Jul 2026· ACM Transactions on Design Automation of Electronic Systems· 0 citations· 27 references
TL;DR
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.
Abstract
Heterogeneous multicore embedded systems can handle complex tasks such as data collection, processing, and wireless communication. However, these systems often face constraints due to limited computational resources, requiring efficient task deployment strategies to manage dependent and real-time tasks. Despite advancements, existing methods rarely address the deployment of Imprecise Computation (IC) tasks on heterogeneous multicore platforms that utilize both Dynamic Voltage/Frequency Scaling (DVFS) and Dynamic Power Management (DPM). This paper introduces a novel QoS-aware task deployment methodology to enhance the Quality of Service (QoS) under resource limitations. The proposed approach simultaneously optimizes frequency assignment, processor state transitions, task allocation, scheduling, and task execution cycle adjustments to maximize system QoS while adhering to time, energy, and dependency constraints. The problem is initially formulated as a Mixed Integer Non-Linear Programming (MINLP) model, then transformed into a Mixed Integer Linear Programming (MILP) formulation to obtain optimal solutions. We developed a heuristic method with low computational complexity to address scalability challenges, employing problem decomposition and a greedy algorithm. The process operates in two stages: task allocation and frequency assignment, followed by task scheduling and cycle adjustment. The simulation results demonstrate that the proposed method achieves superior system performance compared to existing approaches, particularly in QoS ( \(41.30\% \) ), task schedulability ( \(58.67\% \) ), and execution efficiency.
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
We consider the problem of cost-effectively mapping a swarm of soft real-time stream processing applications with moldable-parallel tasks to multicore resources in the device-edge-cloud continuum, consisting of mobile devices, edge resources and cloud resources. We leverage flexibility from different parallelization degrees and frequency levels (DVFS) for the tasks, keeping application throughput constraints and communication bandwidth limitations while minimizing overall cost (including device/edge resource energy, communication cost and cloud resource renting). We present several offline algorithmic solutions with a global view of the environment: an integer linear program (ILP) extending the crown scheduling approach for multi-layer distributed systems, a variant leveraging symmetries in application and system structure, and a greedy heuristic algorithm. We also expanded the problem formulation to consider the dynamic joining of application task graphs, introducing a dynamic approach based on the proposed ILP and greedy heuristic algorithm. Our experimental evaluation for several real-world and synthetic scenarios shows that the time required for solving the scheduling problem to cost-optimality by the ILP is feasible for nontrivial scenarios. The heuristic achieves about 3% worse cost efficiency on average, yet operates much faster (by 1–2 orders of magnitude), allowing to scale up the problem size more than the ILP approach. The symmetry-folding applied to the ILP approach improves its optimization time by about 1 order of magnitude, at the expense of less than a 5% increase in cost compared to a non-folded static ILP solution. The heuristic is likewise accelerated by leveraging symmetry, though to a minor extent. The dynamic incremental variant of the ILP approach reduces the long optimization time of the static ILP method with a minor cost penalty compared to a clean-slate offline solution.
Sajad Khosravi, Christoph W. Kessler, Sebastian Litzinger et al.· Cluster Computing· 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
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.
Igiri C. G, Ejekwu Obunezi, Ujah Alechenu Israel· Journal of Artificial Intell...· 0 citations
The rapid growth of Internet-of-Things (IoT) devices has increased the need for computing support close to end users, particularly for applications that cannot tolerate long processing delays or excessive energy consumption. Fog computing has emerged as a practical extension of the cloud to address these requirements, yet real deployments often involve a mix of devices with different processing abilities, communication characteristics, and power constraints. These differences make it difficult to decide when and where tasks should be offloaded.
This study introduces a task-offloading approach that adapts to changing conditions in a heterogeneous fog environment. The method continuously observes factors such as processor utilization, task size, communication delay, and the remaining energy of participating devices. Using this information, the system determines whether a task should run on the originating device, a nearby fog node, or the cloud. The approach aims to limit unnecessary transfers while striking a balance between energy use and execution delay.
Simulation experiments conducted in iFogSim indicate that the proposed strategy consistently improves performance over conventional static or energy-unaware schemes. The results show notable reductions in overall energy usage and significant improvements in task-completion success under varying network loads. These findings suggest that integrating real-time monitoring with adaptive decision-making can strengthen the efficiency and responsiveness of fog-based IoT systems.
Ashish Bagla, Deepak Dagar, Pratik Srivastava· International Journal For Mu...· 0 citations
A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.