Storage Systems for Serverless Analytics

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Darlene Ross
5 years ago
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1 Storage Systems for Serverless Analytics Ana Klimovic * Yawen Wang * Christos Kozyrakis * Patrick Stuedi ⱡ Jonas Pfefferle ⱡ Animesh Trivedi ⱡ * ⱡ

2 Serverless: a new cloud computing paradigm Users write applications as collections of stateless functions The cloud provider schedules user functions on physical resources ü Elastic scaling ü Granular resource allocation à lower cost ü Cluster management and scheduling is abstracted away 2

3 Applications Event-driven web microservices and IoT applications Real-time data analytics, video processing, machine learning, accelerating parallel tasks run on local machines (e.g., software builds) Key challenge: efficient access to remote data 3

4 Remote Storage for Serverless Analytics What are the storage requirements of applications? IO size, throughput per capacity, latency sensitivity What is the impact of application scale? 10s vs. 1000s of λs What kind of storage technology is the best fit? Disk, Flash or DRAM What can we learn from existing storage options do we need a new system? 4

5 3 applications gg parallel software build Parallelism limited by number of independent files to compile MapReduce sort I/O intensive application Abundant parallelism Video analytics (Thousand Island Scanner) High I/O concurrency to fetch 250MB decoder Decode & compute histogram 5

6 3 storage systems S3 Disk-based object store ElastiCache DRAM-based key-value store Crail- -ReFlex Flash-based distributed data store Apache Crail with ReFlex Flash tier 6

7 3 storage systems Per-Lambda network limit ~650 Mb/s S3 Disk-based object store ElastiCache DRAM-based key-value store Crail- -ReFlex Flash-based distributed data store Apache Crail with ReFlex Flash tier 7

8 Latency sensitivity Data analytics applications are generally not latency sensitive Only gg showed sensitivity to latency, but only at low concurrency make -j2 make j1000 8

9 gg with S3 Fetching dependencies Executing Uploading results time (s) worker # 9

10 gg with Redis 30 Fetching dependencies Executing Uploading results Dependencies on compute-bound λs time (s) worker # 10

11 Impact of application parallelism Lambda clients are wimpy: Up to 3GB memory Up to 650 Mb/s networking Low compute power Applications with ample parallelism can just use many concurrent λs Applications with limited parallelism are likely to become bottlenecked by per-λ resources before remote storage 11

12 Throughput intensity MapReduce sort is I/O intensive with ample parallelism When we provision enough throughput and compute in the storage cluster, the bottleneck becomes per-lambda compute Performance scales linearly with the number of workers 12

13 Throughput intensity Video analytics is also throughput-intensive since all Lambdas fetch the same 250MB decoder Improve performance by replicating decoder binary and provisioning a high-throughput storage cluster S3 throughput 100Gb/s 13

14 Throughput per capacity How to design a storage system that meets the throughput per capacity requirements of applications in a cost efficient manner? 850 MB 100 GB 1.1 GB 14

15 Choice of storage technology How to design a storage system that meets the throughput per capacity requirements of applications in a cost efficient manner? 850 MB Flash DRAM: 20 GB/s / 64 GB = GB Flash Flash: 3.2 GB/s / 500 GB = GB DRAM Disk: 0.7 GB/s / 6000 GB =

16 Summary What are the storage requirements of applications? Apps with ample parallelism require high throughput (e.g., >100 Gb/s for 500 workers) I/O size varies from bytes to 100s of MBs, most ephemeral data written/read once Applications are generally not latency sensitive What is the impact of scale? As we increase λ concurrency, applications require higher throughput storage while remote storage latency becomes less important What kind of storage technology is the best fit? Mostly Flash (and DRAM if/when client networking improves) What do learn from existing storage options do we need a new system? S3 provides a convenient storage abstraction and cost model but data-intensive applications require higher throughput and more predictable performance. 16

17 Next challenges and opportunities A tiered Flash + DRAM storage system that meets the demands of (serverless) analytics applications Auto-scaling and right-sizing storage cluster resources Interference-aware data placement Automatic garbage collection to reduce capacity utilization Harvesting short-lived slack resources in the cloud 17

Pocket: Elastic Ephemeral Storage for Serverless Analytics

Pocket: Elastic Ephemeral Storage for Serverless Analytics Ana Klimovic*, Yawen Wang*, Patrick Stuedi +, Animesh Trivedi +, Jonas Pfefferle +, Christos Kozyrakis* *Stanford University, + IBM Research 1