Resource Allocation Approach Research Articles

Optimal resource allocation for multiple shop floor tasks in collaborative assembly

Industries are adopting advanced technologies to attain the goals of the manufacturing system and the demands of the current industrial era. A collaborative and flexible manufacturing system that integrates human intelligence with a robotic workforce leads to customized automation and reinforces the safety of the human workforce. This system creates a flexible platform to assemble the parts as per the requirements and it supports reconfigurability with respective upcoming technologies. The proposed work involves an approach of a human-robot collaborative task allocation (HRTA) system. The HRTA can concern the demand for new product variants in the market and the upgradation of new technologies in the existing manufacturing system. The proposed work is organized into two phases such as resource allocation, and resource estimation using an assembly line balancing approach. The resource allocation approach is developed to identify the appropriate resource(human/robot) for each assembly task using the Nelder-Mead simplex optimization approach. Further assembly line balancing technique is used to quantify the number of resources to minimize the total idle time. The performance of the proposed method is corroborated by implementing it on an industrial case study and the obtained results are discussed with suitable illustrations.

Dynamic centralized resource allocation approach with contextual impacts: analyzing Chinese carbon allocation plans

Dynamic centralized resource allocation approach with contextual impacts: analyzing Chinese carbon allocation plans

Dynamic Resource Allocation in Industrial Internet of Things (IIoT) using Machine Learning Approaches

In today's era of rapid smart equipment development and the Industrial Revolution, the application scenarios for Internet of Things (IoT) technology are expanding widely. The combination of IoT and industrial manufacturing systems gives rise to the Industrial IoT (IIoT). However, due to resource limitations such as computational units and battery capacity in IIoT devices (IIEs), it is crucial to execute computationally intensive tasks efficiently. The dynamic and continuous generation of tasks poses a significant challenge to managing the limited resources in the IIoT environment. This paper proposes a collaborative approach for optimal offloading and resource allocation of highly sensitive industrial IoT tasks. Firstly, the computation-intensive IIoT tasks are transformed into a directed acyclic graph. Then, task offloading is treated as an optimization problem, taking into account the models of processor resources and energy consumption for the offloading scheme. Lastly, a dynamic resource allocation approach is introduced to allocate computing resources to the edge-cloud server for the execution of computation-intensive tasks. The proposed joint offloading and scheduling (JOS) algorithm creates its DAG and prepare a offloading queue. This queue is designed using collaborative q-learning based reinforcement learning and allocate optimal resources to the JOS for execution of tasks present in offloading queue. For this machine learning approach is used to predict and allocate resources. The paper compares conventional and machine learning-based resource allocation methods. The machine learning approach performs better in terms of response time, delay, and energy consumption. The proposed algorithm shows that energy usage increases with task size, and response time increases with the number of users. Among the algorithms compared, JOS has the lowest waiting time, followed by DQN, while Q-learning performs the worst. Based on these findings, the paper recommends adopting the machine learning approach, specifically the JOS algorithm, for joint offloading and resource allocation.

A Strategic Decision Support System Using Multiplayer Non-Cooperative Games for Resource Allocation After Natural Disasters

A severe natural disaster causes concurrent emergencies in separate geographical locations. However, tackling all the emergencies simultaneously with the limited available resources is challenging. A novel single-stage, non-cooperative multiplayer game-based solution approach for resource allocation is proposed in this paper, where the crisis locations in the demand of resources are treated as the players. The game-based decision support system (DSS) is intended to be implemented by the concerned disaster management authority to obtain a practical strategy for the allocation of indivisible and divisible resources among individual players in a limited resource environment. Any feasible allocation is associated with some cost on the basis of a non-monetary cost function. A discrete strategic game is formulated to tackle indivisible resource allocation and a continuous-kernel game for divisible resources. A mathematical analysis establishes that the game with the proposed cost function possesses at least one pure strategy Nash equilibrium (PSNE) for both types of games. Based on different selection criteria, a particular PSNE is chosen from the collection of multiple PSNEs and used as a desirable resource allocation strategy. A complexity analysis of the proposed algorithm is also carried out. Case studies are given in this paper to demonstrate the results developed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The cost function proposed in this paper to formulate the non-cooperative game-based DSS will be useful to disaster management authorities in resource allocation problems after any natural disaster. While distributing a limited quantity of essential resources, the cost function maintains an overall balance so that none of the disaster locations is deprived or favoured unreasonably. The feasible allocations of the existing resources are determined by the solutions of a multiplayer game. The case study indicates that the disaster management authority can choose different post-disaster ground truths to compute the possible resource demands of the disaster locations. Using the demand vector (a vector comprising the demands of the players as elements) and the number of available resources as the inputs, the game-theoretic model generates the allocation vector, according to which the disaster locations can be allocated resources.

