What are Percentiles? ● A percentile (or a centile) is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations falls. For example, the 20th percentile is the value (or score) below which 20% of the observations may be found. Equivalently, 80% of the observations are found above the 20th percentile. -- Wikipedia ● Percentile Latency - The latency number below which a certain percentage of requests fall. A 95%tile of 100ms means 95% of the request are being served under 100ms.
What is Tail Latency? ● Last 0.X% of the request latency distribution graph. ● In general we can take slowest 1% response times or the 99%ile response times as the tail latency of that request. ○ May vary depending on the scale of the system ■ InMobi - 8B Ad Requests/day (99.5 %tile) ■ Capillary - 500M Requests/day (99% tile) ● Responsive/Interactive Systems ○ Better User Experience, Fluidity -- Higher Engagement ○ 100ms -- Large Information Retrieval Systems ○ Reads out number the writes!
Why should we care about Tail Latency? ● Tail latency becomes important as the scale of our system increases. ○ 10K requests/day → 100M requests/day ○ Slowest 1% queries - 100 requests → 1M requests ● Just as fault-tolerant computing aims to create a reliable whole out of less-reliable parts, large online services need to create a predictably responsive whole out of less-predictable parts; we refer to such systems as “latency tail-tolerant,” or simply “tail-tolerant.” ● Many Tail Tolerant Methods can leverage infra provided for Fault Tolerance - Better Utilisation
Why does such Variability Exists? ● Software Variability ○ Shared Resources ■ Co-hosted applications ● Containers can provide isolation. ● Within app-contention still exists. ○ Daemons ■ Background or batch jobs running in the background. ○ Global Resource Sharing ■ Network Switches, Shared File Systems, Shared Disks. ○ Maintenance activities (Log compaction, data shuffling, etc). ○ Queueing (Network buffers, OS Kernels, intermediate hops) ● Other Aspects - Hardware Variability, Garbage Collection, Energy Management
Variability is amplified by Scale! ● At large scale, components or parallel operations increase. (Fan-outs) ○ Micro-Services ○ Data Partitions ● Further increase in the overall latency of the request. ○ Overall Latency ≥ Latency of Slowest Component ● Server with 1 ms avg. but 1 sec 99%ile latency ○ 1 Server: 1% of requests take ≥ 1 sec ○ 100 Servers: 63% of requests take ≥ 1 sec (Fan-out)
At Google Scale - 10ms 99% percentile for any single request, the 99% percentile for all requests to finish is 140ms, and the 95% percentile is 70ms. ○ meaning that waiting for the slowest 5% of the requests to complete is responsible for half of the total 99%-percentile latency. Techniques that concentrate on these slow outliers can yield dramatic reductions in overall service performance.
Reducing Component Variability - Some Practices ● Prioritize Interactive Requests or Real-time requests over background requests ○ Differentiating service classes and prefer higher-level queuing (Google File System) ○ AWS uses something similar, albeit for Load Shedding [Ref#2] ● Reducing head-of-line blocking ○ Break long-running requests into a sequence of smaller requests to allow interleaving of the execution of other short-running requests. (Search Queries) ○ Example - Pagination requests when scanning large lists. ● Managing background activities and synchronized disruption ○Throttling, Service Operation Breakdowns. ○Example - Anti-virus, security scans, log compression can be run during off-business hours. ● Caching doesn’t impact variability much unless whole data is residing in the cache.
Living with Variability! ● ~Impossible to eliminate all latency variability -- especially with the Cloud Era! ● Develop tail-tolerant techniques that mask or work around temporary latency pathologies, instead of trying to eliminate them altogether. ● Classes of Tail Tolerant Techniques ○ Within Request // Immediate Response Adaptations ■ Focus on reducing variations within a single request path. ■ Time Scale - 10ms ○ Cross Request Long Term Adaptations ■ Focus on holistic measures to reduce the tail at the system level. ■ Time Scale - 10 seconds and above.
