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1Auto-Scaling to Minimize Cost and  Meet Application Deadlines in        Cloud Workflows                        SC 11     ...
Introduction2       Resource provisioning questions are not trivial           Under-provisioning → hurt performance     ...
Auto-Scaling3       Schedule-based and rule-based auto-scaling           E.g. “run 10 instances between 8AM to 6PM every...
Auto-Scaling4       Goals of auto-scaling mechanisms             Balance performance and cost                   E.g. me...
Cloud application model5                                                                                            Credit...
Problem definition6        Cloud application            app = {Si}                                                    J...
Solution7       SCS (Scaling – Consolidation - Scheduling)         Task bundling         Deadline assignment         S...
Solution – Step 18       Task bundling         Idea – force tasks run on the same instance to improve          performan...
Solution – Step 29       Deadline assignment         Idea – to break task dependencies, assign deadlines          propor...
Solution – Step 310        Determine the number of instances          From   deadline assignment, we have            Ta...
Solution – Step 511        Instance consolidation          Idea – put tasks on the same instance even if some           ...
Solution – Step 612        Scheduling – Earliest Deadline First          The dynamic scaling feature can make sure that ...
Solution – Overview13                            Parallelism   reduction
Evaluation14        Workload patterns        Application models                                                        V...
Evaluation15      SCS cost saving ranges from 6.8% to 40.4%      The performance difference is larger with longer deadli...
Evaluation – High volume V.S. Low volume16        High workload (10X ) V.S. low workload (X)          Pipeline,        1...
Evaluation – Imprecise parameters17                 Deadline(0.5hour) Non-Miss Rate for           Pipeline application, 2...
Related work18        Dynamic resource provisioning in virtualized         environment              Multi-tier web appli...
Conclusion and future work19        Conclusions            SCS cost saving ranges from 6.8% to 40.4%            SCS can...
20     Thank you!
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Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows

http://www.cs.virginia.edu/~mm5bw/papers/WorkflowAutoScaling.pdf

The presentation for SC 2011

http://dl.acm.org/citation.cfm?id=2063449

www.mingmao.org

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Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows

