be cloud compatible: data storage, support software short-term system disaster recovery hosting websites development cycles demonstrations and business continuity overflow processing Training and media processing or large-scale simulation education and rendering scientific data processing •  Research shows that cost advantages of cloud computing are deeply use case specific •  Be aware of comparing non comparable use cases •  This contribution presents some data of educational use cases (similar usage characteristics of simulational use cases) Prof. Dr. rer. nat. Nane Kratzke 7 Computer Science and Business Information Systems
  • 8. Analyzed use case •  Web technology lecture/practical course for computer science students (bachelor) in summer 2011 and summer/winter 2012. •  Projects: Development of web information systems (Drupal based) •  All groups were assigned cloud service accounts provided by Amazon Web Services (AWS). •  Analysis of billing as well as usage data provided by AWS. Prof. Dr. rer. nat. Nane Kratzke 8 Computer Science and Business Information Systems
  • 9. (A) Costs per Month (aligned to Weeks) 500 Cost analysis 400 Costs in USD 300 200 Total costs: 846.99 $ 100 Total students: 49 Cost per student: 17.28 $ 0 CW 13 CW 14 – CW 17 CW 18 – CW 21 CW 22 – CW 25 Calendar Weeks (CW) (B) Main Cost Drivers instancehour (62%) Main identified cost drivers: (1)  Server uptime (2/3) datatransfer (0%) adressing (3%) (2)  Data storage (1/3) datastorage (34%) Prof. Dr. rer. nat. Nane Kratzke 9 Computer Science and Business Information Systems
  • 10. Server Usage Analysis (A) Maximum and Average Box Usage Training 50 Average Box Usage Maximum Box Usage in an hour 40 Used Server Boxes 30 Project 24x7 Migration 20 10 0 13 14 15 16 17 18 19 20 21 22 23 24 25 Calendar Week Prof. Dr. rer. nat. Nane Kratzke 10 Computer Science and Business Information Systems (B)
  • 11. 0 13 14 15 16 17 18 19 20 21 22 23 24 25 Average to Peak Ratio per Week Calendar Week (C) Average Box to Maximum Box Ratio according to Weinman 1.0 Cloud computing is economical not reasonable Avg to Max Box Usage Ratio 0.8 Cloud computing 0.6 might be reasonable 0.4 Cloud computing is 0.2 economical reasonable 0.0 14 16 18 20 22 24 Calendar Week Prof. Dr. rer. nat. Nane Kratzke 11 Computer Science and Business Information Systems
  • 12. Economical Decision Analysis A four step process to decide for or against cloud based solutions (A) Determine your atp Maximum and Average Box Usage 50 ratio Average Box Usage Maximum Box Usage in an hour 40 Used Server Boxes 30 Determine your 20 dedicated costs 10 0 13 14 15 16 17 18 19 20 21 22 23 24 25 Calendar Week Determine your (B) maximal cloud costs Max instances: 49 2000 Accumulated Processing Hours per Week Processing hours: 7612 1500 Processing Hours Determine appropriate Average: 7612 / (26 * 7 * 24) = 1.74 1000 cloud ressources Overall atp ratio: 1.74 / 49 = 0.035 500 Prof. Dr. rer. nat. Nane Kratzke 12 0 Computer Science and Business Information Systems 13 14 15 16 17 18 19 20 21 22 23 24 25
  • 13. Economical Decision Analysis A four step process to decide for or against cloud based solutions Determine your atp „In other words, even if cloud services cost, ratio say, twice as much, a pure cloud solution makes sense for those demand curves where the peak-to-average ratio is two-to-one or higher.“ Determine your dedicated costs Weinman, Mathematical Proof of the Inevitability of Cloud Computing, 2011 Determine your Example Server: 500 US Dollar maximal cloud costs Amortization: 3 years 500$ Determine appropriate d3years (500$) = = 0.019 $ h cloud ressources 3 • 365 • 24h Prof. Dr. rer. nat. Nane Kratzke 13 Computer Science and Business Information Systems
  • 14. Economical Decision Analysis A four step process to decide for or against cloud based solutions According to Weinman the peak-to-average Determine your atp ratio ratio should be greater than the ratio between the variable costs c and your (assumed) dedicated costs d: Determine your dedicated costs Determine your maximal cloud costs Determine appropriate 0.019 $ h $ cloud ressources c Max = = 0.54 0.035 h Prof. Dr. rer. nat. Nane Kratzke 14 Computer Science and Business Information Systems
  • 15. Economical Decision Analysis A four step process to decide for or against cloud based solutions Determine your atp 0.019 $ h $ ratio c Max = ≈ 0.54 0.035 h Pricings for EU region, 19th March, 2012 Determine your € Example: Amazon Web Services EC2- dedicated costs Determine your maximal cloud costs Determine appropriate cloud ressources Prof. Dr. rer. nat. Nane Kratzke 15 Computer Science and Business Information Systems
  • 16. Economical Decision Analysis A four step process to decide for or against cloud based virtual labs The measured ATP ratio of 0.035 means in fact a 1/0.035 == 28.57 times cost advantage. This means for the presented use case: A cloud based solution provides a more than 25 times cost advantage. Compared to necessary investment efforts for a classical dedicated system implementation. Prof. Dr. rer. nat. Nane Kratzke 16 Computer Science and Business Information Systems
  • 17. Why this big cost advantage? (A) How to dimensionize the data center? Maximum and Average Box Usage peak load 50 Average Box Usage And the delta? Maximum Box Usage in an hour 40 Used Server Boxes 30 Measures the overdimension of a data center 20 average 10 load 0 13 14 15 16 17 18 19 20 21 22 23 24 25 Calendar Week What is the need? Prof. Dr. rer. nat. Nane Kratzke 17 Computer Science and Business Information Systems (B)
  • 18. In other words ... (A) Maximum and Average Box Usage You have to finance a really big house ... 50 Average Box Usage Maximum Box Usage in an hour 40 ... knowing Used Server Boxes that you will inhabit 30 only some rooms of it. 20 10 0 13 14 15 16 17 18 19 20 21 22 23 24 25 Calendar Week Prof. Dr. rer. nat. Nane Kratzke 18 Computer Science and Business Information Systems (B)
  • 19. Findings •  Cloud computing loves peak load scenarios (be happy) •  25 times cost advantage (analyzed use case) •  Cloud generated costs are use case specific (be carefull) •  Decision making must not be obvious •  Four step decision making model (to determine your ATP ratio) •  Main cost drivers are (try to minimize) •  Server uptime •  Data storage (server volumes) •  Data transfer (in communication intensive use cases) •  Uneconomical use cases (try to avoid) •  24x7 and •  constant loads •  So if you have to deal with peak load scenerios it is likely that cloud based solutions might be an economical option ... Prof. Dr. rer. nat. Nane Kratzke 19 Computer Science and Business Information Systems
  • 20. Thank you for listening Find this presentation here: http://www.slideshare.net/i21aneka/itis-ws-2013 Slideshare: i21aneka XING: Nane_Kratzke LinkedIn: nanekratzke Prof. Dr. Nane Kratzke Computer Science and WEB: Business Information Systems http://praktische-informatik.fh-luebeck.de Lübeck University of Applied Sciences Mönkhofer Weg 239 Mail: Twitter: 23562 Lübeck nane.kratzke@fh-luebeck.de @nanekratzke Germany Prof. Dr. rer. nat. Nane Kratzke 20 Computer Science and Business Information Systems