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The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity. In order to meet the needs of an increasing number of researchers, it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment. In this paper, we propose a novel quantum computing paradigm, Virtual QPU (VQPU), which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity. The proposal introduces three innovative concepts: (1) The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput. (2) The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility. (3) The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity. The concepts have been validated through the utilization of a self-built simulated quantum cloud platform.

Scheduling computing is a well-studied area focused on improving task execution by reducing processing time and increasing system efficiency. Divisible Load Theory (DLT) provides a structured analytical framework for distributing partitionable computational and communicational loads across processors, and its adaptability has allowed researchers to integrate it with other models and modern technologies. Building on this foundation, previous studies have shown that Amdahl-like laws can be effectively combined with DLT to produce more realistic performance models. This paper further develops analytical models that further extend such integration by incorporating Gustafson’s Law and Juurlink’s Law into DLT to capture broader scaling behaviors. It also extends the analysis to workload distribution in virtual multicore systems, providing a more structured basis for evaluating parallel performance. Methods include analytically computing speedup as a function of the number of cores and the parallelizable fraction under different scheduling strategies, with comparisons across workload conditions. Results show that combining DLT with speedup laws and virtual core design offers a deeper and more structured approach for analytical parallel system evaluation. While the analysis remains theoretical, the proposed framework establishes a mathematical foundation for future empirical validation, heterogeneous workload modeling, and sensitivity analysis.

In recent years, the increasing adoption of High-Performance Computing (HPC) clusters in scientific research and engineering has exposed challenges such as resource imbalance, node idleness, and overload, which hinder scheduling efficiency. Accurate multidimensional task prediction remains a key bottleneck. To address this, we propose a hybrid prediction model that integrates Informer, Long Short-Term Memory (LSTM), and Graph Neural Networks (GNN), enhanced by a hierarchical attention mechanism combining multi-head self-attention and cross-attention. The model captures both long- and short-term temporal dependencies and deep semantic relationships across features. Built on a multitask learning framework, it predicts task execution time, CPU usage, memory, and storage demands with high accuracy. Experiments show prediction accuracies of 89.9%, 87.9%, 86.3%, and 84.3% on these metrics, surpassing baselines like Transformer-XL. The results demonstrate that our approach effectively models complex HPC workload dynamics, offering robust support for intelligent cluster scheduling and holding strong theoretical and practical significance.

Approximate computing is a promising approach to the design of area–power-performance-efficient circuits for computation error-tolerant applications such as image processing and machine learning. Approximate functional units, such as approximate adders and approximate multipliers, have been actively studied for the past decade, and some of these approximate functional units can dynamically change the degree of computation accuracy. The greater their computational inaccuracy, the faster they are. This study examined the high-level synthesis of approximate circuits that take advantage of such accuracy-controllable functional units. Scheduling methods based on integer linear programming (ILP) and list scheduling were proposed. Under resource and time constraints, the proposed method tries to minimize the computation error of the output value by selectively multi-cycling operations. Operations that have a large impact on the output accuracy are multi-cycled to perform exact computing, whereas operations with a small impact on the accuracy are assigned a single cycle for approximate computing. In the experiments, we explored the trade-off between performance, hardware cost, and accuracy to demonstrate the effectiveness of this work.

The Exponential Distribution Optimizer (EDO) is a newly developed mathematics-based metaheuristic with a simple structure and high efficiency. However, the EDO faces dilemmas, including poor initial population quality, premature convergence, insufficient population diversity, and low convergence accuracy when addressing complex high-dimensional optimization and cloud computing task scheduling problems. To overcome these drawbacks, this paper proposes an Adaptive Synergistic Exponential Distribution Optimizer (ASEDO) integrated with three collaborative strategies for global optimization and cloud computing task scheduling. First, a Multi-Source Hybrid Perturbation Initialization is designed using first-order differential mutation and high-order Bernstein polynomial perturbation to expand the initial search space and boost population diversity. Second, a Bipolar Adaptive Search Mechanism is presented to enable bidirectional learning from elite and inferior individuals, effectively preventing local optima trapping. Third, an Oscillating Random Mapping Learning Mechanism is introduced to strengthen local search ability and convergence precision via random learning and second-order oscillation mapping. The proposed ASEDO is verified on CEC2022 benchmark functions and cloud computing task scheduling under small-scale, large-scale, and dynamic task scenarios. Ablation experiments and comparison results demonstrate that the synergistic effect of the three strategies significantly improves the performance of EDO. Meanwhile, the ASEDO shows stronger global search capability, higher solution accuracy, and better stability than several state-of-the-art algorithms in both global optimization and cloud task scheduling applications.

