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Raptor - Distributed Scheduling for Serverless Functions

Core Concepts
Effective serverless scheduling policies are crucial to address issues like high cold start latencies, poor fault tolerance, and limited support for parallel processing in modern serverless platforms.
The article discusses the challenges faced by serverless platforms in scheduling function requests efficiently. It introduces Raptor, a distributed scheduling service designed to integrate with existing serverless frameworks. Raptor addresses key issues such as cold start latencies, fault tolerance, and support for parallel processing patterns. The architecture and benefits of Raptor are detailed along with an empirical evaluation showcasing its performance improvements. Related work in the field of serverless computing scheduling is also mentioned.
Our service reduces cold start execution latencies by up to 80%, steady state latencies by up to 10%, and does so with negligible time and memory overhead. Around half of the functions running on modern serverless platforms today take less than 1 second to execute. For certain workloads, poor scheduling and limited fault tolerance exclude serverless computing as a possible substitution for traditional data processing frameworks such as Spark, MapReduce, or Dryad.

Key Insights Distilled From

by Kevin Exton,... at 03-26-2024

Deeper Inquiries

How can Raptor's approach to distributed scheduling be applied to other serverless platforms?

Raptor's approach to distributed scheduling, which includes integrating with existing serverless frameworks and providing native operating system controls within user function containers, can be applied to other serverless platforms by following a similar implementation strategy. By designing a distributed scheduling service that works in conjunction with the specific architecture of different serverless platforms, developers can enhance performance and fault tolerance across various environments. The key lies in understanding the underlying infrastructure of each platform and adapting Raptor's principles accordingly.

What are the potential drawbacks or limitations of integrating Raptor with existing serverless architectures?

While integrating Raptor with existing serverless architectures offers benefits such as reduced cold start latencies and improved steady state performance, there are potential drawbacks and limitations to consider: Compatibility Issues: Existing serverless architectures may have unique configurations or dependencies that could conflict with Raptor's implementation. Resource Overhead: Integrating additional components like Raptor may increase resource consumption on already constrained systems. Complexity: Adding a new layer of scheduling complexity could introduce challenges in maintenance, troubleshooting, and debugging. Security Concerns: Any changes made through integration must not compromise the security posture of the overall system.

How might advancements in data locality management further enhance the performance of serverless computing workflows?

Advancements in data locality management can significantly boost the performance of serverless computing workflows by: Reducing Latency: Improved data locality ensures that functions have quicker access to required data, reducing network latency. Optimizing Resource Usage: Efficient data caching mechanisms can minimize redundant data transfers between functions, optimizing resource utilization. Enhancing Scalability: Effective data locality management enables better scalability for parallel processing tasks by ensuring efficient distribution and retrieval of shared datasets. Improving Reliability: By maintaining consistent access to relevant data sources, advancements in data locality management contribute to increased workflow reliability and consistency. By leveraging these advancements, organizations can streamline their serverless workflows for enhanced efficiency and performance across diverse use cases.