Capping Server Cost and Energy Use with Hybrid Cloud Computing

Transcription

1 Capping Server Cost and Energy Use with Hybrid Cloud Computing Richard Gimarc Amy Spellmann Mark Preston CA Technologies Uptime Institute RS Performance Connecticut Computer Measurement Group Friday, June 3, 2011 Copyright 2011 CA, All rights reserved

2 Capping Server Cost and Energy Use with Hybrid Cloud Computing Questions: What happens if I grow my IT infrastructure into the Cloud? Can I control and/or limit cost by utilizing the Cloud? Will I still be able to provide satisfactory service? Can I use the Cloud to limit my energy footprint? How will the Cloud affect my budget? Cloud Provisioning? Scale Into Cloud Baseline Scale Out On-Premise Provisioning? Cut through the hype: View the Cloud as a new platform Quantify the cost of utilizing Cloud Energy - kwh Cost - CapEx & OpEx Performance - Response Time Utilize a repeatable, scientific methodology 1

3 Cloud Computing Promises Flexible, on-demand IT services Low entry cost (CapEx vs OpEx) Timely Scalable Greener Energy saving incentives, rebates, carbon penalties Elastic Extend capacity of a full data center Efficiency Follow the herd blindly into the Cloud? 2

4 Why Consider Cloud Computing? Today On-Premise Demand for IT services is increasing IT infrastructure needs to support business growth Many data centers at space & power limits Tomorrow Cloud Expand on-premise IT with on-demand resourcing Pay-as-you go vs. large up front investment Government & utility energy saving incentives, rebates, carbon penalties 3

5 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 & 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) Shifting computing from our data center to the Cloud: Reduces energy consumption in our data center (greener) Decreases overall energy consumption due Cloud s economies of scale 4

6 Cloud Considerations (1 of 2) Attribute Traditional On-Premise Cloud Computing Infrastructure Scalability Deployment Timeline Server upgrade (CapEx) Potential data center expansion (CapEx) Weeks to months Cloud resources (OpEx) Limits data center expansion (CapEx) On-demand in minutes/hours Infrastructure Physical and virtualized (Virtual) Cloud resources Management components Business Continuity Physical Resource Utilization Responsibility of on-premise IT staff Size to handle peak loads Low utilization during nonpeak time Partner with Cloud provider Scale dynamically with ondemand resources Eliminates the need to overbuild 5

7 Cloud Considerations (2 of 2) Attribute Traditional On-Premise Cloud Computing Network Infrastructure Performance Energy IT Budget Categories Data center IT staff responsible for networks and network expansion (CapEx) Response time depends on workload volumes & supporting infrastructure Driven by IT infrastructure & supporting facility Fixed limit on power draw CapEx for infrastructure OpEx for facility & energy Utilize Cloud network infrastructure (OpEx) Additional on-premise network may be required (CapEx) Dependent on responsiveness of the virtual resources Potential new network delay between on-premise and Cloud Energy consumption managed by the provider Limits on-premise energy growth Primarily OpEx for infrastructure 6

8 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 7

9 Decision Support for Leveraging the Cloud Predictive Performance & Capacity Management Business Service Analysis & Planning Predictive Capacity Planning Energy Footprint Projection On-Premise Cloud 25 EFP(hour) - On-Premise Scenario kwh Hour Database Cache Image Web Cost, Energy, Performance Comparison Instance Count EC2 Standard Instance Requirements Hour On-Premise vs. Std Small Cloud Total Cumulative Cost Comparison $180,000 $160,000 $140,000 $120,000 $100,000 $80,000 $60,000 $40,000 $20,000 $ Month Std Small Std Large Std XLarge On-Premise Std Small+Data Xfer 8

10 Disclaimer What are we doing? Showing how to include Cloud into your capacity planning Demonstrating a methodology Walking through a sample case study Using Amazon Web Services Elastic Compute Cloud as an example What are we not doing? Recommending a specific Cloud provider Providing a recommendation for choosing Cloud provider A over provider B Making any pro/con recommendations regarding AWS EC2 9

