Computation Scheduling Research Articles

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

Retraction Note: Computational grid scheduling architecture using MapReduce model-based non-dominated sorting genetic algorithm

Retraction Note: Computational grid scheduling architecture using MapReduce model-based non-dominated sorting genetic algorithm

Dynamic computation scheduling for hybrid energy mobile edge computing networks

The rapid expansion of Mobile Edge Computing (MEC) is driven by the growing demand for resource-intensive applications within the Internet of Things. Computation offloading allows these applications to be executed at the network edge, but it leads to significant electricity expenses for network operators. To mitigate these costs, one promising solution is to power base stations (BSs) with a hybrid energy supply that combines unpredictable harvested energy with stable energy from the smart grid. This paper investigates joint computation scheduling for mobile devices (MDs) and resource allocation in a MEC network incorporating hybrid energy sources. Our objective is to maximize long-term time-averaged service utility by optimizing parameters such as BS battery supply, harvestable energy, CPU frequency, transmission power, task-partition factor, and MD-BS associations. To tackle this complex problem, we exploit the Lyapunov optimization framework to decompose it into deterministic subproblems for each time slot and propose an online network service utility maximization scheduling (NSUMS) algorithm. Experimental results show that our algorithm outperforms benchmark schemes in service utility and energy expenditure, improving the completion ratio by 32%, reducing the failure rate by 80%, and decreasing MD energy consumption by 28%.

A genetic algorithm‐based virtual machine scheduling algorithm for energy‐efficient resource management in cloud computing

SummaryTo address the unbalanced resource load of a virtual machine cluster, the author proposes an energy‐saving virtual machine scheduling algorithm based on resource management cloud computing technology. This article analyzes the current cloud computing and virtual machine scheduling research in the cloud computing environment. It discusses the concept, characteristics, classification, application scenarios, and key cloud computing technologies. A genetic algorithm is used to solve the problem of high energy consumption in the data center. The test results show that in the same original configuration scheme, the migration times based on the greedy algorithm adopted by GA2ND are about 1000, and the migration times of GA1ST are between 200 and 500. The GA2ND migration scheme requires fewer virtual machines. In the result analysis, the experiments compare the proposed algorithms—DVFS, IMC, GA1ST, and GA2ND—with a focus on energy consumption and virtual machine migration. Notably, DVFS serves as a reference for energy efficiency, IMC represents the proposed algorithm without genetic optimization, GA1ST denotes the genetic algorithm under a heterogeneous model, and GA2ND signifies the enhanced genetic algorithm introduced in this article. The comparison aims to assess the energy efficiency and virtual machine migration performance of each algorithm in the context of a simulated cloud computing environment. Therefore, the algorithm proposed in this article can effectively reduce energy consumption and avoid frequent migration of virtual machines.

Priority-weight-based computing and spectrum resource scheduling for dependent tasks in EON-supported computing first networks

The computing first network (CFN) supported by the elastic optical network (EON) is proposed as an innovative information infrastructure, where end users can access computing resources on demand. However, since the link capacity is limited, the network capacity bottleneck may cause task offloading failures even with sufficient computing resources. This paper proposes a cost-efficient computing and spectrum resource scheduling scheme to solve the above problem. The goal is to determine offloading decisions for dependent tasks by minimizing the completion delay and the energy consumption on computing nodes powered by non-green energy (i.e., non-green energy consumption). A priority-weight-based computing and spectrum resource scheduling (PW-CSRS) algorithm is proposed, which can sort the execution order of sub-tasks and optimize computing node selection and spectrum allocation. PW-CSRS is compared with three benchmarks in the 52-node and 33-node systems, and simulation results show that PW-CSRS can effectively improve the acceptance ratio and reduce non-green energy consumption.

