2026· Journal of Artificial Intelligence and Emerging Technologies· 0 citations
TL;DR
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).
Abstract
Heterogeneous multicore architectures encompassing Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Field-Programmable Gate Arrays (FPGAs) have emerged as the dominant computational paradigm for high-performance and embedded workloads. Task scheduling across such architectures presents a formidable challenge: the assignment of computational tasks to heterogeneous processing elements must satisfy precedence constraints whilst minimising overall workflow completion latency. Classical scheduling heuristics, including Earliest Finish Time (EFT) and Heterogeneous Earliest Finish Time (HEFT), offer polynomial-time approximations yet demonstrate limited adaptability under dynamic runtime conditions, frequently yielding suboptimal execution latency in large-scale task graphs. This paper proposes the Latency-Aware Adaptive Spotted Hyena Optimizer (LA-ASHO), a novel metaheuristic scheduling framework grounded in the social hunting behaviour of spotted hyenas. The framework encodes task-to-core assignments as discrete permutation vectors and evaluates fitness exclusively through a mathematically rigorous execution latency model that integrates computation time, memory overhead, and inter-core communication delays. Comprehensive simulation experiments conducted over 100 to 1,000 tasks across 8 to 64 heterogeneous cores, each repeated across 30 statistically independent runs demonstrate that LA-ASHO achieves statistically significant reductions in workflow completion latency relative to established baseline schedulers such as; Min-Min, Heterogeneous Earliest Finish Time (HEFT). The principal contribution of this work is the formulation of an execution-latency-focused metaheuristic framework that is both theoretically grounded and practically scalable for real-world heterogeneous computing deployments.
A novel QoS-aware task deployment methodology to enhance the Quality of Service (QoS) under resource limitations is introduced and results demonstrate that the proposed method achieves superior system performance compared to existing approaches.
Haotong Zhu, Lei Mo, T. Al-Hasan et al.· ACM Transactions on Design A...· 0 citations
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.
M. Yacoub, A. Raouf, Walaa K. Gad et al.· Electronics· 0 citations
Efficient energy scheduling in heterogeneous computing environments is a critical challenge, as task allocation decisions directly affect both energy consumption and execution performance. This work presents an energy aware scheduling framework based on a discretized grasshopper optimization algorithm (GOA), designed to balance energy reduction with acceptable makespan. The model formulates scheduling as a constrained objectives optimization problem, incorporating energy use, makespan, heterogeneous resource capacities, workflow precedence, and non preemptive execution. A constant aware representation and repair based decoding strategy enable GOA to generate feasible task to resources assignments. Implemented in Python, the framework is evaluated against HEFT, Min and Random scheduling under varying workload. Results show that the schedules based on GOA achieves lower energy consumption and improved performance delay energy while maintaining competitive makespan, with performance gains becoming more pronounced as workload complexity increases. These findings demonstrate the scalability and effectiveness of discretised GOA as a metaheuristic solution for energy aware scheduling in heterogeneous systems.
Macauley Opuwari, C. Igiri, D. Ikeh· International Journal Of Eng...· 0 citations
Energy-aware task scheduling in heterogeneous cloud infrastructures remains challenging due to the combinatorial growth of task-to-resource assignments, resource heterogeneity, and the need to balance energy consumption with scheduling performance. This paper proposes an Adaptive Dominance-Guided Grey Wolf Optimizer (ADG-GWO) for non-preemptive task scheduling in heterogeneous cloud environments. ADG-GWO adapts Grey Wolf Optimization to discrete task-to-VM assignment by integrating dominance-guided genetic reproduction, Hamming-distance-based diversity regulation, and adaptive reproduction control. These mechanisms are designed to improve search stability, reduce premature convergence, and support effective exploration of high-dimensional assignment spaces without expanding the externally tuned hyperparameter space.The proposed method is evaluated through simulation under workload-scaling and capacity-scaling scenarios using heterogeneous cloud configurations. For evaluation, workload instances and heterogeneous VM configurations are derived from Google Cluster Trace 2019 to instantiate realistic task-to-VM scheduling scenarios. The results show that the proposed dominance-guided adaptive search improves energy-aware scheduling effectiveness while maintaining competitive scheduling efficiency in heterogeneous cloud environments.
Saleh Al Shamaa, Wei Shi, J. Corriveau· IEEE International Conferenc...· 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
Modern clustered (power-island) multicore processors increasingly expose retention as a low-overhead low-power state, yet exploiting it in real-time systems critically depends on whether the scheduler can create sufficiently long cluster-level all-cores-idle windows that exceed a break-even length. This feasibility bottleneck is particularly acute for always-on arbitrary-deadline Directed Acyclic Graph (DAG) workloads, where precedence-induced execution fluctuations fragment idle times and the relative deadline may exceed the period. This paper proposes a retention-oriented scheduling framework for such workloads, consisting of two algorithms with a clear progression. PRES performs design-time planning and constructs a postponable division for each DAG, reshaping execution to aggregate slack into contiguous cluster-level idle windows; a lightweight event-driven runtime then enforces the plan and triggers retention without increasing online scheduling complexity. Building on PRES, PREHS further improves core utilization via multi-DAG merging and intra-cluster spatial sharing, consolidating workload onto fewer active clusters to reduce static/leakage energy while preserving deadline guarantees. We conduct extensive experiments across diverse cluster configurations and DAG structural characteristics, and further include a case study based on an application-derived DAG from a real-world communication/signal-processing pipeline. The results show that, compared with a representative work-conserving baseline with automatic retention/PG, PREHS reduces static energy consumption by up to 65.62%; moreover, under the same resource constraints, PREHS improves task acceptance ratio over PRES by up to 43 percentage points, with the largest gains observed in resource-tight configurations.
Xiangzhen Xiao, Weijie Wang, Weichen Liu et al.· ACM Transactions on Embedded...· 0 citations