Resource Allocation Scheduling Research Articles

A hierarchical adaptive federated reinforcement learning for efficient resource allocation and task scheduling in hierarchical IoT network

The increasing demand for processing numerous data from IoT devices in a hierarchical IoT network drives researchers to propose different resource allocation methods in the edge hosts efficiently. Traditional approaches often compromise on one of these aspects: either prioritizing local decision-making at the edge, which lacks global system insights or centralizing decisions in cloud systems, which raises privacy concerns. Additionally, most solutions do not consider scheduling tasks at the same time to effectively complete the prioritized task accordingly. This study introduces the hierarchical adaptive federated reinforcement learning (HAFedRL) framework for robust resource allocation and task scheduling in hierarchical IoT networks. At the local edge host level, a primal–dual update based deep deterministic policy gradient (DDPG) method is introduced for effective individual task resource allocation and scheduling. Concurrently, the central server utilizes an adaptive multi-objective policy gradient (AMOPG) which integrates a multi-objective policy adaptation (MOPA) with dynamic federated reward aggregation (DFRA) method to allocate resources across connected edge hosts. An adaptive learning rate modulation (ALRM) is proposed for faster convergence and to ensure high performance output from HAFedRL. Our proposed HAFedRL enables the effective integration of reward from edge hosts, ensuring the alignment of local and global optimization goals. The experimental results of HAFedRL showcase its efficacy in improving system-wide utility, average task completion rate, and optimizing resource utilization, establishing it as a robust solution for hierarchical IoT networks.

Enhancing Cloud Task Scheduling with Multi-Objective Optimization Using K-Means Clustering and Dynamic Resource Allocation

The scheduling and resource allocation procedure is an essential component of cloud resource management. Effective resource allocation is severely hampered by the task arrival rates' erratic and unclear behavior. To prevent under or overusing resources, an effective scheduling strategy is necessary. To improve scheduling and allocation performance, a multi-objective optimization technique is presented for the best resource allocation and task scheduling inside scientific workflow datasets in a heterogeneous environment. In the first stage, the system calculates four key metrics: Communication Cost, Computation Cost, Earliest Finished Time on a particular VM, and Total Task Length for a specific scientific workflow dataset. These metrics provide a comprehensive understanding of the resource requirements and help make informed scheduling decisions. In the second stage, tasks are clustered using the K-Means clustering algorithm. This clustering groups similar tasks together, making managing and scheduling them easier. In the third stage, the proposed resource allocation algorithm allocates the clustered tasks to the appropriate VMs. This step ensures that the tasks are assigned to the best-suited resources, optimizing the overall system performance and resource utilization. By following this multi-stage process, the system aims to achieve optimal resource allocation and task scheduling, thereby improving the efficiency and effectiveness of cloud resource management. The proposed method significantly outperforms PSO, CSO, and GWO by consistently achieving lower Makespan—under 400 units at 50 tasks—while maintaining high resource utilization rates above 0.75, demonstrating superior efficiency in task execution and resource management.

Multi-Layer Objective Model and Progressive Optimization Mechanism for Multi-Satellite Imaging Mission Planning in Large-Scale Target Scenarios

With the continuous increase in the number of in-orbit satellites and the explosive growth in the demand for observation targets, satellite resource allocation and mission scheduling are faced with the problems of declining benefits and stagnant algorithm performance. This work proposes a progressive optimization mechanism and population size adaptive strategy for an improved differential evolution algorithm (POM-PSASIDEA) in large-scale multi-satellite imaging mission planning to address the above challenges. (1) MSIMPLTS based on Multi-layer Objective Optimization is constructed, and the MSIMPLTS is processed hierarchically by setting up three sub-models (superstructure, mesostructure, and understructure) to achieve a diversity of resource selection and step-by-step refinement of optimization objectives to improve the task benefits. (2) Construct the progressive optimization mechanism, which contains the allocation optimization, time window optimization, and global optimization phases, to reduce task conflicts through the progressive decision-making of the task planning scheme in stages. (3) A population size adaptive strategy for an improved differential evolution algorithm is proposed to dynamically adjust the population size according to the evolution of the population to avoid the algorithm falling into the local optimum. The experimental results show that POM-PSASIDEA has outstanding advantages over other algorithms, such as high task benefits and a high task allocation rate when solved in a shorter time.

Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing

AbstractThe most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model.

Resource allocation scheduling scheme for task migration and offloading in 6G Cybertwin internet of vehicles based on DRL

AbstractAs vehicular technology advances, intelligent vehicles generate numerous computation‐intensive tasks, challenging the computational resources of both the vehicles and the Internet of Vehicles (IoV). Traditional IoV struggles with fixed network structures and limited scalability, unable to meet the growing computational demands and next‐generation mobile communication technologies. In congested areas, near‐end Mobile Edge Computing (MEC) resources are often overtaxed, while far‐end MEC servers are underused, resulting in poor service quality. A novel network framework utilizing sixth‐generation mobile communication (6G) and digital twin technologies, combined with task migration, promises to alleviate these inefficiencies. To address these challenges, a task migration and re‐offloading model based on task attribute classification is introduced, employing a hybrid deep reinforcement learning (DRL) algorithm—Dueling Double Q Network DDPG (QDPG). This algorithm merges the strengths of the Deep Deterministic Policy Gradient (DDPG) and the Dueling Double Deep Q‐Network (D3QN), effectively handling continuous and discrete action domains to optimize task migration and re‐offloading in IoV. The inclusion of the Mini Batch K‐Means algorithm enhances learning efficiency and optimization in the DRL algorithm. Experimental results show that QDPG significantly boosts task efficiency and computational performance, providing a robust solution for resource allocation in IoV.

