![References
• [1] Q. Xie, A. Yekkehkhany, Y. Lu. Scheduling with Multi-level Data
Locality: Throughput and Heavy-traffic Optimality. In Proceedings of
INFOCOM. IEEE, 2016.
• [2] Q. Xie, and Y. Lu. Priotrity Algorithm for Near-data Scheduling:
Throughput and Heavy-traffic Optimality. In Proceedings of INFOCOM.
IEEE, 2015.
• [3] W. Wang, K. Zhu, L. Ying, J. Tan, and L. Zhang. Map Task Schedul-
ing in MapReduce with Data Locality: Throughput and Heavy-traffic
Optimality. In Proceedings of INFOCOM. IEEE, 2013.
• [4] J. M. Harrison. Heavy traffic analysis of a system with parallel servers:
Asymptotic optimality of discrete review policies. Annals of Applied
Probability, 1998.
• [5] J. M. Harrison and M. J. L´opez. Heavy traffic resource pooling in
parallel-server systems. Queueing Syst. Theory Appl., 33(4), Apr. 1999.
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Scheduling for cloud systems with multi level data locality
- 1. Scheduling for Cloud Systems with Multi-level Data Locality: Throughput and Heavy-traffic Optimality Ali Yekkehkhany In collaboration with Qiaomin Xie, and Professor Yi Lu University of Illinois at Urbana-Champain (UIUC) 1
- 2. Data Processing • Previously, storage and computing were separate Computing StorageNetwork 2
- 3. Data-Intensive Processing Explosion of data sets by industry and research Computing StorageNetwork Bottleneck 3
- 4. Data Centers • Use separate smaller centers for storage • Move computing to data Bottleneck 4
- 5. Data Centers Rack Rack Top of Rack Switch Core Switch 5
- 6. Data-parallel Processing A A BC D TA TB C Rack 1 Rack 2 local rack-local remote 6
- 7. Data-parallel Processing A A BC D TB C Rack 1 Rack 2 TA 7
- 8. Convention A task type is defined by the locations of its data block Task Types Servers 2,5,6 λ2,5,6 1 4,7,8 λ4,7,8 3,4,9 λ3,4,9 2 3 n 7,8,9 λ7,8,9 λi,j,k i,j,k O(n3 ) unknown 8
- 9. Local, Rack-local, and Remote Service 9 1 2 3 4 5 6 7 8 9 10 Rack 1 Rack 2 Task (1, 3, 4)
- 10. Question 10 1 2 3 4 5 6 7 8 9 10 Rack 1 Rack 2 A new task arrives , and scheduling? What queue should the task be routed to? What algorithm to use for routing Idle To which queue should the server give service when it becomes idle?
- 11. Metrics of Optimality for the Algorithm Throughput Optimality: Stabilizing any arrival rate vector within capacity region. Delay Optimality in Heavy-traffic: Asymptotically minimizing the average delay as the arrival rate vector approaches the boundary of the capacity region. 11
- 12. Previous Work for Two Levels of Data Locality 1- Fluid model Planning, Harrison (98), Harrison-Lopez (99), Bell-Williams (05). 12 Task Types Servers 2,5,6 λ2,5,6 1 4,7,8 λ4,7,8 3,4,9 λ3,4,9 2 3 n 7,8,9 λ7,8,9 λi,j,k i,j,k O(n3 ) unknown
- 13. Previous Work for Two Levels of Data Locality 1- Fluid Model Planning: 1.1 Throughput optimal 1.2 Heavy-traffic optimal But NOT practical! 13
- 14. Previous Work for Two Levels of Data Locality 2- Join the Shortest Queue-Maxweight (JSQ-MW) Wang et al. (13). 2.1 Throughput optimal 2.2 Not heavy-traffic optimal in all loads 2.3 Heavy-traffic optimal in SPECIFIC loads 14
