Aug 2026· Journal of Soft Computing Paradigm· 0 citations· 11 references
TL;DR
A dynamic priority scheduling framework at the kernel level that enhances the CPU allocation to latency-sensitive containers running in Kubernetes environments and reveals a significant improvement in terms of latency reduction, enhanced throughput, efficient utilization of CPU resources, and stable performance of scheduling under resource contention.
Abstract
The use of containerized cloud platforms has now become inevitable for latency-sensitive applications; but resource contention between different workloads often creates the problem of noisy neighbors, which causes higher response latency and poor service quality. We present SCX_MUS (Scheduler Extensions for Multi-User Scheduling), a dynamic priority scheduling framework at the kernel level that enhances the CPU allocation to latency-sensitive containers running in Kubernetes environments. The proposed framework uses Linux sched_ext architecture along with the extension of eBPF to support dynamic cgroup-based scheduling through runtime awareness. A lightweight user space component continuously observes the workload of Kubernetes and adjusts the scheduling priorities of the containers without any need for recompiling the kernel or restarting containers. The experimental evaluation of the proposed framework was done with the help of Redis and stress-ng workloads in the same benchmarking condition compared to the native Linux Completely Fair Scheduler (CFS). The experimental results reveal a significant improvement in terms of latency reduction, enhanced throughput, efficient utilization of CPU resources, and stable performance of scheduling under resource contention.
Ultra-low-latency (ULL) SSDs enable cloud service providers to co-locate latency-sensitive services and throughputoriented background jobs on the same machines. However, their microsecond-scale latency creates a scheduling dilemma: conventional I/O schedulers introduce visible overhead, while disabling scheduling removes the kernel's ability to enforce workload-specific Quality of Service (QoS). This paper presents ARDA, an Adaptive Request Dispatching Algorithm for QoSaware I/O scheduling on ULL SSDs. ARDA dynamically assigns dispatch budgets based on workload QoS requirements inspired by real-time CPU scheduling concepts. Unlike conventional schedulers that rely on request types or relative priorities, ARDA regulates the amount of I/O each workload may inject into the device, thereby satisfying latency targets while preserving throughput and avoiding hardware queue overload. Evaluation on an emulated ULL SSD shows that ARDA reduces average latency by $\mathbf{2 2. 9 6 - 4 2. 9 5 \%}$ and maximum latency by $\mathbf{3 6. 2 6 - 4 4. 8 7 \%}$ compared with Kyber under mixed workloads. Under writesaturated conditions, ARDA reduces worst-case latency by up to 59% compared with the none scheduler. These results show that target-aware scheduling can provide predictable QoS for colocated workloads on ULL SSDs without sacrificing throughput.
Ming Wei, Tzu-Chieh Huang, Chieh-Lin Tsai et al.· IEEE International Conferenc...· 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· De Computis· 0 citations
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions.
Omar Hekal, Josepaul Paulachan, Daniel Onwuchekwa et al.· Future Internet· 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).
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
A resource-aware optimization framework that dynamically selects the MPI process count and performs node- and NUMA-aware process placement and reduces task-sequence execution time and improves the evaluated resource-utilization metrics by more than 30%.
Wenxiao Wang, Zi-Bo Gao, Guoding Ji et al.· Journal of Intelligent Compu...· 0 citations
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