Within Request Immediate Response Adaptations ● Cope with a slow subsystem in context of a higher level request ● Time Scale == Right Now ( User is waiting ) ● Multiple Replicas for additional throughput capacity. ○ Availability in the presence of failures. ○ This approach is particularly effective when most requests operate on largely read-only, loosely consistent datasets. ○ Spelling Correction Service, Contacts Lookup - Write Once Read in Millions ● Replication (request & data) can be used to reduce variability in a single higher-level request ○ Hedged Requests ○ Tied Requests
● Issue Same Request to multiple replicas & use the first quickest response. ○ Send a request to a most appropriate Replica (Appropriate Definition is Open) ○ In case, no response within a threshold, issue to another replica. ○ Cancel the outstanding requests after receiving the first response. ● Can amplify the traffic significantly if not implemented prudently. ○ One such approach is to defer sending a secondary request until the first request has been outstanding for more than the 95th-percentile expected latency for this class of requests. This approach limits the additional load to approximately 5% while substantially shortening the latency tail. ○ Mitigates the effect of external interferences. Not the same request slowness. Hedged Requests
● Google Benchmark ○ Read 1,000 keys stored in a BigTable table ○ Distributed across 100 different servers. ○ Sending a hedging request after a 10ms delay reduces the 99.9th-percentile latency for retrieving all 1,000 values from 1,800ms to 74ms while sending just 2% more requests. ● Vulnerabilities ○ Multiple Servers might execute the same requests - redundant computation. ○ 95% tile techniques reduces the impact. ○ Further reduction requires aggressive cancellation of requests. Hedged Requests
Queueing Delays ● Queueing delay add variability before a request begins execution. ● Once a request is actually scheduled and begins execution, the variability of its completion time goes down substantially. ● Mitzenmacher* - Allowing a client to choose between two servers based on queue lengths at enqueue time exponentially improves load-balancing performance over a uniform random scheme. * Mitzenmacher, M. The power of two choices in randomized load balancing. IEEE Transactions on Parallel and Distributed Computing 12, 10 (Oct. 2001), 1094–1104.
Tied Requests ● Instead of choosing one, enqueue in multiple servers. ● Send the identity of the other servers as a part of the request -- Tieing! ● Send a cancellation request to the other servers once a server picks it off the queue. ● Corner Case: ○ What if both servers pick up the request while the cancellation messages are in transit? Network Delay? ○ Typical under low traffic scenario when server queues are empty. ○ Client can introduce a small delay of 2X the average network message delay (1ms or less in modern data-center networks) between the first request and the second request.
● Tied Request Performance on BigTable - Uncached Read Requests from Disk. ● Case 1 - Tests in Isolation. No external interference. ● Case 2 - Concurrent Sorting Job running along with the benchmark test. ● In both scenarios, the overhead of tied requests in disk utilization is less than 1%, indicating the cancellation strategy is effective at eliminating redundant reads. ● Tied requests allow the workloads to be consolidated into a single cluster, resulting in dramatic computing cost reductions.
Cross Request Long Term Adaptations ● Reducing latency variability caused by coarse-grain phenomenon ○ Load imbalance. (Unbalanced data partitions) ■ Centered on data distribution/placement techniques to optimize the reads. ○ Service-time variations ■ Detecting and mitigating effects of service performance variations. ● Simple Partitioning ○ Partitions have equal cost. Static assignment of a partition to a single machine? ○ Sub-Optimal ■ Performance of the underlying machines is neither uniform nor constant over time (Thermal Throttling and Shared workload Interference) ■ Outliers/Hot Items can cause data-induced load imbalance ● Particular item becomes popular and the load for its partition increases.
Micro-Partitioning ● # Partitions > # Machines ● Partition large datasets into multiple pieces (10-100 per machine) [BigTable - Ref#3] ● Dynamic assignment and Load balancing of these partitions to particular machines.
