FaaSConf: QoS-aware Hybrid Resources Configuration for Serverless Workflows

Serverless computing, also known as Function-as-a-Service (FaaS), is a significant development trend in modern software system architecture. The workflow composition of multiple short-lived functions has emerged as a prominent pattern in FaaS, exposing a considerable resources configuration challeng...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 957 - 969
Hlavní autoři: Wang, Yilun, Chen, Pengfei, Dou, Hui, Zhang, Yiwen, Yu, Guangba, He, Zilong, Huang, Haiyu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 27.10.2024
Témata:
Concurrent computing
Configuration Tuning
Costs
Hardware
MARL
Quality of service
Reinforcement learning
Robustness
Serverless computing
Software engineering
Software systems
Source coding
ISSN:2643-1572
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Serverless computing, also known as Function-as-a-Service (FaaS), is a significant development trend in modern software system architecture. The workflow composition of multiple short-lived functions has emerged as a prominent pattern in FaaS, exposing a considerable resources configuration challenge compared to individual independent serverless functions. This challenge unfolds in two ways. Firstly, workflows frequently encounter dynamic and concurrent user workloads, increasing the risk of QoS violations. Secondly, the performance of a function can be affected by the resource reprovision of other functions within the workflow.With the popularity of the mode of concurrent processing in one single instance, concurrency limit as a critical configuration parameter imposes restrictions on the capacity of requests per instance. In this study, we present FaaSConf, a QoS-aware hybrid resource configuration approach that uses multi-agent reinforcement learning (MARL) to configure hybrid resources, including hardware resources and concurrency, thereby ensuring end-to-end QoS while minimizing resource costs. To enhance decision-making, we employ an attention technique in MARL to capture the complex performance dependencies between functions. We further propose a safe exploration strategy to mitigate QoS violations, resulting in a safer and efficient configuration exploration. The experimental results demonstrate that FaaSConf outperforms state-of-the-art approaches significantly. On average, it achieves a 26.5% cost reduction while exhibiting robustness to dynamic load changes.CCS CONCEPTS* Computing methodologies → Distributed computing methodologies.
ISSN:2643-1572
DOI:10.1145/3691620.3695477