11 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: Cloud Provisioning? 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? 10

12 Case Study Methodology Workload growth Cloud resources Energy, cost & performance Workload Growth Cloud Resources Energy, Cost, Performance What on-premise infrastructure do we need to support workload growth? Model the workload growth & on-premise infrastructure What Cloud resources are required to support increased demand? Model the on-demand growth & the Cloud resources How do energy, cost & performance compare; on-premise versus Cloud? Use a model to predict & compare 11

13 Case Study Approach Workload Growth Cloud Resources Energy, Cost, Performance Pct Increase Over Baseline Daily Workload Growth Pattern 100% 75% 50% 25% % Hour Modeled Growth Estimated Growth Start with current infrastructure Scale on-premise to handle workload growth Route workload growth to the Cloud Side-by-side results comparison 12

14 Case Study Today s Current Baseline (27 servers) Quantity Tier Make/Model Relative Speed 4 Cache Dell PowerEdge GHz 20 1 Database Dell PowerEdge GHz 81 7 Image Dell PowerEdge GHz Web Dell PowerEdge GHz 20 Baseline Server Utilization 100% 90% 80% Cloud Provisioning? Baseline 70% 60% 50% 40% 30% 20% 10% 51.7% 58.0% 65.8% 73.4% 0% On-Premise Provisioning? Cache[4] Database Image[7] Web[15] 13

15 Case Study Workload Growth Pattern Workload Growth Cloud Resources Energy, Cost, Performance Historical data shows workload volume by hour Business expects 100% growth at peak hour of the day Pct Increase Over Baseline 100% 75% 50% 25% Daily Workload Growth Pattern % Cloud Provisioning? Baseline On-Premise Provisioning? Modeled Growth Hour Estimated Growth Increased workload from hours 6-21 On-premise infrastructure must be scaled to support peak load Cloud is dynamic (on-demand) 14

16 Case Study Size On-Premise to Handle Growth Workload Growth Cloud Resources Energy, Cost, Performance Increase workload by 2x 25% growth steps Upgrade DB server Add +31 other servers 58 total on-premise servers 70% utilization threshold Utilization 100% 90% 80% 70% 60% 50% 40% 30% Model Results: Results: On-Premise On-Premise IT IT Infrastructure Cloud Provisioning? Baseline Scale Out On-Premise Provisioning? 20% 10% 0% Increase number of Image & Web servers Add Cache servers Upgrade Database Baseline +25% +50% +75% +100% Cache Database Image Web Quantity Change Tier Make/Model Relative Speed 8 +4 Cache Dell PowerEdge GHz Database Dell PowerEdge R GHz Image Dell PowerEdge GHz Web Dell PowerEdge GHz 20 15

17 Case Study Evaluate Cloud Services Workload Growth Cloud Resources Energy, Cost, Performance Standard Instance EC2 Compute Units Small 1 1 Number of Virtual Cores Description 1.7 GB of memory, 1 Amazon EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit), 160 GB of instance storage, 32-bit platform Large GB of memory, 4 Amazon EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each), 850 GB of instance storage, 64-bit platform 15 GB of memory, 8 Amazon EC2 Compute Units (4 virtual Extra Large 8 4 cores with 2 EC2 Compute Units each), 1690 GB of instance storage, 64-bit platform Cloud Provisioning? Scale Into Cloud Baseline On-Premise Provisioning? Amazon Web Services (AWS) Elastic Compute Cloud (EC2) Standard Instances EC2 Compute Unit has the equivalent CPU capacity of a GHz 2007 Opteron or 2007 Xeon processor 16

18 Case Study Prepare to Model Cloud Resources Workload Growth Cloud Resources Energy, Cost, Performance Standard Instance EC2 Compute Units Small 1 1 Number of Virtual Cores Description 1.7 GB of memory, 1 Amazon EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit), 160 GB of instance storage, 32-bit platform Large GB of memory, 4 Amazon EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each), 850 GB of instance storage, 64-bit platform 15 GB of memory, 8 Amazon EC2 Compute Units (4 virtual Extra Large 8 4 cores with 2 EC2 Compute Units each), 1690 GB of instance storage, 64-bit platform Cloud Provisioning? Scale Into Cloud Baseline On-Premise Provisioning? Develop model representations of EC2 Standard Instances Standard Instance EC2 Compute Units Number of Processors Relative Speed Small Large Extra Large