- 15. Previous Work for Two Levels of Data Locality 3- Priority Algorithm for Near Data Scheduling (Pandas), Q. Xie, Y. Lu (15) 3.1 Throughput optimal 3.2 Heavy-traffic optimal for all loads 15
- 16. Three Levels of Data Locality 1. Fluid Model planning 1. Throughput optimal 2. Heavy-traffic optimal 3. NOT practical! 2. Extension of JSQ-MaxWeight 1. Throughput optimal 2. NOT heavy-traffic optimal for all loads 3. Pandas 1. Not throughput optimal 2. Not heavy-traffic optimal 16
- 17. Extension of JSQ-MW for Three Levels of Locality 17 1,2,3 Joining the Shortest One
- 18. Extension of JSQ-MW for Three Levels of Locality • Extension of JSQ-MaxWeight for systems with rack structure, Xie et al. (16): – Throughput optimal. – Not heavy-traffic optimal in all loads. Just heavy traffic optimal in specific loads. 18
- 19. Our Throughput and Heavy-traffic Optimal Algorithm • The routing and scheduling for our algorithm is as follows: – Routing: Weighted Workload – Scheduling: Priority Scheduling for Local, Rack- local, and Remote tasks queued in the 3 queues associated to each server. 19
- 20. Weighted-Workload Routing 20 Rack 1 Rack 2 1 2 43 l k r l k rl k r l k r
- 21. Weighted-Workload Routing 21 1 2 Rack 1 Rack 2 43 l - local k - rack-local r - remote workload W 1 W 2 W 3 W 4 l k r l k rl k r l k r
- 22. Weighted-Workload Routing 22 1 2 Rack 1 Rack 2 43 W 1 W 2 W 3 W 4 local rack-local remote l k r l k rl k r l k r
- 23. Weighted-Workload Routing 23 1 2 Rack 1 Rack 2 43 W 1 W 2 W 3 W 4 local rack-local remote l k r l k rl k r l k r
- 24. Weighted-Workload Routing 24 1 2 43 W 1 W 2 W 3 W 4 < << l k r l k rl k r l k r Rack 1 Rack 2
- 25. Priority Scheduling 25 1 2 Rack 1 Rack 2 43 Each server serves in the order of l k r l k rl k r l k r local,
- 26. Priority Scheduling 26 1 2 Rack 1 Rack 2 43 Each server serves in the order of l k r l k rl k r l k r local, rack-local, remote
- 27. Weighted Workload Algorithm The Weighted Workload (WW) algorithm proposed by Xie et al. (16) is proved to be both throughput optimal and heavy traffic optimal in all loads. 27
- 28. Evaluation 28
- 29. Comparing the Stability Regions 29
- 30. Heavy-traffic Optimality in Special Load 30
- 31. Heavy-traffic optimality of WW 31
- 32. References • [1] Q. Xie, A. Yekkehkhany, Y. Lu. Scheduling with Multi-level Data Locality: Throughput and Heavy-traffic Optimality. In Proceedings of INFOCOM. IEEE, 2016. • [2] Q. Xie, and Y. Lu. Priotrity Algorithm for Near-data Scheduling: Throughput and Heavy-traffic Optimality. In Proceedings of INFOCOM. IEEE, 2015. • [3] W. Wang, K. Zhu, L. Ying, J. Tan, and L. Zhang. Map Task Schedul- ing in MapReduce with Data Locality: Throughput and Heavy-traffic Optimality. In Proceedings of INFOCOM. IEEE, 2013. • [4] J. M. Harrison. Heavy traffic analysis of a system with parallel servers: Asymptotic optimality of discrete review policies. Annals of Applied Probability, 1998. • [5] J. M. Harrison and M. J. L´opez. Heavy traffic resource pooling in parallel-server systems. Queueing Syst. Theory Appl., 33(4), Apr. 1999. 32
- 33. Future Work • Scheduling for multi-level data locality instead of three levels of data locality. 33
- 34. Thanks for Your Attention 34