Dirigent: A High-Performance Serverless Orchestration System

核心概念

Dirigent is a new cluster manager architecture designed to efficiently orchestrate short-lived, sporadically invoked serverless functions, addressing the performance limitations of existing FaaS platforms that build on top of generic cluster management systems.

要約

The paper proposes Dirigent, a clean-slate system architecture for serverless (Function as a Service, FaaS) cluster management, designed to address the performance limitations of existing FaaS platforms that build on top of generic cluster management systems like Kubernetes.

Key insights and highlights:

• Current FaaS cluster managers built on Kubernetes suffer from high scheduling latency, especially when handling bursts of concurrent function invocations that require creating many new function sandboxes (containers) on worker nodes.
• The root cause is the complex, hierarchical state management and persistent state updates in Kubernetes-based designs, which become a bottleneck under the high churn of short-lived function sandboxes.
• Dirigent adopts three key design principles to address these issues:
  1. Simplified internal cluster management abstractions to minimize state management complexity.
  2. Elimination of persistent state updates on the critical path of function invocations, relaxing exact state reconstruction guarantees.
  3. Monolithic control and data planes to minimize internal communication overheads.
• Dirigent can create 2500 function sandboxes per second, 1250x more than Knative, a representative Kubernetes-based FaaS platform.
• For a production FaaS workload trace, Dirigent reduces 99th percentile per-function scheduling latency by 2.79x compared to AWS Lambda.
• Dirigent maintains fault tolerance guarantees comparable to existing FaaS platforms while improving performance.

統計

Dirigent can create 2500 function sandboxes per second, 1250x more than Knative.
For a production FaaS workload trace, Dirigent reduces 99th percentile per-function scheduling latency by 2.79x compared to AWS Lambda.

引用

"While initializing function sandboxes on worker nodes takes 10-100s of milliseconds1 with today's FaaS worker system software [34, 37, 43, 60, 74, 80, 81], we find that the end-to-end latency to initialize function sandboxes is often one or more orders of magnitude higher in operational FaaS environments."
"We find that the current approach of building FaaS cluster managers on top of legacy orchestration systems like Kubernetes leads to high scheduling delay at high sandbox churn, which is typical in FaaS clusters."

抽出されたキーインサイト

Dirigent: Lightweight Serverless Orchestration

by Laza... 場所 arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16393.pdf

深掘り質問

How can Dirigent's design principles be applied to improve the performance of other types of distributed systems beyond serverless computing?

Dirigent's design principles, such as simplifying internal abstractions, eliminating persistent state updates on the critical path, and using a monolithic control and data plane, can be applied to improve the performance of various distributed systems. For example, in microservices architectures, simplifying internal abstractions can reduce the complexity of managing multiple services and improve overall system performance. By eliminating persistent state updates on the critical path, systems can reduce latency and improve throughput, especially in scenarios where frequent state updates are not necessary. Additionally, using a monolithic control and data plane can streamline communication and reduce overhead in distributed systems, leading to better scalability and fault tolerance.

What are the potential drawbacks or limitations of Dirigent's approach of relaxing exact state reconstruction guarantees, and how could this be addressed in future work?

One potential drawback of relaxing exact state reconstruction guarantees in Dirigent is the possibility of data inconsistency or loss in certain failure scenarios. While this approach may improve performance and reduce latency, it could lead to challenges in maintaining data integrity and consistency across the system. In future work, this limitation could be addressed by implementing mechanisms for eventual consistency, where the system ensures that data eventually reaches a consistent state even if immediate consistency is not guaranteed. Additionally, incorporating techniques such as distributed transactions or checkpointing mechanisms could help mitigate the risks associated with relaxed state reconstruction guarantees.

Given the performance benefits of Dirigent, how might this impact the broader adoption and evolution of serverless computing platforms?

The performance benefits of Dirigent, such as higher throughput, lower latency, and improved fault tolerance, could significantly impact the broader adoption and evolution of serverless computing platforms. These benefits could attract more users and organizations to adopt serverless computing for their applications, especially those with high scalability and performance requirements. The efficiency and reliability offered by Dirigent could lead to increased trust in serverless platforms and drive innovation in the serverless ecosystem. Additionally, the success of Dirigent could inspire other platform providers to optimize their systems for better performance, ultimately raising the bar for serverless computing platforms industry-wide.