A deep reinforcement learning assisted task offloading and resource allocation approach towards self-driving object detection

With the development of communication technology and mobile edge computing (MEC), self-driving has received more and more research interests. However, most object detection tasks for self-driving vehicles are still performed at vehicle terminals, which often requires a trade-off between detection accuracy and speed. To achieve efficient object detection without sacrificing accuracy, we propose an end–edge collaboration object detection approach based on Deep Reinforcement Learning (DRL) with a task prioritization mechanism. We use a time utility function to measure the efficiency of object detection task and aim to provide an online approach to maximize the average sum of the time utilities in all slots. Since this is an NP-hard mixed-integer nonlinear programming (MINLP) problem, we propose an online approach for task offloading and resource allocation based on Deep Reinforcement learning and Piecewise Linearization (DRPL). A deep neural network (DNN) is implemented as a flexible solution for learning offloading strategies based on road traffic conditions and wireless network environment, which can significantly reduce computational complexity. In addition, to accelerate DRPL network convergence, DNN outputs are grouped by in-vehicle cameras to form offloading strategies via permutation. Numerical results show that the DRPL scheme is at least 10% more effective and superior in terms of time utility compared to several representative offloading schemes for various vehicle local computing resource scenarios.

Federated Reinforcement Learning-Based Resource Allocation in D2D-Enabled 6G

The current 5G and conceived 6G era with ultrahigh density, ultra-high frequency bandwidth, and ultra-low latency can support emerging applications like Extended Reality (XR), Vehicle to Everything (V2X), and massive Internet of Things (IoT). With the rapid growth of transmission rate requirements and link numbers in the wireless communication network, how to allocate resources reasonably and further improve spectrum utilization challenges the traditional approaches. To address these problems, technologies such as device-to-device (D2D) communication and machine learning (ML) are introduced to the traditional cellular communication network to improve network performance. However, due to the interference caused by spectrum reusing, efficient resource allocation for both cellular users and D2D users is necessary. In this article, we consider underlay mode D2D-enabled wireless network to improve the spectrum utilization, and deep reinforcement learning (DRL)- based federated learning (FL)-aided decentralized resource allocation approach to maximize the sum capacity and minimize the overall power consumption while guaranteeing the quality of service (QoS) requirement of both cellular users and D2D users. The performance of the proposed schemes is evaluated through simulations under 5G millimeter-wave (mm-wave) and 6G terahertz (THz) scenarios separately. The simulation results show that the proposal achieves significant network performance compared with the baseline algorithms.

A Survey of Energy Optimization Approaches for Computational Task Offloading and Resource Allocation in MEC Networks

With the increased penetration of cloud computing and virtualization, a plethora of internet of things devices have been deployed globally. As a result, computationally intensive tasks are transmitted from the edge towards the centralized cloud for processing that leads to increased energy utilization in the cloud data centers while at the same increasing significant latency for critical applications. Recent years have witnessed a paradigm shift from centralized cloud computing towards mobile edge computing (MEC), where computational tasks are offloaded at the edge servers near user equipment (UE). This paradigm leads to lowering the energy utilization in the cloud data centers, along with low latency for UE and efficient resource utilization at the edge. In this context, the scale and complexity of the MEC networks is drastically increasing and, consequently, finding effective energy-efficient solutions for computational task offloading and resource allocation in MEC networks has become an ambitious task. To address the aforementioned challenges, this work surveys the state of the art in different categorizations of algorithm-based computational task offloading and resource allocation strategies focusing on energy utilization. It also provides a detailed cross-comparison of existing strategies in terms of their implementation specifications. Additionally, this paper also highlights open challenges and potential future research directions to facilitate efficient task offloading and resource allocation at the edge with reduced energy consumption at the centralized data centers. Our work also paves the way for the deployment of critical applications at the edge that require low latency and high service quality guarantees.

Analysis of feasible region in network slicing: Markov chain approach

Network slicing is a key feature introduced for 5G and Beyond 5G (B5G) to provide complete connectivity between people and things by overcoming limited resources. This study considers a theoretical approach for resource allocation when network slicing is employed. Specifically, an algorithm finding a feasible region of users for a given environment is proposed, and a continuous time Markov chain is adopted for its analysis. Some performance measures, such as average throughput and expected total revenue of the infrastructure provider, are expressed by the stationary probabilities of the Markov chain. A simple example and three different environments are presented in the experiment to illustrate the analysis procedure and investigate the effects of environments on performance, respectively. This analytical approach can simplify and speed up the evaluation of the performance of the network operator's strategy and increases the reliability of the evaluation, which is beneficial in practical applications. Moreover, this can provide theory based-guidance for infrastructure providers in determining the consistent network slice allocation policies, considering the users' needs and the network operator's decision strategy.

