Lambda
Learn how Sedai uniquely optimizes AWS serverless functions to achieve your performance and cost goals.
Sedai optimizes two key aspects of AWS Lambdas:
Memory Allocation
Autonomous Concurrency
Memory Allocation
Sedai profiles serverless functions and determines optimal memory configurations based on reinforcement learning. Optimizations typically yield faster and cheaper results.
You can define specific goals to guide how the system optimizes functions.
Autonomous Concurrency
This feature helps eliminate cold starts with marginal impact on cloud spend. Using a combination of the following, Sedai informs its central control and learning unit to manage Lambda cold starts:
Sedai AV Lambda Extension (external extension written in Go): Analyzes Lambda behavior such as runtime events, cold start duration, and concurrency patterns (this allows close Lambda monitoring without any overhead)
Traffic Seasonality: Monitors and builds a seasonality model in order to understand local and global trends and anticipate traffic fluctuations
Provisioned Concurrency: Monitors concurrency events as well as concurrency scaling based on seasonality
Supported Runtimes
NodeJS 12 and above on
x86_64
Python 3.7 and above on
x86_64
Java 8 and above on
x86_64
The static and runtime characteristics of the extension are limited to:
Additional 12 MB size in deployed Lambda layer
0.1 ms latency overhead per 1,000 invocations
0.11 ms execution time overhead per 1,000 invocations
Setup
The AV Lambda extension acts as Sedai's control center for your Lambdas. It is responsible for collecting metrics from Sedai's autonomous concurrency extensions and communicating with Sedai core components.
Deploy the AV Lambda extension manually or via your preferred IaC tool:
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