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Leveraging the Cloud for Green IT: Predicting the Energy, Cost & Performance of Cloud Computing © 2009 Optimal Innovations, Hyperformix & RS Performance.

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Presentation on theme: "Leveraging the Cloud for Green IT: Predicting the Energy, Cost & Performance of Cloud Computing © 2009 Optimal Innovations, Hyperformix & RS Performance."— Presentation transcript:

1 Leveraging the Cloud for Green IT: Predicting the Energy, Cost & Performance of Cloud Computing © 2009 Optimal Innovations, Hyperformix & RS Performance Amy Spellmann,Optimal Innovations Richard Gimarc,Hyperformix, Inc. Mark Preston,RS Performance December 9, 2009 Session# 431

2 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 1 Leveraging the Cloud for Green IT: What are we going to talk about? We will describe & demonstrate a methodology for predicting performance, energy and cost for expanding on-premise IT into the Cloud. Cut through the hype:  View Cloud as a new platform in your infrastructure  Quantify the effect of utilizing Cloud Energy Cost Performance  Utilize a repeatable, scientific methodology

3 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 2 Leveraging the Cloud for Green IT: Agenda  Business Motivation  Cloud Considerations  Methodology  Case Study  Summary & Recommendations

4 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 3 Cloud Computing: What is it?  A general term for  Delivering hosted services over the Internet  Highly automated platforms that can be assigned to applications or services on demand  Services are broadly divided into 3 categories – Infrastructure-as-a-Service (IaaS) – Platform-as-a-Service (PaaS) – Software-as-a-Service (SaaS)  Cloud services have 3 distinct characteristics that differentiate it from traditional hosting – Sold on demand, e.g., by the minute or the hour – Elastic, a user can have as much or as little of a service as they want at any given time – Fully managed by the provider

5 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 4 Cloud Computing Promises  Flexible, on-demand IT services  Low entry cost (CapEx vs OpEx)  Timely  Scalable  Greener  Elastic Follow the herd blindly into the Cloud?

6 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 5 Is Cloud a Good Fit for You?  Have Cloud solutions evolved enough to meet the demands of your growing enterprise?  Is Cloud a good fit for your business from a cost, energy and performance perspective?  Are Cloud solutions greener? The decision requires a formal evaluation to ensure that the benefits can be realized

7 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 6 Energy Consumption: Are Clouds Greener? Energy consumption in the data center is often not well understood [Koomey] Economies of scale enable Cloud providers to be more efficient – Scalability and efficiency must be balanced to meet profit margins – Energy usage can be reduced through free cooling and specialty hardware (e.g. Amazon, IBM, Google, Microsoft) Cloud vendors must be able to demonstrate their energy efficiency

8 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 7 Why Consider Cloud Computing?  IT infrastructure at capacity, need to accommodate growth  Available power to the data center at capacity  Opportunity to expand on-premise IT with on-demand resourcing  Pay-as-you go cash outlay vs. large investment up front  Government & utility energy saving incentives: rebates, carbon penalties

9 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 8 Cloud Considerations AttributeTraditional On-PremiseCloud Computing Infrastructure Scalability  Server upgrade (CapEx)  Potential data center expansion (CapEx)  Cloud resources (OpEx)  Limits data center expansion (CapEx) Deployment Timeline  Weeks to months  On-demand in minutes/hours Infrastructure Management  Physical and virtualized components  (Virtual) Cloud resources Business Continuity  Responsibility of on-premise IT staff  Partner with Cloud provider Physical Resource Utilization  Size to handle peak loads  Low utilization during non-peak time  Scale dynamically with on-demand resources  Eliminates the need to overbuild (1 of 2)

10 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 9 Cloud Considerations AttributeTraditional On-PremiseCloud Computing Network Infrastructure  Data center IT staff responsible for networks and network expansion (CapEx)  Utilize Cloud network infrastructure (OpEx)  Additional on-premise network may be required (CapEx) Performance  Response time depends on workload volumes & supporting infrastructure  Dependent on responsiveness of the virtual resources  Potential new network delay between on-premise and Cloud Energy  Driven by IT infrastructure & supporting facility  Fixed limit on power draw  Energy consumption managed by the provider  Limits on-premise energy growth IT Budget Categories  CapEx for infrastructure  OpEx for facility & energy  Primarily OpEx for infrastructure (2 of 2)

