International Conference on Computer and Knowledge Engineering

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FaaScaler: An Automatic Vertical and Horizontal Scaler for Serverless Computing Environments

Authors :

Zahra Rezaei¹ Saeid Abrishami² Seid Nima Moeintaghavi³

1- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran 2- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran 3- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Keywords :

Serverless Computing،Vertical Scaling،Horizontal Scaling،Reinforcement Learning

Abstract :

Function as a Service (FaaS) is a cloud computing model that relieves application developers from the responsibility of managing infrastructure tasks like resource provisioning and scaling. Serverless functions, however, have specific and limited execution times, making effective auto-scaling decisions for these services particularly challenging. To ensure proper configuration and scaling of resources, it is essential to have a thorough understanding of environmental changes and dynamic factors that influence system performance, alongside considering function specifications and user needs. To tackle this issue, we introduce FaaScaler, a reinforcement learning-based approach for scaling functions and containers, aimed at meeting function deadlines and enhancing processor efficiency. FaaScaler models the scaling challenge as a Markov Decision Process (MDP) and utilizes the Proximal Policy Optimization (PPO) learning algorithm to train an agent to make both horizontal and vertical scaling decisions. The proposed system is evaluated using the OpenFaaS platform, with results indicating that FaaScaler effectively meets execution deadlines for most requests while optimizing processor utilization.

Papers List

List of archived papers

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Improving Soft Error Reliability of FPGA-based Deep Neural Networks with Reduced Approximate TMR

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Towards Low-Overhead Mitigation of Trojan Bit-Flip Attacks on DNNs via Causal Inference

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A routing method with the approach of reducing energy consumption in WSNs with the Jellyfish Search (JS) optimizer algorithm and unequal clustering

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