Jun 2026· IEEE Conference on Network Softwarization· pp. 225-230· 0 citations· 19 references
Computer Science
Abstract
Efficient resource management remains a main challenge in Kubernetes, where scaling plays a key role in ensuring that resource provisioning adapts to workload variability. Vertical Pod Autoscaler (VPA) is the default mechanism in Kubernetes for vertical scaling, but its reliance on evicting pods to apply new resource values often disrupts applications. To address this, Kubernetes introduced In-Place Resource Resizing (IRR), which allows CPU and memory requests to be updated directly on running pods without eviction. In this paper, we experimentally evaluate VPA combined with IRR under dynamic and longrunning workloads. We designed and implemented a trafficdriven ns-3 workload to generate realistic CPU and memory variations over time, and developed a controller that bridges VPA recommendations with IRR to enable automated, eviction-free vertical scaling while considering QoS-related constraints defined by Kubernetes. Our evaluation analyzes the stability and responsiveness of IRR under different workload intensities, and extends the analysis to power consumption. The results show that eviction-free vertical scaling with IRR provides more stable resource and power behavior compared to the traditional VPA.
Modern cloud-native applications increasingly rely on Kubernetes to orchestrate large-scale, heterogeneous workloads. However, experimenting with its cluster-level mechanisms in a scalable way, such as its diverse scheduling policies, remains difficult in production environments due to operational costs and limited observability. This demonstration showcases KubeTwin, a practical framework that creates and maintains a live Digital Twin of a Kubernetes cluster. KubeTwin enables the modeling of multi-cluster infrastructures, including edge and cloud nodes, heterogeneous resource capacities, and network latencies, allowing for safe experimentation, debugging, and what-if analysis without disrupting production workloads. Through an interactive demonstration, we show how researchers and developers can leverage KubeTwin to (i) assess the performance of workloads under diverse traffic patterns and (ii) evaluate diverse scheduling strategies under different infrastructure scenarios. The demo highlights how Kubernetes Digital Twins can bridge the gap between simulation and real-world experimentation, providing a powerful tool for both operational decision-making and research prototyping.
José Santos, D. Borsatti, Walter Cerroni et al.· IEEE Conference on Network S...· 0 citations
Results demonstrate that HPA effectively responds to workload increases by provisioning additional pods, maintaining system stability and throughput during high-demand periods, with CPU usage and RPS exhibiting predictable scaling behavior aligned with the 15-second Metrics Server scraping interval.
Shamsuddeen Rabiu, Sani Muhammad Tanko, Eli Adama Jiya· Journal of Basics and Applie...· 0 citations
A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.
Mohammed Alhakimi, R. Latip· Computers· 0 citations
Efficient workload scheduling is central to the performance, scalability, and cost-effectiveness of modern clouds. In the most popular cloud platform today, Kubernetes (K8s), the scheduler can be extended with multiple scheduling plugins, enabling fine-grained control over scheduling decisions. However, cloud providers often lack clear insights into the trade-offs of individual placement strategies and resort to combining multiple plugins without a clear rationale. This uncertainty creates a gap in understanding which scheduling configuration yields optimal results for a given application or traffic pattern under specific infrastructure conditions. Thus, this paper investigates the implications of different scheduling strategies in K8s by leveraging KubeTwin (KT), a high-fidelity simulator for the K8s platform designed for reproducible experimentation. This work extended KT with advanced scheduling capabilities to evaluate the impact of single and combined scheduling plugins across diverse workloads and infrastructure conditions, focusing on key aspects such as performance, fairness, and resource utilization. The results highlight that Node-affinity and Diktyo single-purpose plugins consistently outperform multi-objective scheduling combinations, achieving the lowest deployment costs (below 50 price/day), shortest queue times (under 50 ms), and fastest response times (below 200 ms). In contrast, combined strategies, such as Balanced, result in significantly higher costs and higher latency. This study shows that combining multiple optimization criteria within a single scheduling strategy often degrades performance, whereas targeted, single-objective plugins deliver robust and predictable behavior across heterogeneous infrastructures and workload patterns.
José Santos, D. Borsatti, Walter Cerroni et al.· IEEE Conference on Network S...· 1 citation
A hybrid congestion-aware autoscaling framework for serverless platforms inspired by TCP slow-start and congestion control principles is proposed, which significantly improves latency performance, scalability, and resource efficiency in serverless environments.
Mohammad Tari, Mostafa Ghobayee-Arani, Jafar Pouramini· International Journal of Clo...· 0 citations
Flagger is recommended for strict resource predictability in threshold-breach environments, while Argo Rollouts is recommended for broader fault-type resilience is operationally critical where broader fault-type resilience is operationally critical.
Gagah Syuja Saka Abdullah, Rama Aria Megantara· SinkrOn· 0 citations
