Jun 2026· IEEE Conference on Network Softwarization· pp. 213-218· 1 citation· 16 references
Computer Science
Abstract
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.
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
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
The default scheduler of Kubernetes, the state-of-the-art container orchestrator, uses fast, local placement decisions. Unfortunately, this design leads to resource fragmentation, reduced cluster usage, and overprovisioning. External solvers can compute global placement plans, but enforcing these plans in upstream clusters is hard. Kubernetes provides no native cross-node preemption, uncoordinated concurrent scheduling leads to inconsistencies, and replacing the default scheduler would sever deployments from upstream cycles. We present OPSche, an open-source Kubernetes Scheduling Framework plugin where external solvers can drive cluster-wide placement decisions in concert with the default scheduler. OPSche atomically validates and enforces solver-produced plans through coordinated framework hooks and supports three trigger modes: scheduling-failure, periodic, and stable-queue -- resp. triggered when a workload cannot be placed, at fixed time intervals, when the set of pending workloads stabilises. Each mode has a blocking variant for a finer tuning of placement quality, latency, and disruption. We pair OPSche with a constraint-based optimisation solver, showing its feasibility across a broad set of cluster configurations and reporting improvements of resource usage by up to 3.0% and scheduling latency by more than a second.
Henrik Christensen, S. Giallorenzo, J. Mauro· 0 citations
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.
Hadil Bouasker, Massinissa Ait Aba, Abdenour Yasser Brahmi et al.· IEEE Conference on Network S...· 0 citations
Cloud providers commonly employ oversubscription strategies to maximize profitability, leveraging the significant gap between the resources purchased by tenants and those actually consumed by their workloads. However, the temporal volatility of workloads may lead to overload on oversubscribed nodes. To address this issue, existing works typically focus on designing reactive rescheduling mechanisms triggered by overload events or adopt conservative oversubscription strategies to mitigate overload risks. Nonetheless, these solutions compromise either tenant experience or provider profitability. In fact, reducing the temporal volatility of workloads is key to addressing the above challenges. We observe that many workloads exhibit temporal complementarity. Aggregating such workloads can effectively mitigate temporal volatility, thereby improving overall resource utilization. Motivated by this insight, we first design a new metric, called Maximum-based Coefficient of Variation (MCV), to quantify the temporal volatility of workloads. We then propose Hestia, a framework that achieves long-term stable oversubscription through workload aggregation. Specifically, we propose a smoothing-based method to classify workloads suitable for aggregation according to their periodicity. Subsequently, we design an aggregation algorithm to minimize the overall MCV, and treat the aggregated workloads as the units for oversubscription. Experimental results show that, using CPU as a representative example, Hestia reduces MCV by 43.3% and increases oversubscription profit by 66.74%.
Baoqing Wang, Gongming Zhao, Hongli Xu et al.· Proceedings of the ACM SIGCO...· 0 citations
Complex microservices topology poses significant challenges to resource scheduling. Consequently, most schedulers rely on extensive offline analysis to prevent potential SLO violations resulting from inaccurate online decisions, leading to high deployment costs. Furthermore, regarding metric selection that is important to bridge the scheduling decision to actual resource allocation, although CPU throttling-based auto-scaling has emerged as a promising approach to capture latency sensitivity, existing methods lack a quantitative mechanism to map throttling signals to resource quota, relying instead on coarse heuristics that potentially waste resources. To address these issues, we propose Bayesian Optimization and Throttling based Vertical Pod Autoscaling (BOTVPA), a sample-efficient scheduling method. BOTVPA features a two-tier architecture: 1) Central Scheduler: We employ Bayesian Optimization (BO) to enable rapid decision-making with minimal samples, eliminating offline training overhead. 2) Local Scheduler: We introduce a novel CPU throttling model that mathematically bridges the gap between throttling rates and CPU allocation. Leveraging this model, the local scheduler dynamically translates BO’s decision into precise CPU resource limits at runtime. Results show that BOTVPA reduces CPU consumption by up to 19.4% over the best-performing baseline while satisfying SLO.
Xiaoming Ye, Weiwei Lin, Xiaoxuan Luo et al.· IEEE Transactions on Service...· 0 citations