siphon expediting inter datacenter coflows in wide area
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Siphon: Expediting Inter-Datacenter Coflows in Wide-Area Data Analytics Shuhao Liu, Li Chen , Baochun Li University of Toronto July 12, 2018 What is a Coflow ? One stage in a data analytic job Map 1 Reduce 1 Map 2 Reduce 2 Map 3 Reduce 3


  1. Siphon: Expediting Inter-Datacenter Coflows in Wide-Area Data Analytics Shuhao Liu, Li Chen , Baochun Li University of Toronto July 12, 2018

  2. What is a Coflow ? One stage in a data analytic job Map 1 Reduce 1 Map 2 Reduce 2 Map 3 Reduce 3 Map 4 Reduce 4

  3. What is a Coflow ? One stage in a data analytic job Map 1 Reduce 1 Map 2 Reduce 2 Map 3 Reduce 3 Map 4 Reduce 4 Map tasks

  4. What is a Coflow ? One stage in a data analytic job Map 1 Reduce 1 Map 2 Reduce 2 Map 3 Reduce 3 Map 4 Reduce 4 Map tasks Reduce tasks

  5. What is a Coflow ? One stage in a data analytic job Map 1 Reduce 1 Map 2 Reduce 2 Map 3 Reduce 3 Map 4 Reduce 4 all-to-all shuffle

  6. What is a Coflow ? One stage in a data analytic job Map 1 Reduce 1 Map 2 Reduce 2 Map 3 Reduce 3 Map 4 Reduce 4 Coflow: considered done only when all flows finish

  7. Coflow Scheduling Objective: minimizing average coflow completion time Network model: datacenter networking Big switch abstraction network core is congestion-free Job 2 Job 1

  8. Coflow Scheduling Objective: minimizing average coflow completion time Network model: datacenter networking Big switch abstraction network core is congestion-free Job 2 Job 1

  9. Coflow Scheduling Objective: minimizing average coflow completion time Network model: datacenter networking Big switch abstraction network core is congestion-free Job 2 Job 1

  10. Coflow Scheduling Objective: minimizing average coflow completion time Network model: datacenter networking Big switch abstraction network core is congestion-free Job 2 Job 1

  11. Coflow Scheduling Objective: minimizing average coflow completion time Network model: datacenter networking Big switch abstraction network core is congestion-free Job 2 Job 1

  12. Coflow Scheduling Objective: minimizing average coflow completion time 1 1 2 2 Non-blocking 3 3 Switch 3 2 1 Coflow 2 Job 2 Coflow 1 Job 1

  13. Coflow Scheduling Objective: minimizing average coflow completion time 1 1 2 2 3 1 2 2 Non-blocking 3 3 3 Switch 3 2 1 Coflow 2 Job 2 Coflow 1 Job 1

  14. Coflow Scheduling Objective: minimizing average coflow completion time 1 1 3 2 2 3 2 2 1 2 2 Non-blocking 3 1 3 3 3 Switch 3 2 1 Coflow 2 Job 2 Coflow 1 Job 1

  15. Wide-Area Data Analytics

  16. Wide-Area Data Analytics Map 1 Map 2 Map 3 Map 4 Data 1 Reduce 1 Reduce 2 Datacenter 1 Wide Area Network Data 2 Data 3 Data 4 Datacenter 3 Datacenter 2 Datacenter 4

  17. Wide-Area Data Analytics Map 1 Data 1 Datacenter 1 Wide Area Network Reduce 1 Reduce 2 Map 2 Map 3 Map 4 Data 2 Data 3 Data 4 Datacenter 2 Datacenter 3 Datacenter 4

  18. With tasks placed in different datacenter, what about their generated inter-datacenter coflows ?

  19. Challenges Dumb bell network model: inter-datacenter links are the only bottleneck Inter-datacenter link Datacenter A Datacenter B

  20. Challenges Constantly changing available bandwidth CA-EU US-EU 55 68.75 82.5 96.25 110 Measured Bandwidth (Mbps) in a 100s interval

  21. Can existing heuristics work? Link 1 Link 2 8 7 4 5 6 0 1 2 3 Estimated Flow Completion Time

  22. Coflow scheduling should consider the distribution of available bandwidth.

  23. Monte Carlo Simulation

  24. Monte Carlo Simulation Scheduling Decision Tree

  25. Monte Carlo Simulation Scheduling Decision Tree A [0/0] B [0/0] C [0/0]

  26. Monte Carlo Simulation Scheduling Decision Tree A [0/0] B [0/0] C [0/0] B C A C A B

  27. Monte Carlo Simulation Scheduling Decision Tree A [0/0] B [0/0] C [0/0] B C A C A B 9.3 15.5 16.2 12.5 20.2 13.1

  28. Monte Carlo Simulation Scheduling Decision Tree A [0/0] B [0/0] C [0/0] B C A C A B 9.3 15.5 16.2 12.5 20.2 13.1

  29. Monte Carlo Simulation Scheduling Decision Tree A [1/1] A [0/0] B [0/1] B [0/0] C [0/1] C [0/0] B C A C A B 9.3 15.5 16.2 12.5 20.2 13.1

