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.
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
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
The method lets an HPC site compare deployment strategies using both user-visible performance and scheduler impact, then select the fastest strategy within its own RPC-demand limit.
Nil Tianchen Mu, William Dizon, Glen Otero et al.· 0 citations
This paper presents a serverless platform-driven CPU load balancing framework that enables the control plane to directly influence CPU scheduling through a custom Linux scheduler built on SchedExt(SCX).
EnsembleLauncher is introduced, a recursively hierarchical workflow orchestrator for exascale systems, featuring a fully decentralized control plane and a programmable scheduling policy interface that outperforms state-of-the-art tools by more than four times.
Harikrishna Tummalapalli, Christine M. Simpson, Riccardo Balin et al.· 0 citations