Work

ScaleIP: A hybrid autoscaling of VoIP services based on deep reinforcement learning

Year: 2025

Type: article

Abstract: Adaptive resource provisioning has become crucial for cloud-based applications, especially those managing real-time traffic like Voice over IP (VoIP), which experience rapidly fluctuating workloads. Traditional static provisioning methods often fall short in these dynamic environments, leading to inefficiencies and potential service disruptions. Existing solutions struggle to maintain performance under varying traffic conditions, particularly for time-sensitive applications. This paper introduces ScaleIP, a hybrid autoscaling solution for containerized VoIP services that offers real-time adaptability and efficient resource management. ScaleIP leverages Deep Reinforcement Learning to make dynamic and efficient scaling decisions, improving call latency, increasing the number of successfully routed calls, and maximizing resource utilization. We evaluated ScaleIP through extensive experiments conducted on a real testbed utilizing the customer Call Detail Record (CDR) from 2023 provided by World Direct, encompassing over 89 million calls. The results show that ScaleIP consistently maintains call latency below 2 s, increases the number of successfully routed calls by 3.26 ×, and increases the resource utilization up to 60 % compared to state-of-the-art autoscaling methods.

Source: Computer Communications

DOI: 10.1016/j.comcom.2025.108314

Authors: Zahra Najafabadi Samani, Juan Aznar Poveda, Dominik Gratz, Rene Hueber, Philipp Kalb, Thomas Fahringer

Institution: Universität Innsbruck

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,620