Serverless Computation with OpenLambda
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1 less Computation with OpenLambda Scott Hendrickson, Stephen Sturdevant, Tyler Harter, Venkateshwaran Venkataramani, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau Unaffiliated
2 Web development in the cloud CDN: static content (e.g., JavaScript) Compute: dynamic logic (e.g., Python) Storage: application data RPCs Queries
3 Web development in the cloud CDN: static content (e.g., JavaScript) Compute: dynamic logic (e.g., Python) Storage: application data RPCs Queries (VMs)
4 Web development in the cloud CDN: static content (e.g., JavaScript) Compute: dynamic logic (e.g., Python) Storage: application data RPCs Queries (VMs) compute is evolving
5 Web development in the cloud CDN: static content (e.g., JavaScript) Compute: dynamic logic (e.g., Python) Storage: application data RPCs Queries (Containers) compute is evolving
6 Web development in the cloud CDN: static content (e.g., JavaScript) Compute: dynamic logic (e.g., Python) Storage: application data RPCs Queries (Lambdas) compute is evolving
7 Web development in the cloud CDN: static content (e.g., JavaScript) Compute: dynamic logic (e.g., Python) Storage: application data RPCs Queries (Lambdas) prior to the Lambda model, cloud compute was neither elastic nor pay-as-you-go
8 Outline Evolution of compute Non-conventional virtualization Lambda model Why OpenLambda? Conclusion
9 How to virtualize compute?
10 Classic web stack RPCs Application OS Hardware
11 Classic web stack RPCs Application OS weak virtualization Hardware
12 1st generation: virtual machines RPCs Application OS Hardware virtual H/W
13 1st generation: virtual machines RPCs Application Application OS Hardware OS virtual H/W
14 1st generation: virtual machines RPCs Application Application OS Hardware OS virtual H/W advantages: very flexible use any OS problems: interposition is RAM used? (ballooning) redundancy (e.g., FS journal)
15 2nd generation: containers RPCs Application Application OS virtual OS Hardware advantages: centralized view init H/W once
16 Are containers good enough?
17 Container case studies Literature: Google Borg Internal container platform [1] 25 second median startup 80% of time spent on package installation matters for flash crowds, load balance, interactive development, etc [1] Large-scale cluster management at Google with Borg.
18 Container case studies Literature: Google Borg Internal container platform [1] 25 second median startup 80% of time spent on package installation matters for flash crowds, load balance, interactive development, etc Experimental: Amazon Elastic Beanstalk Autoscaling cloud service Build applications as containerized servers, service RPCs Rules dictate when to start/stop (various factors) [1] Large-scale cluster management at Google with Borg.
19 Interesting autoscaling rule
20 Experiment Simulate a small short burst Maintain 100 concurrent requests Use 200 ms of compute per request Run for 1 minute
21 Container Case Study: Elastic Beanstalk
22 Container Case Study: Elastic Beanstalk
23 Container Case Study: Elastic Beanstalk Conclusion: Elastic Beanstalk does not scale quickly enough to handle load bursts.
24 Container Case Study: Elastic Beanstalk Elastic BS Conclusion: Elastic Beanstalk does not scale quickly enough to handle load bursts.
25 Why should it take minutes (or even seconds) to execute scripts that are <1000s of LOC?
26 2nd generation: containers RPCs Application Application OS virtual OS Hardware advantages: centralized view init H/W once problems: large deployment bundle server spinup
27 2nd generation: containers RPCs Application Application OS virtual OS Hardware
28 3rd generation: Lambdas RPCs Application Application and Runtime virtual servers OS Hardware
29 3rd generation: Lambdas RPCs Application Application and Runtime virtual servers OS Hardware serverless computing
30 3rd generation: Lambdas RPCs FnA FnZ Fn0 Fn9 virtual servers and Runtime OS Hardware decompose application
31 3rd generation: Lambdas RPCs FnA FnZ Fn0 Fn9 small deployment bundle virtual servers already running and Runtime already in mem OS Hardware advantages: very fast startup agile deployment share memory problems: not flexible
32 Lambda elasticity Repeat ElasticBS experiment Maintain 100 concurrent requests Spin 200 ms per request Run for 1 minute
33 Lambda elasticity
34 Lambda elasticity Conclusion: Lambdas are highly elastic
35 Lambda elasticity Conclusion: Lambdas are highly elastic (though a little slow)
36 Outline Evolution of compute Non-conventional virtualization Lambda model Why OpenLambda? Conclusion
37 Lambda model Run user handlers in response to events web requests (RPC handlers) database updates (triggers) scheduled events (cron jobs) Pay per function invocation actually pay-as-you-go no charge for idle time between calls e.g., charge actual_time * memory_cap
38 Share everything Share server pool between customers Any worker can execute any handler No spinup time Less switching Encourage specific runtime (C#, Node.JS, Python) Minimize network copying Code will be in resident in memory
39 Multi-node architecture load balancers workers Load Balancer Load Balancer Python handler store Python
40 Multi-node architecture load balancers workers Load Balancer Load Balancer Python handler store developer upload code H1 H2 Python
41 Multi-node architecture load balancers workers Load Balancer Load Balancer large Python handler store small H1 H2 Python
42 Multi-node architecture load balancers workers user RPC Load Balancer Python Load Balancer handler store H1 H2 Python
43 Multi-node architecture load balancers workers user RPC Load Balancer Python Load Balancer handler store H1 H2 Python
44 Multi-node architecture load balancers workers user RPC Load Balancer H2 Python Load Balancer handler store H1 H2 Python
45 Multi-node architecture load balancers workers user RPC Load Balancer H2 sandbox Python Load Balancer handler store H1 H2 Python
46 Multi-node architecture load balancers workers user RPC Load Balancer H2 sandbox Python Load Balancer handler store H1 H2 Python
47 Outline Evolution of compute Non-conventional virtualization Lambda model Why OpenLambda? Conclusion
48 Need for open source serverless Many research areas Applications, tools, distributed systems, execution engines Evaluate ideas by building, not just simulating
