Work

Trust Your Local Scaler: A Continuous, Decentralized Approach to Autoscaling

Year: 2024

Type: article

Abstract: Autoscaling is a critical component of modern cloud computing environments, improving flexibility, efficiency, and cost-effectiveness. Current approaches use centralized autoscalers that make decisions based on averaged monitoring data of managed service instances in fixed intervals. In this scheme, autoscalers are single points of failure, tightly coupled to monitoring systems, and limited in reaction times, making non-optimal scaling decisions costly. This paper presents an approach for continuous decentralized autoscaling, where decisions are made on a service instance level. By distributing scaling decisions of instances over time, autoscaling evolves into a quasi-continuous process, enabling great adaptability to different workload patterns. We analyze our approach on different abstraction levels, including a model-based, simulation-based, and real-world evaluation. Proof-of-concept experiments show that our approach is able to scale different applications deployed in containers and virtual machines in realistic environments, yielding better scaling performance compared to established baseline autoscalers, especially in scenarios with highly dynamic workloads.

Source: Performance Evaluation

DOI: 10.1016/j.peva.2024.102452

Authors: Martin Straesser, Stefan Geissler, Stanislav Lange, Lukas Kilian Schumann, Tobias Hossfeld, Samuel Kounev

Institutions: University of Würzburg, Norwegian University of Science and Technology

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,900

Funder: Deutsche Forschungsgemeinschaft