11 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 10 Cloud Consideration – Set of Attributes Addressed in Case Study Infrastructure Scalability Physical Resource Utilization Network Infrastructure Performance Energy IT Budget Categories Not Addressed in Case Study  Deployment Timeline  Infrastructure Management  Business Continuity  Security  Software Licensing  Operations Staffing  Facilities Cost  Maintenance

12 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 11 Modeling & Capacity Planning Business Growth / Demand Decision Support for Leveraging the Cloud Predictive Performance & Capacity Management On-Premise Provisioning ? Cloud Provisioning ?

13 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 12 Methodology Traditional + Cloud Analysis Traditional Capacity Planning + Cloud Analysis Infrastructure Capacity Analysis Workload & Growth Analysis IT Provisioning Plan & Cost Optimization Business Planning & Communications Energy Footprint Impact Cloud Provider Capacity On-Premise + Cloud Performance Compare On-Premise vs. Cloud Identify Cloud Services

14 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 13 Case Study Data Center & Application Growth Challenges  Web-based eCommerce application  Current server infrastructure at capacity limit  Expecting 2x workload growth over next year  Data Center reaching power limits Capacity Planning Question: Should we expand our on-premise infrastructure to handle the expected growth, or should we leverage the Cloud to support the increased workload volume? On-Premise Provisioning ? Cloud Provisioning ?

15 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 14 Case Study Approach: Compare On-Premise vs. Cloud  Determine workload growth pattern  Size on-premise infrastructure to handle growth  Select a Cloud provider  Quantify required Cloud resources  Compare & contrast energy, cost & performance Capacity Planning Question: Should we expand our on-premise infrastructure to handle the expected growth, or should we leverage the Cloud to support the increased workload volume? On-Premise Provisioning ? Cloud Provisioning ?

16 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 15 Case Study: Capacity Planning Factors to Address  What additional on-premise infrastructure would we need to support workload growth?  What Cloud resources would we utilize to support the additional workload volume?  How does the cost compare; on-premise versus Cloud?  How is our energy bill affected by growing into the Cloud?  Is there a performance penalty for augmenting our on-premise IT infrastructure with the Cloud?  Are we able to leverage the on-demand nature of Cloud resources? These factors will enable us to answer:  Should we expand our on-premise infrastructure to handle the expected growth, or should we leverage the Cloud to support the increased workload volume?

17 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 16 Case Study Workload Growth Pattern  Historical data shows workload volume by hour  Simplify analysis by assuming 25% steps  Analyze 4 load levels instead of 24  Baseline workload: hours 0-5 & 22-23  Increased workload volume from hours 6-21; add on-premise capacity or route to the Cloud?

18 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 17 Case Study Today’s Current Baseline  27 servers  Maintain 70% utilization threshold

19 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 18 Case Study Size On-Premise to Handle 2x Growth  Increase workload by 2x  25% growth steps  Upgrade DB server  Add servers to other tiers (+31)

20 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 19 Case Study Select a Cloud Provider  Amazon Web Services (AWS)  Elastic Compute Cloud (EC2) - PaaS Standard Instances  High-CPU Instances  EC2 Compute Unit has the equivalent CPU capacity of a 1.0-1.2 GHz 2007 Opteron or 2007 Xeon processor

21 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 20 Case Study Prepare to Model Cloud Resources  Develop model representations of EC2 Standard Instances

22 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 21  Number of instances required to satisfy on-demand  3 instance sizes  Maintain 70% utilization threshold  Leave DB on-premise On-Demand Provisioning Case Study Quantify Required Cloud Resources

23 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 22  On-premise servers will handle workload from hours 0-5 and 22-23  Additional workload from hours 6-21 will be routed to the Cloud Case Study Cloud Workload Routing

24 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 23  Instance pricing per hour of use  Data transfer cost based on GB quantity of in/out per month Case Study Compute Cloud Cost per Month

25 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 24 Case Study Estimate Hourly Energy Footprint  Cloud EFP constant across the day – additional work routed to the cloud  On-Premise scenario EFP follows workload volume  +31 additional servers contribute to On-Premise EFP

