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Ming Mao, Marty HumphreyCS Department, UVaScaling and Scheduling to MaximizeApplication Performance within BudgetConstrain...
2 Dynamic scalability and cost saving are two of the most important factors whenconsidering cloud adoptionTwo major benef...
3 Dynamic scalability – the ability to acquire/release resources in response todemand dynamically Dynamic scalability ch...
4 Problem - What resources should be acquired/released in the cloud,and how should the computing activities be mapped to ...
5 An application consists of service components. A workflow goes through differentservice components and therefore consis...
6Minimize job turnaround time within budget constraintsProblem formulation Problem terminology Cloud application app = ...
7 Scheduling-first Idea – allocate application budget to individual jobs based on prioritiesand schedule tasks within jo...
8 Scheduling-first Step 1 – Distribute budget: 𝐵𝑗 = 𝐵 × 𝑝𝑗/ 𝑝𝑗𝑗Minimize job turnaround time within budget constraintsSol...
9Minimize job turnaround time within budget constraintsSolution: scaling-first Scaling-first Idea – determine the comput...
10Minimize job turnaround time within budget constraintsSolution: scaling-first Scaling-first Step 1 – determine the VMs...
11 Instance consolidation Schedule tasks on different VM types to save partial instance hour cost Budget allocation sch...
 Workload patterns Application models12 Time 72 hours Task execution Randomly generated VM lag 5 minMinimize job t...
13Minimize job turnaround time within budget constraintsEvaluation – job turnaround time above – weighted average job tur...
14Minimize job turnaround time within budget constraintsEvaluation – sensitivity to inaccurate parameters left – scheduli...
15Minimize job turnaround time within budget constraintsEvaluation – instance consolidation left – job turnaround time / ...
16 Conclusions choose appropriate VM types based on the workload. Scheduling-first and scaling-first are trade-offs bet...
17Thanks!
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Scaling and scheduling to maximize application performance within budget constraints

Scaling and scheduling to maximize application performance within budget constraints in cloud workflows

IPDPS 2013

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Scaling and scheduling to maximize application performance within budget constraints