Collaborative computation offloading and wireless charging scheduling in multi-UAV-assisted MEC networks: A TD3-based approach

Computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users’ (MUs’) tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks continuously increase, it is difficult for a single UAV to meet all computational demands, result in the decline of QoS. In order to address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery-limited UAVs cannot complete all tasks and sustain flight without recharging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price-based incentive mechanism to encourage the adjacent UAVs with unused computation resources to cooperate with busy UAVs, so as to maximize the total revenue obtained from UAVs’ collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the maximum overall system utility under different scenarios.

Single-machine scheduling simultaneous consideration of resource allocations and exponential time-dependent learning effects

This study addresses convex resource allocation scheduling problems with exponential time-dependent learning effects. Under a single-machine, a bicriteria scheduling model is introduced where the first criterion is to minimize makespan, and the other is to minimize resource consumption cost. For some special cases, polynomial time algorithms are developed for three versions of considering the two costs. For the general case of the three versions of these two costs, we present a heuristic algorithm, a simulated annealing (SA), and a branch and bound algorithm to solve the problem.

Supporting VoIP communication in IEEE 802.11ax networks: A new admission control and scheduling resource allocation scheme

The current IEEE 802.11ax standard enhances Wi-Fi networks with a series of new features, such as multi-user (MU) transmission, an Orthogonal Frequency Division Multiple Access (OFDMA) scheme and the ability to multiplex traffic from different access categories (ACs). These features can be utilized to enhance the QoS support for VoIP traffic and optimize the usage of IEEE 802.11ax network resources. This work proposes a new scheme to multiplex VoIP calls in IEEE 802.11ax MU frames and a new scheduler and resource allocation algorithm specifically designed for VoIP data traffic. Our scheduler allocates VoIP packets requiring longer transmission times into the same frame(s), minimizing the channel air-time assigned to VoIP transmission. In addition, unused radio resources in the MU frame are leveraged to transmit best-effort packets along with VoIP packets. For completeness, we also define a call admission control (CAC) algorithm that anticipates channel saturation conditions to ensure VoIP users can maintain a guaranteed level of QoS. Based on simulation results, our scheme is more efficient in reducing channel utilization than other schedulers such as multi-user round-robin (RR) (implemented by ns-3) or single-user FIFO. For example, for 30 VoIP stations using the G.711 codec under mixed channel conditions, our scheme reduces by 30% the air-time required to transmit VoIP packets. When coupled with the ability to also send best-effort packets along with VoIP packets, this translates into a higher throughput (i.e. 10 Mbit/s vs 4 Mbit/s) and more simultaneous VoIP users with guaranteed QoS (up to 46 VoIP users vs 26 and 28 users for the multi-user RR and single-user FIFO scheduling algorithms, respectively).

Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization

INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends.
 OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment.
 METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance.
 RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment 
 CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.

Joint Client Scheduling and Wireless Resource Allocation for Heterogeneous Federated Edge Learning With Non-IID Data

Joint Client Scheduling and Wireless Resource Allocation for Heterogeneous Federated Edge Learning With Non-IID Data

A Review on Deploying Blockchain Technology for Network Mobility Management

In wireless mobile networks, Mobility Management (MM) is a critical aspect that involves different tasks such as handover management, location management, offloading, resource allocation, and others. Blockchain platform comprising of an array of linked blocks innately supports the incorruptibility, sustaining the authenticity, and sustain pseudonymous privacy. Traditional MM is centralized, in contrast to blockchain-grounded MM that is partially or fully decentralized in its approach. As the inaugural researchers to review on blockchain-grounded MM, we are proud to cluster the blockchain-grounded MM concept under 7 clusters and closely study each framework with respect to different aspects such as blockchain specific parameters, MM specific parameters, network parameters, and more. We gathered a sample of 92 source references by curating the articles for eligibility criteria examined from electronic archives utilizing a comprehensive and enduring way. Grounded on this inspection, blockchain-grounded MM reinforces the overall security, trust, and privacy of location management and resource allocation, blockchain-grounded secure handover, facilitating registration and authentication, automatic MM utilizing smart contracts, reducing vulnerabilities, repairing low quality data, coordination, fair scheduling, in offloading, and blockchain-grounded resource allocation. Close studying proves that out of blockchain grounded MM frameworks, 37.5% utilize blockchain grounded secure handover, 92.5% utilize simple blockchain architecture, 32.5% utilize PoW consensus, 100% distributed MM, 97.5% host grounded MM, 95% packet reordering tolerant MM, 52.5% QoS aware MM, and 12.5% proposed for 5G networks. Finally, we present the openings and obstructions of the concept of blockchain-grounded MM and then catering surmounting advices.