19 Case Study Quantify Required Cloud Resources Workload Growth Cloud Resources Energy, Cost, Performance Cloud Provisioning? Number of instances required to satisfy ondemand Large & XLarge instances Maintain 70% utilization threshold Leave DB on-premise Scale Into Cloud Baseline On-Premise Provisioning? Instance Count EC2 Standard Instance Requirements +25% +50% +75% +100% Std Large On-Demand Provisioning Hour Pct Increase Over Baseline 100% 75% 50% 25% % Std XLarge Daily Workload Growth Pattern Modeled Growth Hour Estimated Growth 18

20 Case Study Large Cloud Resources for +100% Workload Growth Cloud Resources Energy, Cost, Performance Consider 100% growth Route growth to the Cloud Estimate number of required Large Cloud instances 73% 66% 52% 26% 26% Web[15] Image[7] Cache[4] Database Baseline Workload +100% Cloud Provisioning? Scale Into Cloud Baseline All workload uses the upgraded database On-Premise servers are at baseline levels Repeat for all workload levels & instance sizes Web[11] Image[5] Cache[2] 68% 62% 70% On-Premise Provisioning? 19

21 Case Study Cloud Workload Routing Workload Growth Cloud Resources Energy, Cost, Performance Cloud Provisioning? On-premise servers will handle workload from hours 0-5 and Additional workload from hours 6-21 will be routed to the Cloud Scale Into Cloud Baseline On-Premise Provisioning? Instance Count EC2 Standard Instance Requirements Std Large Hour Std XLarge Standard Total Instance Hours Instance per Day Large 163 Extra Large 95 20

22 Case Study On-Premise Energy Footprint (EFP) Workload Growth Cloud Resources Energy, Cost, Performance kwh EFP(hour) - Cloud Scenario Hour Database Cache Image Web On-Premise EFP using Cloud stays constant across the day additional work routed to the cloud On-Premise scenario EFP follows workload volume +31 additional servers increase On-Premise energy use 25 EFP(hour) - On-Premise Scenario Scale Into Cloud 263 kwh/day 20 Cloud Provisioning? Baseline Scale Out kwh Hour On-Premise Provisioning? Database Cache Image Web 509 kwh/day 21

23 Case Study Estimated EFP in the Cloud Workload Growth Cloud Resources Energy, Cost, Performance EFP per Hour: Cloud Scenario EFP per Day: On-Premise vs. Cloud Scenario kw per Hour 10 5 kw per Day kwh 15 kwh Hour kwh 0 Cloud Provisioning? Scale Into Cloud Baseline Scale Out On-Premise Provisioning? Estimated daily Cloud EFP is 15 kwh On-demand characteristics of Cloud drastically reduces overall EFP Average 7 Large instances per hour Assumes 4 Large instances per physical server 22

24 Case Study Compute Cloud Cost per Month Workload Growth Cloud Resources Energy, Cost, Performance Cloud Provisioning? Instance pricing per hour of use Data transfer cost based on GB quantity of in/out per month Scale Into Cloud Baseline On-Premise Provisioning? Standard Instance Total Number of Hourly Instances per Day Windows Usage per Hour Total Cost per Month Large 163 $0.48 $2,380 Extra Large 95 $0.96 $2,774 Fee Category Price per GB GB per Month Total Cost per Month Transfer IN $ $89 Transfer OUT First 10 TB/Month $ ,000 $1,700 Next 40 TB/Month $0.13 6,816 $818 Next 100 TB/Month $ Over 150 TB/Month $ TOTAL 17,701 $2,607 Standard Instance Instance Cost per Month Data Transfer Cost per Month Total Cost per Month Large $2,380 $2,607 $4,987 Extra Large $2,774 $2,607 $5,381 23