49 Need for open source serverless Many research areas Applications, tools, distributed systems, execution engines Evaluate ideas by building, not just simulating First implementations are proprietary AWS Lambda Google Cloud Functions
50 Need for open source serverless Many research areas Applications, tools, distributed systems, execution engines Evaluate ideas by building, not just simulating First implementations are proprietary AWS Lambda Google Cloud Functions OpenLambda: explore further-reaching techniques Goal: enable academic research on Lambdas Storage awareness, kernel support, RPC inspection
51 Need for open source serverless Many research areas Applications, tools, distributed systems, execution engines Evaluate ideas by building, not just simulating First implementations are proprietary AWS Lambda Google Cloud Functions OpenLambda: explore further-reaching techniques Goal: enable academic research on Lambdas Storage awareness, kernel support, RPC inspection Other recent open-source implementations Azure Functions IBM OpenWhisk
52 OpenLambda research topics Workloads Workload studies Benchmarks Versioning+dependencies Code characteristics Package management Distributed systems Databases Load balancing Scatter gather patterns Sessions and streams Tools Debugging Monetary cost optimization Porting legacy applications Execution engines Sandboxing Containers Just-in-time interpreters
53 OpenLambda research topics load balancers workers user RPC Load Balancer H2 sandbox Python Load Balancer handler store developer upload code H1 H2
54 OpenLambda research topics load balancers workers user workloads RPC Load Balancer H2 sandbox Python Load Balancer handler store execution engine developer tools upload code H1 H2 distributed systems
55 OpenLambda research topics load balancers workers user workloads RPC Load Balancer H2 sandbox Python Load Balancer handler store execution engine developer tools upload code H1 H2 distributed systems
56 Understanding Lambda workloads Collaborate with industry, measurement studies e.g., Azure Functions Build LambdaBench Everybody joining builds an application Ticketing, calendar, autocomplete, OCR, flash card, stock alert, blog, and scientific compute applications Trace RPC calls (e.g., AJAX) of existing apps Gmail:
57 OpenLambda research topics load balancers workers user workloads RPC Load Balancer H2 sandbox Python Load Balancer handler store execution engine developer tools upload code H1 H2 distributed systems
58 OpenLambda research topics load balancers workers user workloads RPC Load Balancer H2 sandbox Python Load Balancer handler store execution engine developer tools upload code H1 H2 distributed systems
59 Developer tools Portability E.g., can Django apps run on Lambdas? Debugging Understand Lambda flows, may be a complex graph Optimizing expense Hard with containers: how to share 1-hour server time across requests? With Lambdas: know cost of every RPC and query Show where money is going
60 OpenLambda research topics load balancers workers user workloads RPC Load Balancer H2 sandbox Python Load Balancer handler store execution engine developer tools upload code H1 H2 distributed systems
61 OpenLambda research topics load balancers workers user workloads RPC Load Balancer H2 sandbox Python Load Balancer handler store execution engine developer tools upload code H1 H2 distributed systems
62 Building locality-aware Lambdas Use deep inspection of RPCs for routing Working with grpc group GSOC project (Stephen Sturdevant)
63 Building locality-aware Lambdas Use deep inspection of RPCs for routing Working with grpc group GSOC project (Stephen Sturdevant) Locality factors code locality data locality session locality
64 Building locality-aware Lambdas Use deep inspection of RPCs for routing Working with grpc group GSOC project (Stephen Sturdevant) Locality factors code locality data locality session locality Lambda workers Load Balancer call B?
65 Building locality-aware Lambdas Use deep inspection of RPCs for routing Working with grpc group GSOC project (Stephen Sturdevant) Locality factors code locality data locality session locality Lambda workers A numpy mem call B (uses numpy) Load Balancer numpy disk mem numpy disk
66 Building locality-aware Lambdas Use deep inspection of RPCs for routing Working with grpc group GSOC project (Stephen Sturdevant) Locality factors code locality data locality session locality Lambda workers DB shard: A-M (keys) call B Load Balancer predict X local query B DB shard: N-Z (keys)
67 Building locality-aware Lambdas Use deep inspection of RPCs for routing Working with grpc group GSOC project (Stephen Sturdevant) Locality factors code locality data locality session locality browser Load Balancer web socket Lambda workers session service Lambda B Engine session service Lambda Engine
68 OpenLambda research topics load balancers workers user workloads RPC Load Balancer H2 sandbox Python Load Balancer handler store execution engine developer tools upload code H1 H2 distributed systems
69 OpenLambda research topics load balancers workers user workloads RPC Load Balancer H2 sandbox Python Load Balancer handler store execution engine developer tools upload code H1 H2 distributed systems
70 Minimizing latency
71 Minimizing latency reduce How can we reduce base latency?
72 Execution engine Sandboxing Process VMs (e.g., JVM): how to mostly initialize? Containers: how to speed up restart and optimize pausing?
73 Execution engine Sandboxing Process VMs (e.g., JVM): how to mostly initialize? Containers: how to speed up restart and optimize pausing? Language runtimes Challenge: code warms up over time How to share dynamic optimizations? code code tracing enables inline optimization cold cold interpreted compiled worker A cold worker B
74 Outline Evolution of compute Non-conventional virtualization Lambda model Why OpenLambda? Conclusion
75 Conclusion Lambdas finally deliver on promises of the cloud finally pay-as-you-go finally elastic will fundamentally change how people build scalable applications New challenges in every area of systems scheduling, isolation, languages, debugging, tools, storage, Getting involved contribute at site:
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