Bridging Stochastic Interference and Deterministic Scheduling: An Automated Design Framework for Heterogeneous Embedded Systems

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.

Toward an Integrated Theory of Adaptive Scheduling in High Performance Computing: A Queuing-Theoretic and Computational Learning Perspective.

High Performance Computing (HPC) systems increasingly operate under heterogeneous workloads, dynamic resource availability, and stringent performance and energy constraints. Traditional batch scheduling policies such as First-Come First-Served (FCFS), backfilling, and priority-based heuristics rely on static assumptions about job behavior and system state, often leading to suboptimal utilization and long waiting times in highly variable environments. This paper proposes an integrated theoretical framework for adaptive HPC scheduling that unifies queuing-theoretic models with computational learning techniques. By interpreting job arrivals and service processes through stochastic queues while enabling scheduling decisions to evolve via data-driven learning, we establish a principled basis for adaptive schedulers that can respond to workload uncertainty. We outline the mathematical foundations of this approach, discuss learning-augmented scheduling policies, and present illustrative scenarios demonstrating how adaptive strategies can outperform static heuristics in terms of mean response time, fairness, and system utilization. This work aims to bridge the gap between analytical scheduling theory and practical intelligent resource management in HPC systems.

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

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).

SPES: A Stochastic Predictive Energy-Aware Scheduling Approach for Efficient Multi-Region Cloud Computing

Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency.

Probabilistic Schedulability Analysis for Mixed-Criticality DAG Tasks on Multiprocessors