Retention-Based Energy-Efficient and High-Core-Utilization Scheduling for Arbitrary-Deadline DAGs

Adaptive Spotted Hyena Optimizer for Latency-Aware Task Scheduling in Heterogeneous Multicore Systems

The Latency-Aware Adaptive Spotted Hyena Optimizer (LA-ASHO) is proposed, a novel metaheuristic scheduling framework grounded in the social hunting behaviour of spotted hyenas that achieves statistically significant reductions in workflow completion latency relative to established baseline schedulers such as; Min-Min, Heterogeneous Earliest Finish Time (HEFT).

Multi-Queue Priority-Based Scheduling for Real-Time Edge Computing Applications

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.

Probabilistic Schedulability Analysis for Mixed-Criticality DAG Tasks on Multiprocessors

Hierarchical Scheduler with Adaptive Time-Budget Reallocation for Time-Triggered Edge-Fog-Cloud Architectures

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.

Hardware-Assisted Context Preservation for Deterministic Task Switching in a RISC-V Real-Time Execution Model

Temporal predictability in real-time preemptive embedded systems is strongly influenced by how execution contexts are interrupted and resumed. This paper investigates whether hardware-assisted context preservation can reduce the temporal jitter introduced by conventional task switching in a RISC-V real-time execution model. A controlled comparative evaluation is performed between a standard shared-pipeline execution configuration and a context-preserving multi-context execution configuration under identical scheduler-driven periodic workloads. The study focuses on determinism-oriented task switching indicators, including deadline misses, preemption counts, switch-related flush events, and accumulated switch overhead. Four control-oriented experimental scenarios are used to expose progressively different switching conditions, ranging from baseline periodic execution to frequent preemption, near-saturation behavior, and tight-deadline pressure. The results show that preserving execution context across preemptions consistently reduces switching overhead and improves deadline-related behavior under recurring real-time activations. The benefits of context-preserving execution become progressively more visible under elevated preemption pressure, reduced timing slack, and tighter deadline conditions. The study therefore provides a controlled comparative basis for analyzing deterministic task switching in RISC-V real-time execution models.

CacheFlow: Using Maximum Flow to Bound Cache-Based Preemption Delays