  1. 1. Ming Mao, Marty HumphreyCS Department, UVaScaling and Scheduling to MaximizeApplication Performance within BudgetConstraints in Cloud WorkflowsIPDPS 2013(May 21st 2013)1
  2. 2. 2 Dynamic scalability and cost saving are two of the most important factors whenconsidering cloud adoptionTwo major benefits- dynamic scalability and costA survey from 39 major technology companies [1] Cloud benefits On-demand self-services Broad network access Resource pooling Rapid elasticity Measured services Cheaper maintenance ……Why do you move into the cloud?
  3. 3. 3 Dynamic scalability – the ability to acquire/release resources in response todemand dynamically Dynamic scalability challenge → It relies on the users to tell the size of resourcepool Over-provisioning → cost more than necessary, offset cloud advantages Under-provisioning → hurt application performance, cannot meet service level agreements andlose application customersCloud dynamic scalabilityover-provisioning under-provisioning
  4. 4. 4 Problem - What resources should be acquired/released in the cloud,and how should the computing activities be mapped to the cloudresources, so that the application performance can be maximizedwithin the budget constrains? In this paper, we discuss limited budget case The unlimited budget case was discussed in our SC 11 paper Solution - This paper argues that an automatic resourceprovisioning and allocation mechanism, i.e., an auto-scalingsolution – is the key to successful cloud adoption. Essentially, anauto-scaling solution needs to answer the following two questions: Capacity determination (or resource provisioning) what types of resources, how much and for how long Job scheduling (or resource allocation) map computing activities onto the cloud resourcesProblem statement
  5. 5. 5 An application consists of service components. A workflow goes through differentservice components and therefore consists of multiple connected tasks Workload is a stream of workflow jobs not known in advance Task precedence constraints need to be preserved Jobs have individual prioritiesService oriented architecture (SOA) & workflow jobs
  6. 6. 6Minimize job turnaround time within budget constraintsProblem formulation Problem terminology Cloud application app = {Si} Job class J = {DAG(Si), priorityJ| Si ∈ app} Cloud VM VMv = {[𝐽 𝑆 𝑖]v , cv , lagv} Workload Wt = 𝑗𝑜𝑏𝐽𝑆 𝑖𝑗𝑜𝑏𝐽𝑆 𝑖 Scaling plan Scalingt = {VMv → Nv} Scheduling plan Schedulet = { 𝑗𝐽𝑆 𝑖→VMv} Goal Min( 𝑗𝑜𝑏𝑡𝑢𝑟𝑛𝑎𝑟𝑜𝑢𝑛𝑑 × 𝑝𝑟𝑖𝑜𝑟𝑖𝑡𝑦/𝑗𝑜𝑏 𝑝𝑟𝑖𝑜𝑟𝑖𝑡𝑦𝑗𝑜𝑏 )&& Cost(app) <= B (budget, dollars/hour) Target - The service provider has a limited budget andaims to maximize the application performance. Solution idea – a monitor-control loop thatmakes scaling and scheduling decisions basedon updated workload and VM information
  7. 7. 7 Scheduling-first Idea – allocate application budget to individual jobs based on prioritiesand schedule tasks within job budget Step 1 – Distribute budget: 𝐵𝑗 = 𝐵 × 𝑝𝑗/ 𝑝𝑗𝑗 Step 2 – Schedule tasks for each job, schedule as many tasks as possible on their fast machines Step 3 – Consolidate budget return job budget to the application the application uses the remaining budget collected from individual jobs to schedulehigh priority tasks Step 4 – Acquire instance acquire instances and execute tasks based on the determined schedule plansMinimize job turnaround time within budget constraintsSolution: scheduling-first
  8. 8. 8 Scheduling-first Step 1 – Distribute budget: 𝐵𝑗 = 𝐵 × 𝑝𝑗/ 𝑝𝑗𝑗Minimize job turnaround time within budget constraintsSolution: scheduling-first Step 2 – Schedule taskse.g. Budget(B) = $1/h;Large(L) = $0.5/h;Medium(M) = $0.3/h;Small(S) = $0.1/h; Step 1: job1 and job2 havethe same priority,job1 → $0.5/h, job2 → $0.5/h Step 2:job1(T1) → $0.5(L);job2(T5) → $0.5(L); Step 3:job1(T2+T3) → $0.5(S+M);job2(T6) → $0.5(L);job1 returns $0.1 to system;job2(T7) → $0.1(S); Step 4acquire instances whennecessary Step 3 – Consolidate budget Step 4 – Acquire instance
  9. 9. 9Minimize job turnaround time within budget constraintsSolution: scaling-first Scaling-first Idea – determine the computing capacity by looking at the overallworkload and schedule tasks based on priority Step 1 – determine the VMs assume tasks run on their fastest machines and calculate the cost Cfast for the nexthour acquire VMs proportionally based on Budget/Cfast Step 2 – consolidate budget use the remaining the budget to purchase new machines. Step 3 – schedule tasks schedule tasks based on task priority
  10. 10. 10Minimize job turnaround time within budget constraintsSolution: scaling-first Scaling-first Step 1 – determine the VMs Step 2 – consolidate budget Step 3 – schedule tasks Step 1: assume tasks run on fastestmachines and calculate Cfast andacquire VMs proportionally based onB/Cfast, Step 2: the remaining $0.5 can be used topurchase 1 L machine Step 3: tasks are scheduledbased on their priorities
  11. 11. 11 Instance consolidation Schedule tasks on different VM types to save partial instance hour cost Budget allocation schemes Evenly distributed – e.g. daily x/365, hourly x/8760 Based on workload – e.g. high on busy times, low on non-busy times Workload prediction – $/hour → $/jobMinimize job turnaround time within budget constraintsOther considerations
  12. 12.  Workload patterns Application models12 Time 72 hours Task execution Randomly generated VM lag 5 minMinimize job turnaround time within budget constraintsEvaluation – experiment setup Baseline StandardVM Type PriceMicro $0.02/hourStandard $0.080/hourHigh-CPU $0.66/hourHigh-Memory $0.45/hourExtra-Large $1.3/hour
  13. 13. 13Minimize job turnaround time within budget constraintsEvaluation – job turnaround time above – weighted average job turnaround time for the hybrid application and cycleworkload pattern Scheduling-first and scaling-first can save 9.8%- 45.2% cost compared to the standardmachine choice. Scaling-first works better under small budget ranges while scheduling-first works betterunder large budget ranges.
  14. 14. 14Minimize job turnaround time within budget constraintsEvaluation – sensitivity to inaccurate parameters left – scheduling-first’s sensitivity to inaccurate parameters (Hybrid application + Cycleworkload pattern) right – scaling-first’s sensitivity to inaccurate parameters (Hybrid application + Cycle workloadpattern) When the estimation error is within ±20%, the job turnaround time shows -10.2% – 16.7%difference. When the task estimation error reaches ±60%, the performance of both algorithms showssignificant degradation (more than ±25% difference)
  15. 15. 15Minimize job turnaround time within budget constraintsEvaluation – instance consolidation left – job turnaround time / resource utilization of scheduling-first’s instance consolidation(Hybrid application + Cycle workload pattern) right – job turnaround time / resource utilization of scaling-first’s instance consolidation(Hybrid application + Cycle workload pattern) When budget is low or high, the improvement is small. When the budget is in between, theimprovement is more significant (e.g. utilization rate improves 2.2% to 19.9% when the budgetis between $15/hour and $25/hour). Scaling-first benefits more from instance consolidation process than scheduling-first
  16. 16. 16 Conclusions choose appropriate VM types based on the workload. Scheduling-first and scaling-first are trade-offs between the task execution time andwaiting time. As long as the VM performance can be correctly ranked, the proposed mechanisms havegood tolerance to inaccurate parameters. Instance consolidation is an efficient strategy to save partial instance hours and improveresource utilization. Future work Other billing models – reserved instances, spot instances, $/min Maximize application performance within budget constraints for data-intensiveapplications Hybrid and federate cloud environments Develop evaluation benchmarks and simulation platformsConclusion and future work
  17. 17. 17Thanks!

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