  30. Monte Carlo Simulation Scheduling Decision Tree A [29/100] B [68/100] C [3/100] B C A C A B

  31. Monte Carlo Simulation Scheduling Decision Tree A [29/100] B [68/100] C [3/100] B C A C A B Complexity? 100 * O(n!)

  32. Reduced Simulation Complexity

  33. Reduced Simulation Complexity Θ ( t × n d ) Bounded Search Depth

  34. Reduced Simulation Complexity Θ ( t × n d ) Bounded Search Depth Reduced Search Breath (Early termination)

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  36. <latexit sha1_base64="SRO9rJSliFs1sIUx4d32DxWNFw=">AB/XicjVDLSgNBEOz1GeMrPm5eBoMQL2FXBD0GvXgzgnlAsobZ2UkyZHZ2mekV4hL8FS8eFPHqf3jzb5w8DioKFjQUVd1U0EihUHX/XDm5hcWl5ZzK/nVtfWNzcLWdt3EqWa8xmIZ62ZADZdC8RoKlLyZaE6jQPJGMDgf+41bro2I1TUOE+5HtKdEVzCKVuoUdi9JiSBpo4i4IeomC0fksFMoemV3AvI3KcIM1U7hvR3GLI24QiapMS3PTdDPqEbBJB/l26nhCWUD2uMtSxW1YX42+X5EDqwSkm6s7SgkE/XrRUYjY4ZRYDcjin3z0xuLv3mtFLunfiZUkiJXbBrUTSXBmIyrIKHQnKEcWkKZFvZXwvpU4a2sPz/SqgflT237F0dFytnszpysAf7UAIPTqACF1CFGjC4gwd4gmfn3nl0XpzX6eqcM7vZgW9w3j4BRU6T0A=</latexit> <latexit sha1_base64="SRO9rJSliFs1sIUx4d32DxWNFw=">AB/XicjVDLSgNBEOz1GeMrPm5eBoMQL2FXBD0GvXgzgnlAsobZ2UkyZHZ2mekV4hL8FS8eFPHqf3jzb5w8DioKFjQUVd1U0EihUHX/XDm5hcWl5ZzK/nVtfWNzcLWdt3EqWa8xmIZ62ZADZdC8RoKlLyZaE6jQPJGMDgf+41bro2I1TUOE+5HtKdEVzCKVuoUdi9JiSBpo4i4IeomC0fksFMoemV3AvI3KcIM1U7hvR3GLI24QiapMS3PTdDPqEbBJB/l26nhCWUD2uMtSxW1YX42+X5EDqwSkm6s7SgkE/XrRUYjY4ZRYDcjin3z0xuLv3mtFLunfiZUkiJXbBrUTSXBmIyrIKHQnKEcWkKZFvZXwvpU4a2sPz/SqgflT237F0dFytnszpysAf7UAIPTqACF1CFGjC4gwd4gmfn3nl0XpzX6eqcM7vZgW9w3j4BRU6T0A=</latexit> <latexit sha1_base64="SRO9rJSliFs1sIUx4d32DxWNFw=">AB/XicjVDLSgNBEOz1GeMrPm5eBoMQL2FXBD0GvXgzgnlAsobZ2UkyZHZ2mekV4hL8FS8eFPHqf3jzb5w8DioKFjQUVd1U0EihUHX/XDm5hcWl5ZzK/nVtfWNzcLWdt3EqWa8xmIZ62ZADZdC8RoKlLyZaE6jQPJGMDgf+41bro2I1TUOE+5HtKdEVzCKVuoUdi9JiSBpo4i4IeomC0fksFMoemV3AvI3KcIM1U7hvR3GLI24QiapMS3PTdDPqEbBJB/l26nhCWUD2uMtSxW1YX42+X5EDqwSkm6s7SgkE/XrRUYjY4ZRYDcjin3z0xuLv3mtFLunfiZUkiJXbBrUTSXBmIyrIKHQnKEcWkKZFvZXwvpU4a2sPz/SqgflT237F0dFytnszpysAf7UAIPTqACF1CFGjC4gwd4gmfn3nl0XpzX6eqcM7vZgW9w3j4BRU6T0A=</latexit> <latexit sha1_base64="SRO9rJSliFs1sIUx4d32DxWNFw=">AB/XicjVDLSgNBEOz1GeMrPm5eBoMQL2FXBD0GvXgzgnlAsobZ2UkyZHZ2mekV4hL8FS8eFPHqf3jzb5w8DioKFjQUVd1U0EihUHX/XDm5hcWl5ZzK/nVtfWNzcLWdt3EqWa8xmIZ62ZADZdC8RoKlLyZaE6jQPJGMDgf+41bro2I1TUOE+5HtKdEVzCKVuoUdi9JiSBpo4i4IeomC0fksFMoemV3AvI3KcIM1U7hvR3GLI24QiapMS3PTdDPqEbBJB/l26nhCWUD2uMtSxW1YX42+X5EDqwSkm6s7SgkE/XrRUYjY4ZRYDcjin3z0xuLv3mtFLunfiZUkiJXbBrUTSXBmIyrIKHQnKEcWkKZFvZXwvpU4a2sPz/SqgflT237F0dFytnszpysAf7UAIPTqACF1CFGjC4gwd4gmfn3nl0XpzX6eqcM7vZgW9w3j4BRU6T0A=</latexit> Reduced Simulation Complexity Θ ( t × n d ) Bounded Search Depth Reduced Search Breath O ( t × n d ) (Early termination) Online Incremental Search

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