  1. 1. 1Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows SC 11 (Nov 16, TCC 305) Ming Mao, Marty Humphrey CS Department, University of Virginia
  2. 2. Introduction2  Resource provisioning questions are not trivial  Under-provisioning → hurt performance  Over-provisioning → pay more than necessary  How much resources?  What types of resources?  When to acquire or release?  How to use them?  A performance-resource mapping problem
  3. 3. Auto-Scaling3  Schedule-based and rule-based auto-scaling  E.g. “run 10 instances between 8AM to 6PM everyday and 2 instances all the other time.”  E.g. “add (remove) 2 instances when the average CPU utilization is above 70% (below 20%) for 5 minutes.”  Simple and convenient, works well for simple applications  What if the relationship between the performance and resources utilization indicators is complex  The resource utilization indicators are low-level and may not be expressive enough  They do not consider the user budgets well
  4. 4. Auto-Scaling4  Goals of auto-scaling mechanisms  Balance performance and cost  E.g. meet performance goals with minimum cost or maximize utilities with the limited budget  Reflect different options for computing resources  E.g. VMs have different processing power and price  Be aware of practical considerations  E.g. VM may takes several min to be ready to use  Be aware of the cloud billing model  E.g. billed by instance-hours  Support specific application performance requirements  E.g. deadlines, the number of concurrent users, communication latency
  5. 5. Cloud application model5 Credit Cloud History Third Party Evaluation Complete Model Gold (5) (8) (10) Members Authentication Loading Profile Health (2) (4) Record Advanced (6) Model Silver Entry Members Point (1) (9) Response (11) Data Base Validation Model Non- (3) (7) Member Auto-Scaling Non-Member Job Silver Member Job Gold Member Job Cloud VMs  App consists of service units  Job consists of tasks  Jobs are categorized into classes (deadline and processing flow)  Cloud offers multiple VM types (price and processing power)  App has no knowledge on the workload info in advance  VM takes time to start up (VM acquisition delay) and are billed by hours
  6. 6. Problem definition6  Cloud application  app = {Si}  Job class  J = {DAG(Si), deadline | Si ∈ app}  Cloud VM 𝑆  VMv = {[𝑗 𝐽 𝑖 ]v , cv , lagv}  Workload 𝑆𝑖  Wt = 𝑆𝑖 𝐽 𝑗𝐽  Scaling plan  Scalingt = {VMv , Nv}  Scheduling plan 𝑆  Schedulet = { 𝑗 𝐽 𝑖 →VMv}  Goal  Min(C) = Min( 𝑣 𝑐 𝑣 𝑁 𝑣)
  7. 7. Solution7  SCS (Scaling – Consolidation - Scheduling)  Task bundling  Deadline assignment  Scaling  Instance consolidation  Scheduling
  8. 8. Solution – Step 18  Task bundling  Idea – force tasks run on the same instance to improve performance and save data transfer cost  Example T6 T8 T6 T8 Bundle task as T6 Server 1 Server 2 Server 1 Server 1 Before After
  9. 9. Solution – Step 29  Deadline assignment  Idea – to break task dependencies, assign deadlines proportionally based on task running time (on their cost- efficient machines)  Example T3 T3 T7 T7 T4 T11 T4 T11 T13 T1 T2 T8 T10 T13 T1 T2 T8 T10 T5 T12 T5 T12 T9 T9 T6 T6 3:00PM 3:00 4:30 3:00 3:10 3:20 3:50 4:00 4:20 4:30 Before After  Task upgrading 𝑚𝑎𝑘𝑒𝑠𝑝𝑎𝑛 𝑏𝑒𝑓𝑜𝑟𝑒 −𝑚𝑎𝑘𝑒𝑠𝑝𝑎𝑛 𝑎𝑓𝑡𝑒𝑟 𝑟𝑎𝑛𝑘 = 𝑐𝑜𝑠𝑡 𝑎𝑓𝑡𝑒𝑟 −𝑐𝑜𝑠𝑡 𝑏𝑒𝑓𝑜𝑟𝑒
  10. 10. Solution – Step 310  Determine the number of instances  From deadline assignment, we have  Task running time – tm  Task execution interval – [T0 ,T1 ]  Load vector  LVm = [tm/( T1 – T0 )]  # of instances = [LVm]  Example T1 0 0 0.25 0 0 T2 0 0 0 0.5 0 0 0 3:00 3:15 3:45 4:00 VM1 All 0 0 0.25 0.75 0.25 0 0
  11. 11. Solution – Step 511  Instance consolidation  Idea – put tasks on the same instance even if some task may not run the most cost-efficiently on that machine  Example T11 Idle High-CPU 3:00 PM 4:00 PM Before T12 Idle 3:00 PM 4:00 PM Standard After T11 T12 Idle Standard 3:00 PM 4:00 PM
  12. 12. Solution – Step 612  Scheduling – Earliest Deadline First  The dynamic scaling feature can make sure that the tasks facing missed deadlines can be found in time 𝑡𝑖 <1 𝑖 𝑇 𝑒𝑛𝑑_𝑖 − 𝑇 𝑠𝑡𝑎𝑟𝑡_𝑖
  13. 13. Solution – Overview13  Parallelism reduction
  14. 14. Evaluation14  Workload patterns  Application models VM Type Price Micro $0.02/hour Standard $0.085/hour High-CPU $0.68/hour High-Memory $0.50/hour  Base line  Time  Task execution  VM lag  Greedy  72 hours  Randomly generated  8 min  GAIN
  15. 15. Evaluation15  SCS cost saving ranges from 6.8% to 40.4%  The performance difference is larger with longer deadlines
  16. 16. Evaluation – High volume V.S. Low volume16  High workload (10X ) V.S. low workload (X)  Pipeline, 1-hour deadline Cost ($) High Volume V.S. Low Volume 120 Greedy- High 100 GAIN- High 80 SCS-High 60 Greedy- 40 Low GAIN- 20 Low 0 SCS-Low Stable Growing Cycle OnOff
  17. 17. Evaluation – Imprecise parameters17 Deadline(0.5hour) Non-Miss Rate for  Pipeline application, 20% variance Non-miss Rate (%) Imprecise Task Execution Estimation 100.0% in estimated execution time, 0.5- 90.0% 80.0% hour deadline Greedy  SCS can finish jobs before 70.0% 60.0% 50.0% GAIN 40.0% SCS deadlines for more than 90%, 30.0% 20.0% much better than Greedy(40%) 10.0% 0.0% and GAIN(50%) Stable Growing Cycle OnOff Deadeline(1 hour) Non-Miss Rate for  Pipeline application, 20% variance Non-miss Rate(%) Imprecise Instance Acquisition Lag in the estimate VM acquisition 100.0% 90.0% time, 1-hour deadline 80.0% 70.0% Greedy  SCS beats Greedy and GAIN 60.0% 50.0% GAIN  The performance is more affected 40.0% SCS 30.0% by the VM acquisition time 20.0% 10.0% 0.0% Stable Growing Cycle OnOff
  18. 18. Related work18  Dynamic resource provisioning in virtualized environment  Multi-tier web applications, queuing theory, control theory  Workflow scheduling in Grid environment with deadline and budget constraints  Single workflow instance  Resource pool is limited  Cloud economics  Cloud provider side V.S. cloud user side  Current cloud auto-scaling mechanisms  E.g. AWS auto-scaling, RightScale, enStratus, Scalr, AzureScale project, etc.
  19. 19. Conclusion and future work19  Conclusions  SCS cost saving ranges from 6.8% to 40.4%  SCS can better handle different workload volume and imprecise parameters  Choosing proper VM types based on the workload saves cost  Instance consolidation can help save partial instance hours  VM acquisition time plays a very important role  Future work  Different scheduling approaches  Real scientific applications  Insufficient budget cases - maximize cloud user benefits/utilities under budget constraints  Data-intensive applications
  20. 20. 20 Thank you!

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