26 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 25 Comparisons On-Premise vs. Cloud Scenarios  Energy  Cost  Performance Cloud provides a way to cap our on-premise energy consumption Cloud requires a lower initial investment, but costs accumulate over time Cloud may increase our business service response times

27 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 26 Cumulative monthly energy cost for the Cloud solution is approximately half of the On-Premise solution (29 vs. 59 servers) $1,602 vs. $3,092 per month Hour-by-hour energy consumption The peak on-premise EFP is 50% lower using the Cloud Comparisons Energy

28 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 27  Cloud scenario: on- premise servers handle all off-peak workload  On-premise upgraded servers built out to handle peak  Small Standard are slower than on-premise counterpart  Large & XLarge are faster than corresponding on- premise servers Comparisons Average Response Time: CPU  Workload routed to Cloud for hours 6-21  Cloud response time is weighted average of requests handled on-premise and those routed to the cloud

29 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 28  Keeping DB on-premise incurs additional network overhead  On average 1.5 DB accesses per transaction (max of 5)  Hybrid architectures may increase response time beyond acceptable limits  Chatty applications are not good candidates  Assume 100 msec round trip latency  Max response time increase > 0.5 seconds Comparisons Average Response Time: CPU + Network Latency

30 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 29 On-Premise  Purchase of 31 new servers  Energy for built-out infrastructure (58 servers) Cloud  Energy cost of on- premise servers (27)  On-demand monthly cost of Cloud instances and data transfer  Slope of Cloud curve is steeper, implying higher incremental cost (OpEx)  On-Premise OpEx is $3,098 - Cloud OpEx is $7,018 Breakeven Comparisons Cost of On-Premise vs. Cloud

31 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 30 On-Premise  Upfront CapEx investment for 31 new servers  OpEx at $3,098 per month for energy Cloud  On-demand Cloud requires no initial CapEx investment  Monthly OpEx of $7,018 Some Cloud providers now support both cash outlay timing models in which an upfront CapEx expense offsets lower recurring OpEx expenses. Comparisons Cost Breakdown $83,685$84,215

32 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 31 Summary Did we meet our data center energy challenges?  Cloud enabled us to keep our on- premise energy requirements flat  Dynamic provisioning enabled us to more closely match infrastructure & demand for energy

33 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 32  Hybrid models can suffer an increase in response time due to the Cloud instance interactions with the on-premise servers  There are two factors that affect response time: speed of the Cloud instances and additional network latency  Network latency can have a significant affect on client response time Summary Did we meet our performance requirements?

34 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 33 Summary Did we meet our data center budget requirements?  Growing into the Cloud requires less up-front (CapEx) investment  Cloud recurring cost can end up exceeding traditional data center cost

35 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 34 Summary Capacity Planning Question Should we expand our on-premise infrastructure to handle the expected growth, or should we leverage the Cloud to support the increased workload volume? Grow into the Cloud Utilize the on-demand Cloud to handle the expected 2x workload growth On-Premise energy usage will be effectively capped Defers need for data center expansion Eliminates CapEx for required On-Premise infrastructure  But  Cloud monthly expenses (OpEx) are larger than On-Premise: $7,018 vs. $3,098  Cumulative cost of Cloud may overtake on-premise solution  Cloud causes increase in average transaction response time  Capacity plan developed for 12-month a planning horizon

36 Leveraging the Cloud for Green IT: Predicting the Energy, Cost and Performance of Cloud Computing 35 Recommendations A Formal Methodology is Required  Focus your analysis on the Cloud provider’s pricing model(s)  Evaluate data transfer cost & any other miscellaneous costs carefully  Don’t ignore performance  Cloud response times must meet business requirements  Manage your energy footprint by leveraging the Cloud  Utilizing Cloud resources effectively caps your on-premise energy cost & consumption  Data center power efficiency can be managed with the Cloud  Balance CapEx & OpEx based on your business requirements  Cloud allows you to shift large upfront infrastructure investments into predictable recurring monthly costs

37 Blind Adoption Can Be Expensive Mitigate Your Risk With Proven Methodology & Best Practices Leveraging the Cloud for Green IT: Predicting the Energy, Cost & Performance of Cloud Computing


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