1. Scalable and Coordinated Scheduling for Cloud-Scale computing
Apollo :
Scalable and Coordinated Scheduling for Cloud-Scale computing
심윤석

2. INDEX Backgroud Goals & Challenges of Apollo Apollo Framework
Evaluation
Conclusion

3. Backgroud SCOPE DAG (Directed acyclic graph) Job Stage Task Compile
150 DOG

4. Backgroud

5. Goals & Challenges Minimize Job Latency & Maximize Cluster Utilization
Scaling
Heterogeneous workload
Maximize Resource Utilization

6. Goals & Challenges Scale Job processes had GB to PB of data
100,000 scheduling request/sec (in peak time)
Clusters contain over 20,000 servers
Clusters run up to 170,000 tasks in parallel

7. Goals & Challenges Heterogeneous workload
Short (Seconds) & Long (Hours) Execution Time
I/O bound, CPU bound
Various Resource Requirements (e.g. Memory, Cores)
Data Locality (Long Task) & Scheduling Latency (Short Task)

8. Goals & Challenges Maximize Utilization Workload Fluctuates Regularly
Especially CPU Utilization

9. Apollo Framework

10. Apollo Framework
Distributed and Coordinate Scheduler

11. Apollo Framework
Estimation
Based
Scheduling

12. Apollo Framework
Wait-Time Update

13. Apollo Framework Wait-Time Matrix For represent server load
Lightweight
Expected Wait Time
Future Resource Availability

14. Apollo Framework 𝐸=𝐼+𝑊+𝑅 𝐶= 𝑃 𝑠𝑢𝑐𝑐 𝐸+𝐾 1− 𝑃 𝑠𝑢𝑐𝑐 𝐸
Estimation-Based Scheduling
For Minimize Task Completion Time
Stable match algorithm
Task Completion Time Equation
E  Estimated Task Completion Time I  Initialization Time W  Wait Time R  Runtime
Include Server Failure Cost
C  Final Estimated Completion Time P  Success Probability K  Server Failure Panalty
𝐸=𝐼+𝑊+𝑅
𝐶= 𝑃 𝑠𝑢𝑐𝑐 𝐸+𝐾 1− 𝑃 𝑠𝑢𝑐𝑐 𝐸

15. Apollo Framework Distributed and Coordinate Scheduler
One scheduler per one job
Each scheduler make Independent Decision based on Global Status
Conflicts can be occur

16. Apollo Framework Correcting Conflicts (Correction Machanism)
Re-evaluates prior scheduling decisions
Duplicate Scheduling
Confidence
Scattering completion time
Randomization

17. Apollo framework Opportunistic Scheduling Opportunistic Task
Maximize Utilization
Random Scheduling  Fairness
Opportunistic Task
Can be preempted
Can be upgrade to regular task
Only consume idle resources
Opportunistic Task can use
if Regular Task does not exist

18. Evaluation Apollo at Sacle Scheduling Quality
Evaluating Estimates Completion Time
Correction Effectiveness
Stable matching Efficiency

19. Evaluation Apollo at Scale Run 170,000 tasks in parallel
Tracks 14,000,000 pending tasks
Well utilized in weekday (90% median CPU utilization)

20. Evaluation Scheduling Quality 80% of Recurring jobs getting faster
Significantly improved wait time
Similar performance with Oracle (No schedule latency, conflicts, failure …)

21. Evaluation
Evaluating Estimates Completion Time

22. Evaluation Correction Effectiveness Stable matching Efficiency
82% Success rate
< 0.5% Trigger rate 
Stable matching Efficiency

23. Conclusion Minimize Job Latency Maximize Cluster Utilization
Loosely Coordinated Distributed Scheduler
High Quality Scheduling
Maximize Cluster Utilization
Opportunistic Scheduling

24. reference
sessions/presentation/boutin
files/osdi14_slides_boutin.pdf