Micro-Partitioning* ● Fine grain load balancing - Move the partitions from one machine to another. ● Average ~20 partitions per machine, the system can shed load in 5% increments and in 1/20th the time it would take if the system had a one-to-one mapping of partitions to machines ● Using such partitions also lead to an improved failure recovery rate. ○ More nodes to takeover the work of a failed node. * Stoica I., Morris, R., Karger, D., Kaashoek, F., and Balakrishnan, H. Chord: A scalable peer-to-peer lookup service for Internet applications. In Proceedings of SIGCOMM (San Diego, Aug. 27–31). ACM Press, New York, 2001, 149–160. ● Does this sound familiar? (Hint : Think Amazon!) ● DynamoDB Paper also talks about a similar concept of multiple virtual nodes mapped to physical nodes. [Ref#4]
Selective Replication ● An enhancement of Micro-partitioning. ● Detection or Prediction of items that are likely to cause load imbalance. ● Create additional replicas of these items/micro-partitions. ● Distribute the load among more replicas rather than moving the partitions across nodes. Practical Examples ● HDFS ○ Replication Factor ○ Rebalancer (Human Triggered) ● Cassandra ○ Auto-rebalancing on topology changes. ○ https://docs.datastax.com/en/opscenter/6.5/opsc/online_help/opscRebalanceCluster.html
Latency Induced Probation Techniques ● Servers can sometimes become slow dues to: ○ May be Data issues ○ Most likely Interference issues (discussed earlier) ● Intermediate Servers - Observe the Latency distribution of the fleet. ● Put a slow server on Probation in case of slowness. ○ Pass shadow requests to collect statistics. ○ Put the node into rotation if it stabilizes. ● Reducing Server Capacity during load can improve overall latency!
Large Information Retrieval Systems ● Latency is a key quality metric. ● Retrieving good results quickly is better than returning best results slowly. ● Good Enough Results? ○ Sufficient amount of corpus has been searched - return the results without waiting for the rest of the queries. ○ Tradeoff between Completeness vs. Responsiveness ● Canary Requests ○ High Fan out systems. ○ Requests may hit untested code paths - crash or degrade multiple servers simultaneously. ○ Forward to limited leaf servers and send to the fleet only if successful.
Mutations/Writes in Data Intensive Services ● For Data Latency variability for mutation is relatively easier. ○ The scale of latency-critical modifications is generally small. ○ Updates can often be performed off the critical path, after responding to the user. ○ Many services can be structured to tolerate inconsistent update models for (inherently more latency-tolerant) mutations. ● Services that require consistent updates, commonly used techniques are quorum-based algorithms (Paxos*) ○ These algorithms touch only three to five replicas, they are inherently tail-tolerant. *Lamport, L. The part-time parliament. ACM Transactions on Computer Systems 16, 2 (May 1998), 133–169.
Hardware Trends ● Some Bad: ○ More aggressive power optimizations to save energy can lead to an increase in variability due to added latency in switching from/to power saving mode. ● Variability technique friendly trends: ○ Lower latency data center networks can make things like Tied Request Cancellations work better.
Conclusions ● Living with Variability ○ Fault-tolerant techniques needed as guaranteeing fault-free operation isn’t feasible beyond a point. ○ Not possible to eliminate all source of variabilities. ● Tail Tolerant Techniques ○ Will become more important with the hardware trends -- software level handling. ○ Require some additional resources but with modest overheads. ○ Leverage the capacity deployed for redundancy and fault tolerance. ○ Can drive higher resource utilization overall with better responsiveness. ○ Common Patterns - easy to bake into baseline libraries.
References & Further Reading 1. https://cseweb.ucsd.edu/~gmporter/classes/sp18/cse124/post/schedule/p74-dean.pdf 2. https://aws.amazon.com/builders-library/using-load-shedding-to-avoid-overload/ 3. Chang F., Dean J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., and Gruber, R.E. BigTable: A distributed storage system for structured data. In Proceedings of the Seventh Symposium on Operating Systems Design and Implementation (Seattle, Nov.). USENIX Association, Berkeley CA, 2006, 205–218. 4. https://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf 5. Managing Tail Latency in Datacenter-Scale File Systems Under Production Constraints Image Source - https://unsplash.com/photos/npxXWgQ33ZQ by @glenncarstenspeters
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