Tail Latency: Networking

1. Tail Latency: Networking

2. The story thus far • Tail latency is bad • Causes: • Resource contention with background jobs • Device failure • Uneven-split of data between tasks • Network congestion for reducers

3. Ways to address tail latency • Clone all tasks • Clone slow tasks • Copy intermediate data • Remove/replace frequently failing machines • Spread out reducers

4. What is missing from this picture? • Networking: • Spreading out reducers is not sufficient. • The network is extremely crucial • Studies on Facebook traces show that [Orchestra] • in 26% of jobs, shuffle is 50% of runtime. • in 16% of jobs, shuffle is more than 70% of runtime • 42% of tasks spend over 50% of their time writing to HDFS

5. Other implication of Network Limits Scalability Scalability of Netflix-like recommendation system is bottlenecked by communication • Did not scale beyond 60 nodes • Comm. time increased faster than comp. time decreased

6. What is the Impact of the Network • Assume 10ms deadline for tasks [DCTCP] • Simulate job completion times based on distributions of tasks completion times • For 40 about 4 tasks (14%)for 400 14 tasks [3%] fail respectively

7. What is the Impact of the Network • Assume 10ms deadline for tasks [DCTCP] • Simulate job completion times based on distributions of tasks completion times (focus on 99.9%) • For 40 about 4 tasks (14%)for 400 14 tasks [3%] fail respectively

8. What is the Impact of the Network • Assume 10ms deadline for tasks [DCTCP] • Simulate job completion times based on distributions of tasks completion times • For 40 about 4 tasks (14%)for 400 14 tasks [3%] fail respectively

9. Other implication of Network Limits Scalability Scalability of Netflix-like recommendation system is bottlenecked by communication • Did not scale beyond 60 nodes • Comm. time increased faster than comp. time decreased

10. What Causes this Variation in Network Transfer Times? • First let’s look at type of traffic in network • Background Traffic • Latency sensitive short control messages; e.g. heart beats, job status • Large files: e.g. HDFS replication, loading of new data • Map-reduce jobs • Small RPC-request/response with tight deadlines • HDFS reads or writes with tight deadlines

11. What Causes this Variation in Network Transfer Times? • No notion of priority • Latency sensitive and non-latency sensitive share the network equally. • Uneven load-balancing • ECMP doesn’t schedule flows evenly across all paths • Assume long and short are the same • Bursts of traffic • Networks have buffers which reduce loss but introduce latency (time waiting in buffer is variable) • Kernel optimization introduce burstiness

12. Ways to Eliminate Variation and Improve tail latency • Make the network faster • HULL, DeTail, DCTCP • Faster networks == smaller tail • Optimize how application use the network • Orchestra, CoFlows • Specific big-data transfer patterns, optimize the patterns to reduce transfer time • Make the network aware of deadlines • D3, PDQ • Tasks have deadlines. No point doing any work if deadline wouldn’t be met • Try and prioritize flows and schedule them based on deadline.

13. Fair-Sharing or Deadline-based sharing • Fair-share (Status-Quo) • Every one plays nice but some deadlines lines can be missed • Deadline-based • Deadlines met but may require non-trial implemantionat • Two ways to do deadline-based sharing • Earliest deadline first (PDQ) • Make BW reservations for each flow • Flow rate = flow size/flow deadline • Flow size & deadline are known apriori

14. Fair-Sharing or Deadline-based sharing • Two versions of deadline-based sharing • Earliest deadline first (PDQ) • Make BW reservations for each flow • Flow rate = flow size/flow deadline • Flow size & deadline are known apriori

15. Issues with Deadline Based Scheduling • Implications for non-deadline based jobs • Starvation? Poor completion times? • Implementation Issues • Assign deadlines to flows not packets • Reservation approach • Requires reservation for each flow • Big data flows: can be small & have small RTT • Control loop must be extremelly fast • Earliest deadline first • Requires coordination between switches & servers • Servers: specify flow deadline • Switches: priority flows and determine rate • May require complex switch mechanisms

16. How do you make the Network Faster • Throw more hardware at the problem • Fat-Tree, VL2, B-Cube, Dragonfly • Increases bandwidth (throughput) but not necessarily latency

17. So, how do you reduce latency • Trade bandwidth for latency • Buffering adds variation (unpredictability) • Eliminate network buffering & bursts • Optimize the network stack • Use link level information to detect congestion • Inform application to adapt by using a different path

