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.
Abstract
The rapid expansion of the cloud–edge continuum requires containerized applications to scale dynamically across highly heterogeneous and resource-constrained environments. As the de facto standard for container orchestration, Kubernetes (K8s for short) relies heavily on its scheduling subsystem to manage these complex distributed environments. However, default scheduling methodologies are inherently designed for homogeneous cloud data centers and bring substantial deployment challenges when used in edge topologies. While numerous custom schedulers, plugins, and extensions have been put forward to bridge this gap, a review of the state of the art is needed to evaluate existing approaches and capture recent trends. In this survey, we present a comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026. We establish a multi-dimensional taxonomy that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics. We investigate open challenges arising across different computing paradigms and highlight recent trends and possible directions for future research in Kubernetes scheduling.
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 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
The new EMC+ proposal is an OS‐driven elasticity manager for container‐based environments that continuously estimates idle core cycles left by regular (inelastic) applications, and reallocates idle cores to elastic ones, even during short time intervals, and has minimal impact on the performance and QoS of colocated inelastic applications.
J. C. Saez, Carlos Bilbao, Manuel Prieto-Matías· Concurrency and Computation· 0 citations
Large-scale managed cloud databases leverage sophisticated load Packing and Migration (PAM) algorithms, which provide the efficiencies necessary for running these services at scale on cloud resources. Research into optimizing the resources and reliability of cloud databases at massive scales is limited by a lack of public NoSQL workloads. We address this in the context of
Cosmos DB
, Microsoft's flagship cloud-hosted NoSQL database. We first propose
open-source NoSQL workloads
from real Cosmos DB clusters, and analyze these traces to derive a novel reliability metric,
Distressed Resource Volume (DRV)
, which captures the quality of service experienced by the end user. We then develop an
open-source policy simulation framework, LoadStar
, powered by a non-parametric statistical model of estimating the QoS of real traffic patterns. These form a reusable benchmark pipeline for validating policies for resource-centric NoSQL workloads. We then define a
resource optimization problem
for placing Cosmos DB replicas onto VM nodes, develop the Luna model for
forecasting
future load distributions, and the Orbit
PAM algorithm
that uses these forecasts to trigger and rebalance stressed replicas, to reduce tail-errors. Our experiments, validated using LoadStar for these workloads, demonstrate Orbit's benefits over the existing Cosmos DB policy and a worst-fit optimized baseline, with higher load delivered at lower error rates and up to 35% reduction in resources. These have been deployed in production, with potential savings of $100
Ms
/yr while improving service reliability for millions of customers.
Gunika Verma, Aashutosh A, Pooja Srinivas et al.· Proceedings of the VLDB Endo...· 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