Research Question and Methodology
The central research question addressed is: “How can we minimize over-allocation of computing resources in cloud-native orchestration platforms without degrading performance?” This question is particularly critical given that over 65% of containers deployed using Kubernetes use less than half of their allocated CPU and memory resources. The research employs a quantitative methodology, examining metrics including CPU utilization, memory utilization, disk I/O, network I/O, CPU throttling, out-of-memory errors, and end-to-end latency. The study accounts for multiple dimensions of resource contention beyond traditional CPU and memory metrics.
Research Design and Techniques
The study proposes a two-stage predictive vertical-scaling mechanism that combines kernel-level telemetry with online learning algorithms. The approach first gathers holistic metrics—from kernel-level run-queue latency and block I/O stalls to container-level CPU and memory usage—and feeds them into a calibrated Random Forest classifier to output a performance-degradation likelihood score. A contextual multi-armed bandit algorithm then uses this degradation likelihood alongside current utilization metrics to learn, over repeated trials, how to adjust CPU and memory allocations, balancing resource savings against performance risk. The framework was implemented on Kubernetes and evaluated against industry standards including the Vertical Pod Autoscaler and SHOWAR.
Findings: Resource Savings vs. Performance and Stability Trade-offs
The results reveal a nuanced picture of optimization trade-offs. The proposed mechanism successfully eliminated all out-of-memory errors in both benchmark applications tested (Google Cloud Online Boutique and Train Ticket), whereas existing state-of-the-art approaches experienced multiple OOM failures. Additionally, the mechanism achieved up to 3x reduction in CPU throttling compared to state-of-the-art solutions while maintaining comparable CPU resource allocation. However, this performance gain comes with trade-offs: the approach demonstrates increased end-to-end latency compared to simpler existing solutions, likely due to the overhead of more frequent resource adjustments and kernel-level instrumentation.
Implications and Future Work
The research highlights the critical interplay between resource efficiency and application stability in containerized environments. While the proposed mechanism excels at preventing performance degradation through predictive capabilities, it incurs significant overhead in terms of CPU and memory consumption for its monitoring and decision-making components. The findings reveal that no single autoscaling configuration is universally optimal; rather, operators must make deliberate trade-off decisions about where their systems should operate on the curve between aggressive resource optimization and conservative performance preservation. Future work could focus on reducing the overhead of kernel-level instrumentation, optimizing the granularity of scaling actions, and integrating the approach with horizontal autoscaling to create more comprehensive resource management strategies.
