Aug 2026· IEEE International Conference on Embedded and Real-Time Computing Systems and Applications· pp. 134-142· 0 citations· 18 references
Abstract
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.
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.
T. Rajkumar, Nishanth D., P. M et al.· Journal of Soft Computing Pa...· 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
STORM is presented, a NIC-level scheduler for all types of RDMA workloads using NIC-only information: the known RDMA request size, and per-queue-pair backlog, and converts these signals into a small number of extra priority levels on the wire and prioritizes requests that are either near completion or blocking queued dependent work.
Jichun Wu, Ran Shu, Gianni Antichi et al.· Conference on Applications,...· 0 citations
The rapid proliferation of Internet of Things (IoT) devices has intensified demands for low-latency, resource-efficient task scheduling at the network edge. Conventional policies such as Round-Robin and First-Come-First-Serve (FCFS) fail to satisfy the Quality-of-Service (QoS) requirements of Industrial-IoT and autonomous-vehicle workloads. This paper presents Multi-Queue Priority-Based Scheduling (MQPBS), a lightweight algorithm that classifies tasks into three dynamic priority queues (High, Medium, Low) using deadline-aware heuristics, applies Shortest-Job-First (SJF) intra-queue ordering, and employs an aging mechanism to prevent starvation. Extensive simulation over task sets of 200–1000 tasks demonstrates that MQPBS reduces average waiting time by up to 17.6%, improves throughput by up to 10.8%, lowers energy consumption by 20%, and cuts the Deadline Miss Ratio (DMR) compared with the Priority-Aware Task-Scheduling (PaTS) baseline. Ablation experiments confirm the independent contribution of each algorithmic component. Scalability and sensitivity analyses further validate the robustness of MQPBS under heterogeneous arrival patterns and varying load intensities. The results establish MQPBS as a scalable, reliable scheduler for next-generation edge infrastructures.
Shibang Maity, Roshan Panda, M. Tanisha et al.· International Conference on...· 0 citations
Adaptive CPU scheduling that responds to dynamic workload characteristics remains an open challenge for minimal operating systems. Traditional schedulers employ static parameters unsuitable for heterogeneous workloads, while recent machine learning approaches incur overhead inappropriate for resource-constrained environments. This paper presents Load-Aware Adaptive Scheduling (LAAS), a novel approach that integrates real-time system load classification with entropy-inspired process scoring to enable dynamic scheduling decisions. The core innovation—a load-dependent scoring formula where weight values adapt based on system load class—represents the first application of entropy principles to CPU process selection logic in a minimal kernel. LAAS achieves adaptation without machine learning runtime overhead (sub-0.5% CPU vs. 1% for ML-based approaches) and is suitable for minimal monolithic kernels (approximately 1,500 LOC total implementation). Experimental evaluation on synthetic workloads demonstrates 31% reduction in average waiting time, 28% reduction in context switches, and 18% improvement in CPU utilization compared to standard Round-Robin scheduling, while achieving a Jain fairness index of 0.94 in our test scenarios. These results indicate that load-aware, entropy-inspired adaptive scheduling is practically feasible in severely resource-constrained kernel environments.
Durgesh Kumar, Aviskha Talukdar, Nandani et al.· 2026 7th International Confe...· 0 citations
Modern HPC systems increasingly rely on tiered storage architectures with SSDs serving as a critical performance tier. However, the inherent asynchronous I/O characteristics of SSDs, including read/write bandwidth asymmetry and interference, pose significant challenges for traditional I/O schedulers. These challenges are exacerbated by the convergence of bursty HPC write workloads (e.g., checkpointing) and sustained AI read workloads (e.g., data streaming) on shared SSD infrastructure. Existing schedulers fail to adequately address these combined workloads, leading to suboptimal resource utilization. This paper introduces HARMONI, a heterogeneity-aware reinforcement learning scheduler for mixed I/O in HPC storage systems. HARMONI leverages a graph neural network (GNN) to encode task-SSD dependencies and a hybrid interference predictor to adapt to hardware and I/O variations. Experimental results across diverse HPC and AI workloads demonstrate that HARMONI significantly reduces average makespan by up to 90% compared to state-of-the-art schedulers, effectively bridging the gap between evolving storage hardware and the dynamic I/O demands of modern HPC systems.
Ze-Xi Cai, Tong Zhao, Shadi Ibrahim et al.· ACM Transactions on Architec...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