Reinforcement Contract Design for Vehicular-Edge Computing Scheduling and Energy Trading via Deep Q-Network With Hybrid Action Space

Reinforcement Contract Design for Vehicular-Edge Computing Scheduling and Energy Trading via Deep Q-Network With Hybrid Action Space

Minimizing Age-of-Information With Joint Transmission and Computing Scheduling in Mobile-Edge Computing

Minimizing Age-of-Information With Joint Transmission and Computing Scheduling in Mobile-Edge Computing

Co-Design of Control, Computation, and Network Scheduling Based on Reinforcement Learning

Co-Design of Control, Computation, and Network Scheduling Based on Reinforcement Learning

Energy-Efficient Communication and Computing Scheduling in UAV-Aided Industrial IoT

Energy-Efficient Communication and Computing Scheduling in UAV-Aided Industrial IoT

Research on Informatization Construction of Educational Management Innovation Path in Colleges and Universities in the Intelligent Era

Abstract The prevailing education management systems in colleges and universities are antiquated, adversely impacting not only the administrative efficiency but also the formulation of faculty teaching plans and class schedules, as well as the management of student electives and credit accrual. This paper presents an innovation and optimization of the existing system, wherein education management processes are categorized into seven distinct modules. It delineates the flow and integration of heterogeneous data sources within the system, leveraging heterogeneous database technology as the foundational framework. Subsequently, we redesign heterogeneous computation scheduling and adaptation, enhancing distributed database allocation and query algorithms. The culmination of this research is the development of a novel pathway for the education management information system in higher education institutions. Upon empirical testing of the system proposed in this paper, we explore its practical implications through IPA analysis. User satisfaction is notably high in areas such as A4 invigilation information, A13 subjective grading of routine assessments, A14 querying and feedback on grades, and A15 remediation of failed courses, with performance ratings of 4.77008, 4.73981, 4.8961, and 4.86558 respectively. These indices predominantly reside in the “continue to maintain” and “oversupply” zones, indicating the substantial efficacy of the innovative college management information system introduced herein. This study significantly contributes to the standardization and intellectualization of college administrative processes, proposing new paradigms and methodologies for the integration of educational management and information technology within tertiary institutions.

A novel hierarchical distributed vehicular edge computing framework for supporting intelligent driving

Recently, various infrastructure-assisted or onboard driving assistant applications have been proposed as a component of intelligent transportation systems (ITS) to improve the transportation system’s efficiency and release public concern about road safety. However, such AI-assisted intelligent applications are mainly data-driven and put great demands on the computing power of the ITS systems. Therefore, in the highly dynamic Internet-of-Vehicles environment in ITS, how to effectively coordinate the limited computing power of the various components of the system and realize reliable support for such resource-consuming applications through efficient resource allocation methods is the focus of our research. Accordingly, a novel joint computing and communication resource scheduling method is proposed to fulfill those ITS applications’ inherent heterogeneous quality of service (QoS) requirements. By fully exploiting the computing resources provided by the onboard computing device, the edge computing device located in the vehicle’s proximity and remote data center, we designed a hierarchical three-layer Vehicular Edge Computing (VEC) framework. Briefly, an onboard joint computation offloading and transmission scheduling policy is designed to assign corresponding offloading decisions to the locally generated computing tasks by considering the vehicle’s computing resources and real-time network link status. Additionally, a new distributed resource allocation policy is developed for the edge devices, in which we derive a server selection policy and allocate communication time based on our proposed metric. To evaluate the performance and validate the effectiveness of our proposed method, we conduct extensive simulation tests and ablation experiments, respectively. The results show that our approach can achieve stable performance in various experimental settings. Also, compared to the state-of-the-art algorithms, our joint resource allocation policy significantly reduces the scheduling overhead, improves the utilization of system resources, and minimizes the data transmission delay caused by vehicle motion.

Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm

With the development of computationally intensive applications, the demand for edge cloud computing systems has increased, creating significant challenges for edge cloud computing networks. In this paper, we consider a simple three-tier computational model for multiuser mobile edge computing (MEC) and introduce two major problems of task scheduling for federated learning in MEC environments: (1) the transmission power allocation (PA) problem, and (2) the dual decision-making problems of joint request offloading and computational resource scheduling (JRORS). At the same time, we factor in server pricing and task completion, in order to improve the user-friendliness and fairness in scheduling decisions. The solving of these problems simultaneously ensures both scheduling efficiency and system quality of service (QoS), to achieve a balance between efficiency and user satisfaction. Then, we propose an adaptive greedy dingo optimization algorithm (AGDOA) based on greedy policies and parameter adaptation to solve the PA problem and construct a binary salp swarm algorithm (BSSA) that introduces binary coding to solve the discrete JRORS problem. Finally, simulations were conducted to verify the better performance compared to the traditional algorithms. The proposed algorithm improved the convergence speed of the algorithm in terms of scheduling efficiency, improved the system response rate, and found solutions with a lower energy consumption. In addition, the search results had a higher fairness and system welfare in terms of system quality of service.