25 Comparisons On-Premise vs. Cloud Scenarios Energy Cloud provides a way to cap our on-premise energy consumption Cost Cloud requires a lower initial investment, but costs accumulate over time Scale Into Cloud Performance Cloud Provisioning? Baseline Scale Out Cloud may increase our business service response times On-Premise Provisioning? 24

26 Comparisons On-Premise Energy Only On-Premise vs. Std Large Cloud On-Premise EFP per Hour Workload Growth Cloud Resources Energy, Cost, Performance Hour-by-hour energy consumption The peak on-premise EFP is 50% lower using the Cloud kwh Hour $80,000 On-Premise vs. Std Large Cloud Cumulative On-Premise Energy Cost On-Premise Scenario Std Large Cloud Scenario $70,000 $60,000 On-Premise Scenario energy cost is twice that for the on-premise portion of the Cloud Scenario: 58 vs. 27 on-premise servers $50,000 $40,000 $30,000 $20,000 $10,000 $ Month $3,098 vs. $1,602 per month On-Premise Scenario Std Large Cloud Scenario 25

27 Comparisons Total Cost On-Premise vs. Cloud Workload Growth Cloud Resources Energy, Cost, Performance On-Premise Scenario CapEx for 31 new servers Energy cost for builtout infrastructure (58 servers) Cloud Scenario Energy cost for onpremise servers (27) On-demand monthly cost of Cloud instances & data transfer (includes Cloud vendor energy cost) $180,000 $160,000 $140,000 $120,000 $100,000 $80,000 $60,000 $40,000 $20,000 $- On-Premise vs. Std Large Cloud Total Cumulative Cost Comparison Breakeven Month On-Premise Std Large+Data Xfer Slope of Cloud curve is steeper, implying higher incremental cost (OpEx) On-Premise OpEx is $3,098 - Cloud OpEx is $6,589 26

28 Comparisons Average Response Time Workload Growth Cloud Resources Energy, Cost, Performance Keeping DB onpremise incurs additional network overhead On average 1.5 DB accesses per transaction (max of 5) Hybrid architectures Response Time (msec) may increase response 40 time beyond 30 acceptable limits 20 Chatty applications are 10 not good candidates 0 Daily Workload Growth Pattern Modeled Average Response Time Hour Pct Increase Over Baseline 100% 75% 50% 25% % Hour On-Premise Std Large Assume 100 msec round trip latency Max response time increase > 0.5 seconds Modeled Growth Estimated Growth 27

29 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? Energy Cost Cloud provides a way to cap our on-premise energy consumption Cloud requires a lower initial investment, but costs accumulate over time Performance Cloud may increase our business service response times 28

30 Summary Did we meet our data center energy challenges? Cloud enabled us to keep our on-premise energy requirements flat Workload growth routed to the cloud Dynamic provisioning enabled us to more closely match infrastructure & demand for energy kwh Instance Count On-Premise vs. Std Large Cloud On-Premise EFP per Hour Hour On-Premise Scenario Std Large Cloud Scenario EC2 Standard Instance Requirements Hour Std Small Std Large Std XLarge 29

31 Summary Did we meet our performance requirements? 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 Response Time (msec) Modeled Average Response Time Hour On-Premise Std Large Network latency can have a significant affect on client response time 30

32 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 Estimated OpEx per month $3,098 - On-Premise $6,589 - Cloud $180,000 $160,000 $140,000 $120,000 $100,000 $80,000 $60,000 $40,000 $20,000 $- On-Premise vs. Std Large Cloud Total Cumulative Cost Comparison Month On-Premise Std Large+Data Xfer 31

33 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 But 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 Cloud monthly expenses (OpEx) are larger than On-Premise: $6,589 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 32

34 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 33

35 Capping Server Cost and Energy Use with Hybrid Cloud Computing Based on the CMG 2009 Best Paper 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 Amy Spellmann Richard Gimarc Mark Preston