18. HULL: Trading BW for Latency • Buffering introduces latency • Buffer is used to accommodate bursts • To allow congestion control to get good throughput • Removing buffers means • Lower throughput for large flows • Network can’t handle bursts • Predictable low latency

19. Why do Bursts Exists? • Systems review: • NIC (network Card) informs OS of packets via interrupt • Interrupt consume CPU • If one interrupt for each packet the CPU will be overwhelmed • Optimization: batch packets up before calling interrupt • Size of the batch is the size of the burst

20. Why do Bursts Exists? • Systems review: • NIC (network Card) informs OS of packets via interrupt • Interrupt consume CPU • If one interrupt for each packet the CPU will be overwhelmed • Optimization: batch packets up before calling interrupt • Size of the batch is the size of the burst

21. Why Does Congestion Need buffers? • Congestion Control AKA TCP • Detects bottleneck link capacity through packet loss • When loss it halves its sending rate. • Buffers help the keep the network busy • Important for when TCP reduce sending rate by half • Essentially the network must double capacity for TCP to work well. • Buffer allow for this doubling

22. TCP Review • Bandwidth-delay product rule of thumb: • A single flow needs C×RTT buffers for 100% Throughput. B ≥ C×RTT B < C×RTT B Buffer Size B 100% 100% Throughput

23. Key Idea Behind Hull • Eliminate Bursts • Add a token bucket (Pacer) into the network • Pacer must be in the network so it happens after the system optimizations that cause bursts. • Eliminate Buffering • Send congestion notification messages before link it fully utilized • Make applications believe the link is full when there’s still capacity • TCP has poor congestion control algorithm • Replace with DCTCP

24. Key Idea Behind Hull • Eliminate Bursts • Add a token bucket (Pacer) into the network • Pacer must be in the network so it happens after the system optimizations that cause bursts. • Eliminate Buffering • Send congestion notification messages before link it fully utilized • Make applications believe the link is full when there’s still capacity

25. Orchestra: Managing Data Transfers in Computer Clusters • Group all flows belonging to a stage into a transfer • Perform inter-transfer coordination • Optimize at the level of transfer rather than individual flows

26. Transfer Patterns Transfer: set of all flows transporting data between two stages of a job • Acts as a barrier Completion time: Time for the last receiver to finish HDFS Broadcast Map Map Map Shuffle Reduce Reduce Incast* HDFS

27. Orchestra Cooperative broadcast (Cornet) • Infer and utilize topology information Weighted Shuffle Scheduling (WSS) • Assign flow rates to optimize shuffle completion time Inter-Transfer Controller • Implement weighted fair sharing between transfers End-to-end performance Inter-Transfer Controller (ITC) ITC Fair sharing FIFO Priority TC (shuffle) TC (broadcast) TC (broadcast) Broadcast Transfer Controller (TC) Shuffle Transfer Controller (TC) Broadcast Transfer Controller (TC) Hadoop shuffle WSS HDFS Tree Cornet HDFS Tree Cornet shuffle broadcast 1 broadcast 2

28. Cornet: Cooperative broadcast • Broadcast same data to every receiver • Fast, scalable, adaptive to bandwidth, and resilient • Peer-to-peer mechanism optimized for cooperative environments • Use bit-torrent to distribute data

29. Topology-aware Cornet Many data center networks employ tree topologies Each rack should receive exactly one copy of broadcast • Minimize cross-rack communication Topology information reduces cross-rack data transfer • Mixture of spherical Gaussians to infer network topology

30. Status quo in Shuffle r1 r2 s1 s2 s3 s4 s5 Links to r1 and r2 are full: 3 time units Link from s3 is full: 2 time units Completion time: 5 time units

31. Weighted Shuffle Scheduling Allocate rates to each flow using weighted fair sharing, where the weight of a flow between a sender-receiver pair is proportional to the total amount of data to be sent r1 r2 1 1 2 2 1 1 s1 s2 s3 s4 s5 Completion time: 4 time units Up to 1.5X improvement

32. Faster spam classification Communication reduced from 42% to 28% of the iteration time Overall 22% reduction in iteration time

33. Summary • Discuss tail latency in network • Types of traffic in network • Implications on jobs • Cause of tail latency • Discuss Hull: • Trade Bandwidth for latency • Penalize huge flows • Eliminate bursts and buffering • Discuss Orchestra: • Optimize transfers instead of individual flows • Utilize knowledge about application semantics http://www.mosharaf.com/