Fairness-aware task offloading and load balancing with delay constraints for Power Internet of Things

The rapid development of the Power Internet of Things (PIoT) enables power smart sensing devices to offload their computation tasks to nearby edges. However, due to the increasing demand for computing and the unbalanced spatial distribution of devices, it is imperative to seek cooperative computing and task scheduling optimization schemes at the edge for PIoT. In this paper, we develop a two-tier cooperative edge network paradigm in PIoT. Then, we define a novel fairness indicator based on the Theil index to measure the allocation balance of the system. We also formulate a fairness and delay guaranteed (FDG) task offloading and load balancing optimization problem, which aims to minimize the allocation difference of the edge network while satisfying the delay constraints for multiple tasks in PIoT. Moreover, we develop a Lyapunov optimization and whale optimization algorithm (LWOA) to solve the problem. The simulation results demonstrate that for two types of typical tasks in PIoT, compared with the NonB scheme, the proposed FDG scheme decreases the time-averaged allocation difference of the system by 10% and 35%, the time-averaged allocation difference within subsystems by 5% and 6%, the time-averaged delay by approximately 5% and 7%, and the time-averaged queue backlog by approximately 30% and 40%. Research, both theoretical and experimental, has demonstrated that cooperation at the edge can significantly improve the performance of PIoT.

Design of Edge-IoMT Network Architecture with Weight-Based Scheduling.

Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting sensor data with a cluster topology, forwarding information through relay nodes, and applying edge computing and transmission scheduling for network scalability and operational efficiency. The proposed distributed network architecture incorporates data compression technologies and effective scheduling algorithms for handling the transmission scheduling of various physiological signals. Compared to existing scheduling mechanisms, the experimental results depict the network performance and show that in analyzing the delay and jitter, the proposed WFQ-based algorithms have reduced the delay and jitter ratio by about 40% and 19.47% compared to LLQ with priority queueing scheme, respectively. The experimental results also demonstrate that the proposed network topology is more effective than the direct path transmission approach in terms of energy consumption, which suggests that the proposed network architecture may improve the development of medical applications with body area networks such that the goal of self-organizing population health monitoring can be achieved.

Dynamic Multiworkflow Offloading and Scheduling Under Soft Deadlines in the Cloud-Edge Environment

Computing offloading and scheduling are emerging, accompanied by smart city services, Internet of Things, and other rich cloud services. Cloud-assisted mobile edge computing provides malleable resources but introduces considerable overhead. Due to the geographical distributions and distances of the edge, cloud, and mobile terminal, promoting cost efficiency and reducing the makespan of applications are urgent targets. For multiple mobile applications, the soft deadline constraint for each application is common in the edge-cloud environment. When multiple users initiate application requests, scheduling on the target offloaded platform commences. Some workflows, especially newly arrived workflows, might suffer from “starving to death,” which has the risk of being missed. This article describes a dynamic, multiworkflow offloading, and scheduling, adaptive heterogeneous earliest-finish-time-based algorithm, which can lightly run on a distributed network. We explore an innovative model composed of a multiobjective makespan and cost for a soft deadline constraint optimization problem. Abundant data are adopted to test the offloading and scheduling effect on a variety of platform parameters, and comparative experiments demonstrate the validity and efficiency of the adaptive system.