Edge computing networks represent an emerging technological paradigm that enhances real-time responsiveness for mobile devices by reallocating computational resources from central servers to the network’s edge. This shift enables more efficient computing services for mobile devices. However, deploying computing services on inappropriate edge nodes can result in imbalanced resource utilization within edge computing networks, ultimately compromising service efficiency. Consequently, effectively leveraging the resources of edge computing devices while minimizing the energy consumption of terminal devices has become a critical issue in resource scheduling for edge computing. To tackle these challenges, this paper proposes a resource scheduling algorithm for edge computing networks based on multi-objective optimization. This approach utilizes the entropy weight method to assess both dynamic and static metrics of edge computing nodes, integrating them into a unified computing power metric for each node. This integration facilitates a better alignment between computing power and service demands. By modeling the resource scheduling problem in edge computing networks as a multi-objective Markov decision process (MOMDP), this study employs multi-objective reinforcement learning (MORL) and the proximal policy optimization (PPO) algorithm to concurrently optimize task transmission latency and energy consumption in dynamic environments. Finally, simulation experiments demonstrate that the proposed algorithm outperforms state-of-the-art scheduling algorithms in terms of latency, energy consumption, and overall reward. Additionally, it achieves an optimal hypervolume and Pareto front, effectively balancing the trade-off between task transmission latency and energy consumption in multi-objective optimization scenarios.

With the rapid development of power Internet of Things (IoT) scenarios such as smart factories and smart homes, numerous intelligent terminal devices and real-time interactive applications impose higher demands on computing latency and resource supply efficiency. Multi-access edge computing technology deploys cloud computing capabilities at the network edge; constructs distributed computing nodes and multi-access systems and offers infrastructure support for services with low latency and high reliability. Existing research relies on a strong assumption that the environmental state is fully observable and fails to thoroughly consider the continuous time-varying features of edge server load fluctuations, leading to insufficient adaptability of the model in a heterogeneous dynamic environment. Thus, this paper establishes a framework for end-edge collaborative task offloading based on a partially observable Markov decision-making process (POMDP) and proposes a method for end-edge collaborative task offloading in heterogeneous scenarios. It achieves time-series modeling of the historical load characteristics of edge servers and endows the agent with the ability to be aware of the load in dynamic environmental states. Moreover, by dynamically assessing the exploration value of historical trajectories in the central trajectory pool and adjusting the sample weight distribution, directional exploration and strategy optimization of high-value trajectories are realized. Experimental results indicate that the proposed method exhibits distinct advantages compared with existing methods in terms of average delay and task failure rate and also verifies the method’s robustness in a dynamic environment.

In this paper, a four-stage service model is constructed by combining M/M/1, MX/M/1 and M/MB/1 queuing systems. In addition, the immediate scheduling strategy and its algorithm are presented in detail, and the computing accomplished scheduling strategy and its algorithm are proposed. Approaches for computing the receiving time, preprocessing time, operating time and transmission time of operation requests that are based on various queuing systems are discussed, and the response time is calculated by adding these times together. Finally, the response times under the two scheduling strategies are obtained by simulating the models numerically, and the results demonstrate that the proposed computing accomplished scheduling strategy outperforms the immediate scheduling strategy.

PurposeWith the continuous technological development of automated driving and expansion of its application scope, the types of on-board equipment continue to be enriched and the computing capabilities of on-board equipment continue to increase and corresponding applications become more diverse. As the applications need to run on on-board equipment, the requirements for the computing capabilities of on-board equipment become higher. Mobile edge computing is one of the effective methods to solve practical application problems in automated driving.Design/methodology/approachIn this study, in accordance with practical requirements, this paper proposed an optimal resource management allocation method of autonomous-vehicle-infrastructure cooperation in a mobile edge computing environment and conducted an experiment in practical application.FindingsThe design of the road-side unit module and its corresponding real-time operating system task coordination in edge computing are proposed in the study, as well as the method for edge computing load integration and heterogeneous computing. Then, the real-time scheduling of highly concurrent computation tasks, adaptive computation task migration method and edge server collaborative resource allocation method is proposed. Test results indicate that the method proposed in this study can greatly reduce the task computing delay, and the power consumption generally increases with the increase of task size and task complexity.Originality/valueThe results showed that the proposed method can achieve lower power consumption and lower computational overhead while ensuring the quality of service for users, indicating a great application prospect of the method.

With satellite systems rapidly developing in multiple satellites, multiple tasks, and high-speed response speed requirements, existing computing techniques face the following challenges: insufficient computing power, limited computing resources, and weaker coordination ability. Meanwhile, most methods have more significant response speed and resource utilization limitations. To solve the above problem, we propose a distributed collaborative computing framework with a genetic algorithm-based task scheduling model (DCCF-GA), which can realize the collaborative computing between multiple satellites through genetic algorithm. Specifically, it contains two aspects of work. First, a distributed architecture of satellites is constructed where the main satellite is responsible for distribution and scheduling, and the computing satellite is accountable for completing the task. Then, we presented a genetic algorithm-based task scheduling model that enables multiple satellites to collaborate for completing the tasks. Experiments show that the proposed algorithm has apparent advantages in completion time and outperforms other algorithms in resource efficiency.