A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode

The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system’s mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments.

The effect of autonomous team role selection in flexible projects

Today, most software projects are flexible, such as agile, extreme, or hybrid. In addition, this approach is increasingly being adopted for other types of projects. Most software project scheduling problems (SPSPs), more broadly, project scheduling problems (PSPs) and resource allocation methods, assume a fixed project structure. If we use these traditional methods, we cannot model and therefore understand why certain flexible, e.g., agile, policies are essential and how they help project management. This research shows how a matrix-based project planning approach can model team roles and synergies between employees. Extending the synergy-based software project scheduling problem, this study shows the effects of autonomous team role selection, highlighted in agile projects. The proposed model can manage (1) flexible projects, (2) synergies between employees, and (3) the difference between hard skills and soft skills. The proposed method is evaluated in a real-life case study. The proposed method is compared with existing, nonsynergy-based project scheduling methods. We found that positive synergy between employees can reduce the makespan of the project, and the best solution is given in autonomous team role selection.

Resource Allocation Scheduling Optimization Based on Internet of Things under Cloud Platforms

In this paper, we explore how to optimize IoT-based resource allocation and scheduling in a cloud platform environment, focusing on improving computing resource utilization and quality of service, while reducing latency and packet loss. a model is adopted, which contains a number of edge servers and randomly generated computational tasks, taking into account the network conditions between the servers and the tasks. an objective function is established, aiming to maximize the computational resource utilization and QoS, and the corresponding constraints are proposed. Simulations are conducted using CloudSim, and the experimental results show that the total number of VoCS increases from 243.63 to 1397.71 when the scheduling demand is increased from 8 to 64, demonstrating the adaptability and efficiency of the algorithm under different demands. In addition, the algorithm is effective in dealing with both small-scale (200 tasks) and large-scale (6000 tasks) tasks. In addition, the algorithm demonstrates low load imbalance and short task completion time when dealing with both small-scale (200 tasks) and large-scale (6000 tasks) task sets, which proves its effectiveness. Ultimately, the scheduling method proposed in this study not only improves resource utilization and quality of service, but also reduces task completion time and cost.

Optimizing Fog Computing Efficiency: Exploring the Role of Heterogeneity in Resource Allocation and Task Scheduling

Optimizing Fog Computing Efficiency: Exploring the Role of Heterogeneity in Resource Allocation and Task Scheduling

Resource Scheduling in URLLC and eMBB Coexistence Based on Dynamic Selection Numerology

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication.

Daily Runoff Prediction with a Seasonal Decomposition-Based Deep GRU Method

Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal decomposition-based-deep gated-recurrent-unit (GRU) method (SD-GRU) is proposed. The raw data is preprocessed and then decomposed into trend, seasonal, and residual components using the seasonal decomposition algorithm. The deep GRU model is then used to predict each subcomponent, which is then integrated into the final prediction results. In particular, the hyperparameter optimization algorithm of tree-structured parzen estimators (TPE) is used to optimize the model. Moreover, this paper introduces the single machine learning model (including multiple linear regression (MLR), back propagation (BP), long short-term memory neural network (LSTM) and gate recurrent unit (GRU)) and a combination model (including seasonal decomposition–back propagation (SD-BP), seasonal decomposition–multiple linear regression (SD-MLR), along with seasonal decomposition–long-and-short-term-memory neural network (SD-LSTM), which are used as comparison models to verify the excellent prediction performance of the proposed model. Finally, a case study of the Qingjiang Shuibuya test set, which considers the period 1 January 2019 to 31 December 2019, is conducted. Case studies of the Qingjiang River show the proposed model outperformed the other models in prediction performance. The model achieved a mean absolute error (MAE) index of 38.5, a Nash-Sutcliffe efficiency (NSE) index of 0.93, and a coefficient of determination (R2) index of 0.7. In addition, compared to the comparison model, the NSE index of the proposed model increased by 19.2%, 19.2%, 16.3%, 16.3%, 2.2%, 2.2%, and 1.1%, when compared to BP, MLR, LSTM, GRU, SD-BP, SD-MLR, SD-LSTM, and SD-GRU, respectively. This research can provide an essential reference for the study of daily runoff prediction models.

Resource allocation and aging priority-based scheduling of linear workflow applications with transient failures and selective imprecise computations

Resource allocation and aging priority-based scheduling of linear workflow applications with transient failures and selective imprecise computations

Towards Scalable Clustered URLLC IoT Network: Resource Allocation and Cooperation Scheduling for Reliability Enhancement

Towards Scalable Clustered URLLC IoT Network: Resource Allocation and Cooperation Scheduling for Reliability Enhancement

Blockchain-Aided Wireless Federated Learning: Resource Allocation and Client Scheduling

Blockchain-Aided Wireless Federated Learning: Resource Allocation and Client Scheduling