Privacy preserving federated learning for full heterogeneity

Federated learning is a novel distribute machine learning paradigm to support cooperative model training among multiple participant clients, where each client keeps its private data locally to protect its data privacy. However, in practical application domains, Federated learning still meets several heterogeneous challenges such data heterogeneity, model heterogeneity, and computation heterogeneity, significantly decreasing its global model performance. To the best of our knowledge, existing solutions only focus on one or two challenges in their heterogeneous settings. In this paper, to address the above challenges simultaneously, we present a novel solution called Full Heterogeneous Federated Learning (FHFL). Firstly, we propose a synthetic data generation approach to mitigate the Non-IID data heterogeneity problem. Secondly, we use knowledge distillation to learn from heterogeneous models of participant clients for model aggregation in the central server. Finally, we produce an opportunistic computation schedule strategy to exploit the idle computation resources for fast-computing clients. Experiment results on different datasets show that our FHFL method can achieve an excellent model training performance. We believe it will serve as a pioneer work for distributed model training among heterogeneous clients in Federated learning.

Independent tasks scheduling of collaborative computation offloading for SDN-powered MEC on 6G networks

Independent tasks scheduling of collaborative computation offloading for SDN-powered MEC on 6G networks

Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN

To satisfy the continuously high energy consumption and high computational capacity requirements for IoT applications, such as video monitoring, we integrate solar harvesting and multi-access edge computing (MEC) technologies to develop a solar-powered MEC system. Considering the stochastic nature of solar arrivals and channel conditions, we formulate a stochastic optimization problem to maximize network energy efficiency under the constraints of energy queue stability, task queue stability, peak transmission power, and maximum CPU frequency of each sensor. To solve the long-term stochastic optimization problem, we propose a Lyapunov-based online joint computational offloading and resource scheduling optimization algorithm, transforming the long-term stochastic problem into a series of deterministic subproblems in each time slot. Simulation results show that the proposed algorithm can find the optimal solution to tradeoff long-term energy efficiency and queueing backlog without requiring a priori knowledge of the channel state and energy arrival, which is a more realistic solution for practical solar-powered MEC systems.

Heterogeneous Computational Resource Allocation for NOMA: Toward Green Mobile Edge-Computing Systems

Mobile Edge Computing (MEC) is a viable solution in response to the growing demand for broadband services in the new-generation heterogeneous systems. The dense deployment of small cell networks is a key feature of next-generation radio access networks aimed at providing the necessary capacity increase. Nonetheless, the problem of green networking and service computing will be of great importance in the downlink, because the uncontrolled installation of too many small cells may increase operational costs and emit more carbon dioxide. In addition, given the resource and computational limitation of the user layer, energy efficiency (EE) and fairness assurance are critical issues in MEC-based cellular systems. Considering the user fairness criteria, this paper proposes a dynamic optimization model which maximizes the total UL/DL EE along with satisfying the necessary QoS constraints. Based on the non-convex characteristics of the EE maximization problem, the mathematical model can be divided into two separate subproblems, i.e., computational carrier scheduling and resource allocation. So that, a subgradient method is applied for the computational resource allocation and also successive convex approximation (SCA) and dual decomposition methods are adopted to solve the max-min fairness problem. The simulation results exhibit considerable EE improvement for various traffic models in addition to guaranteeing the fairness requirements. It also proved that the proposed computational partitioning scheme managed to significantly improve the total throughput for mobile computing services.

A modular fuzzy expert system for chemotherapy drug dose scheduling

This study presents a modular Fuzzy Expert System (FES) for chemotherapy drug dose scheduling. The computational model considers the patient’s Body Surface Area (BSA) and experts’ opinions to calculate chemo doses following the clinical practice. A proper balance between reducing cancerous cells and toxic side effects is required for effective drug scheduling. Still, in many cases, traditional clinical approaches fail to determine appropriate therapeutical doses that balance all restrictions. In our proposed system, FES-1 is developed to determine primary drug doses based on experts’ opinions and competing treatment objectives. To adjust the dose, FES-2 is developed based on clinical practices, the patient’s BSA, and experts’ opinions. The final chemotherapy drug dose schedule is generated by combining the outputs of FES-1 and FES-2, which is the proposed modular FES. A growth model is used in this work to observe response due to administration of chemotherapy drug doses and to determine the following doses by considering cancer patients’ three weight patterns (increasing, decreasing, and random order). Extensive simulation results and comparative assessment with other current computational chemotherapy drug scheduling models validate the effectiveness and the superiority of the model proposed in this study over the other methods reported in relevant studies.