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CLOSER 2021 Apr 28, 2021
SimFaaS: A Performance Simulator for Serverless Computing
Platforms
N. Mahmoudi and H. Khazaei, "SimFaaS: A Simulator for
Serverless Computing Platforms," in The 11th IEEE
International Conference on Cloud Computing and Services
Science (CLOSER 2021).
Hamzeh Khazaei
hkh@yorku.ca
Performant and Available
Computing Systems (PACS) Lab
Nima Mahmoudi
nmahmoud@ualberta.ca
CASCON 2020 Apr 28, 2021
TOC
Introduction
System Description
The Design of SimFaaS
Experimental Validation
Conclusion
2
Sample Use Cases
Introduction
3
Serverless Computing
● A cloud-native deployment model
○ Developers build and run application on the cloud
○ Pay for resources used instead of provisioned
○ Event-driven execution model
● Runtime operation and management done by the provider
○ Overhead reduction
○ Provisioning
○ Scaling resources
● Software is developed by writing functions
○ Well-defined interface
○ Functions deployed separately
4
Typical Developer Workflow
Image source: https://aws.amazon.com/lambda/
5
The Need for a Performance Simulator
● No previous work has been done that accurately captures the unique dynamics of
modern serverless computing platforms
● Accurate performance simulation can beneficial in many ways:
○ Ensure the Quality of Service (QoS)
○ Improve performance metrics
○ Predict/optimize infrastructure cost
○ Move from best-effort to performance guarantees
● It can benefit both serverless provider and application developer
● Allows performance prediction in absence of analytical performance models
6
System Description
7
Function States, Cold Starts, and Warm Starts
● Function States:
○ Initializing: Performing initialization tasks to prepare the function for incoming requests. Includes
infrastructure initialization and application initialization.
○ Running: Running the tasks required to process a request.
○ Idle: Provisioned instance that is not running any workloads. The instances in this state are not billed.
● Cold Start Requests:
○ A request that needs to go through initialization steps due to lack of provisioned capacity.
● Warm Start Requests:
○ Only includes request processing time since idle instance was available
8
Autoscaling
Scaling Out:
Scaling In:
9
Expiration Threshold
The Design of SimFaaS
10
SimFaaS Package Diagram
11
SimFaaS
UML
Class
Diagram
12
Sample Use Cases
13
Steady State
Analysis
14
Sample Code - Instance Count
15
Instance Count Estimation
16
What-If Analysis: Adaptive Expiration Threshold
17
Experimental Validation
18
Experimental Setup
● Experiments done on AWS Lambda
○ Python 3.6 runtime with 128MB of RAM on us-east-1 region
○ A mixture of CPU and IO intensive tasks
● Client was a virtual machine on Compute Canada Arbutus
○ 8 vCPUs, 16GB of RAM, 1000Mbps connectivity, single-digit milliseconds latency to AWS
servers
○ Python with in-house workload generation tool pacswg
○ Official boto3 library for API communication
○ Communicated directly with Lambda API, no intermediary interfaces like API Gateway
19
Experimental Validation
20
Experimental Validation
21
Conclusion
22
Conclusion
● Accurate and extendable performance simulator
● Ability to predict important performance/cost related metrics
● Can predict QoS
● Can benefit serverless providers
● Could be useful to application developers
○ Predict how their system will react under different loads
○ Help them optimize their memory configuration to occur minimal cost that satisfies
performance requirements
● Making the expiration threshold adaptive would allow better cost-performance tradeoff
23
Thank you
Website: research.nima-dev.com
Twitter: @nima_mahmoudi
24

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SimFaaS-CLOSER2021

  • 1. CLOSER 2021 Apr 28, 2021 SimFaaS: A Performance Simulator for Serverless Computing Platforms N. Mahmoudi and H. Khazaei, "SimFaaS: A Simulator for Serverless Computing Platforms," in The 11th IEEE International Conference on Cloud Computing and Services Science (CLOSER 2021). Hamzeh Khazaei hkh@yorku.ca Performant and Available Computing Systems (PACS) Lab Nima Mahmoudi nmahmoud@ualberta.ca
  • 2. CASCON 2020 Apr 28, 2021 TOC Introduction System Description The Design of SimFaaS Experimental Validation Conclusion 2 Sample Use Cases
  • 4. Serverless Computing ● A cloud-native deployment model ○ Developers build and run application on the cloud ○ Pay for resources used instead of provisioned ○ Event-driven execution model ● Runtime operation and management done by the provider ○ Overhead reduction ○ Provisioning ○ Scaling resources ● Software is developed by writing functions ○ Well-defined interface ○ Functions deployed separately 4
  • 5. Typical Developer Workflow Image source: https://aws.amazon.com/lambda/ 5
  • 6. The Need for a Performance Simulator ● No previous work has been done that accurately captures the unique dynamics of modern serverless computing platforms ● Accurate performance simulation can beneficial in many ways: ○ Ensure the Quality of Service (QoS) ○ Improve performance metrics ○ Predict/optimize infrastructure cost ○ Move from best-effort to performance guarantees ● It can benefit both serverless provider and application developer ● Allows performance prediction in absence of analytical performance models 6
  • 8. Function States, Cold Starts, and Warm Starts ● Function States: ○ Initializing: Performing initialization tasks to prepare the function for incoming requests. Includes infrastructure initialization and application initialization. ○ Running: Running the tasks required to process a request. ○ Idle: Provisioned instance that is not running any workloads. The instances in this state are not billed. ● Cold Start Requests: ○ A request that needs to go through initialization steps due to lack of provisioned capacity. ● Warm Start Requests: ○ Only includes request processing time since idle instance was available 8
  • 10. The Design of SimFaaS 10
  • 15. Sample Code - Instance Count 15
  • 17. What-If Analysis: Adaptive Expiration Threshold 17
  • 19. Experimental Setup ● Experiments done on AWS Lambda ○ Python 3.6 runtime with 128MB of RAM on us-east-1 region ○ A mixture of CPU and IO intensive tasks ● Client was a virtual machine on Compute Canada Arbutus ○ 8 vCPUs, 16GB of RAM, 1000Mbps connectivity, single-digit milliseconds latency to AWS servers ○ Python with in-house workload generation tool pacswg ○ Official boto3 library for API communication ○ Communicated directly with Lambda API, no intermediary interfaces like API Gateway 19
  • 23. Conclusion ● Accurate and extendable performance simulator ● Ability to predict important performance/cost related metrics ● Can predict QoS ● Can benefit serverless providers ● Could be useful to application developers ○ Predict how their system will react under different loads ○ Help them optimize their memory configuration to occur minimal cost that satisfies performance requirements ● Making the expiration threshold adaptive would allow better cost-performance tradeoff 23

Editor's Notes

  1. Autoscaling consists of two actions: scaling out and scaling in
  2. Extensible and easy to use Object-oriented design Dependency injection allows design validation Supports popular serverless platforms Built-in tools for visualizing, analyzing, and verifying an analytical performance model Can calculate PDF and CDF for custom state encoding