StatuScale: Status-aware and Elastic Scaling Strategy for Microservice Applications
Linfeng Wen, Minxian Xu, Sukhpal Singh Gill, Muhammad Hafizhuddin Hilman, Satish Narayana Srirama, Kejiang Ye, Chengzhong Xu

TL;DR
StatuScale is a novel elastic scaling framework for microservices that dynamically adjusts resources based on load status, improving response times and resource efficiency during request spikes.
Contribution
The paper introduces StatuScale, a status-aware elastic scaling framework with a load status detector and correlation factor metric, validated on Kubernetes with Alibaba traces.
Findings
Reduces average response time by up to 12.34%
Decreases service level objective violations
Improves resource usage efficiency
Abstract
Microservice architecture has transformed traditional monolithic applications into lightweight components. Scaling these lightweight microservices is more efficient than scaling servers. However, scaling microservices still faces the challenges resulted from the unexpected spikes or bursts of requests, which are difficult to detect and can degrade performance instantaneously. To address this challenge and ensure the performance of microservice-based applications, we propose a status-aware and elastic scaling framework called StatuScale, which is based on load status detector that can select appropriate elastic scaling strategies for differentiated resource scheduling in vertical scaling. Additionally, StatuScale employs a horizontal scaling controller that utilizes comprehensive evaluation and resource reduction to manage the number of replicas for each microservice. We also present a…
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Taxonomy
TopicsSoftware System Performance and Reliability · Cloud Computing and Resource Management · IoT and Edge/Fog Computing