A Novel Approach of Resource Allocation for Distributed Digital Twin Shop-Floor

Facing global market competition and supply chain risks, many production companies are leaning towards distributed manufacturing because of their ability to utilize a network of manufacturing resources located around the world. Deriving from information and communication technologies and artificial intelligence, the digital twin shop-floor (DTS) has received great attention from academia and industry. DTS is a virtual shop-floor that is almost identical to the physical shop-floor. Therefore, multiple physical shop-floors located in different places can easily be interconnected to realize a DT that is a distributed digital twin shop-floor (D2TS). However, some challenges still hinder effective and efficient resource allocation among D2TSs. In order to attempt to address the issues, firstly, this paper proposes an information architecture for D2TSs based on cloud–fog computing; secondly, a novel mechanism of D2TS resource allocation (D2TSRA) is designed. The proposed mechanism both makes full use of a digital twin to support dynamic allocation of geographic resources and avoids the centralized solutions of the digital twin which lead to a heavy burden on the network bandwidth; thirdly, the optimization problem in D2TSRA is solved by a BP neural network algorithm and an improved genetic algorithm; fourthly, a case study for distributed collaborative manufacturing of aero-engine casing is employed to validate the effectiveness and efficiency of the proposed method of resource allocation for D2TS; finally, the paper is summarized and the relevant research directions are prospected.

Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks.

Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models.

Deep Reinforcement Learning Based Approach for Online Service Placement and Computation Resource Allocation in Edge Computing

Due to the urgent emergence of computation-intensive intelligent applications on end devices, edge computing has been put forward as an extension of cloud computing, to satisfy the low-latency requirements of these applications. To process heterogenous computation tasks on an edge node, the corresponding services should be placed in advance, including installing softwares and caching databases/libraries. Considering the limited storage space and computation resources on the edge node, services should be elaborately selected and deployed on the edge node and its computation resources should be carefully allocated to placed services, according to the arrivals of computation workloads. The joint service placement and computation resource allocation problem is particularly complicated, in terms of considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the waiting time of unprocessed tasks. Benefiting from deep reinforcement learning, we propose a novel approach based on parameterized deep Q networks to make the joint service placement and computation resource allocation decisions, with the objective of minimizing the total latency of tasks in a long term. Extensive simulations are conducted to evaluate the convergence and performance achieved by our proposed approach.

An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

AbstractIn a hierarchical edge‐fog‐cloud architecture, edge devices possess limited resources and energy. To contain with, it can offload some tasks generated by the Internet of Things (IoT) to the fog and cloud. Several factors influence this task‐offloading decision, including hardware features, network conditions, and application characteristics. Most of the research studies, in task offloading systems, have confined to changing parameter values, whereas very few have considered fuzzy‐based dynamic approaches for resource allocation. This work proposes a fuzzy‐based task offloading technique (FBOT) for scheduling the tasks (both compute and data‐intensive) to the appropriate nodes of the fog‐cloud system. The proposed method incorporates a vague logic‐based fuzzy task scheduler for scheduling various tasks at the fog layer. This helps to reduce the waiting time for the compute‐intensive tasks and minimizes the starvation problem of data‐intensive tasks. In addition, the proposed technique decreases the task completion time and selects the best computing nodes for each tasks. To validate the performance of the proposed technique, extensive simulation studies have been carried out on various parameters. Compared with the baseline algorithms, the results show that the proposed method offers an average improvement of 30% in terms of the total time of the task.

Multilevel agent negotiation on service bindings for efficient multi-tasking in IoT environments

In Internet-of-Things (IoT) environments, various IoT resources are utilized to provide the services necessary to accomplish users’ tasks. Therefore, IoT resources must be effectively and efficiently allocated so that service providers can successfully generate their service capabilities utilizing IoT resources. Additionally, conflicting resource allocation must be avoided for services supporting multiple user tasks. Furthermore, resource allocation and conflict resolution must be performed dynamically because users perform their tasks spontaneously. To address these issues, we propose an agent-based service-binding approach for efficient resource allocation and conflict resolution through negotiations among resources, service providers, and task agents. Negotiation occurs when a task agent attempts to use a service that is currently occupied by another task agent. The task agent that uses the service is determined in the negotiation process by considering the transfer fee based on the usage price of the service and the availability of a substitute service. Negotiations can occur at the task-service or service-resource levels to support flexible service binding. To evaluate the effectiveness of our approach, we compared its success rate with that of traditional resource allocation methods in terms of allocating resources successfully for multiple services. We also analyze the execution time to measure the overhead incurred by multilevel negotiation, which is an essential component of our approach. The proposed approach showed an average success rate of 79% which is 1.17%p lower than multi-objective optimization algorithms (MOOA), but the running time of the proposed approach was 98.3 s which was 56% of the running time of MOOA.

Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.

Resource-utility approach to ensuring the technological security of the Republic of Belarus

The article is devoted to the development of the utility-based resource allocation approach to the analysis and management of socio-economic systems by extending it to the research, scientific, technical, and innovation spheres. One of the reasons for the inconsistency of scientific and technological progress is demonstrated to be the cost method for evaluating its effectiveness, which identifies costs with results and ignores the usefulness of scientific and technological progress achievements. Appropriate utility criteria and indicators for assessing and managing this area are proposed in terms of implementation of the technology recovery strategy for Belarus and its allied Russia. Based on the analysis of the dynamics of scientific, technical and technological development of a number of Western European countries, a significant positive impact on the scientific and technological progress resulted from using proposed utility assessing criteria oriented towards a more efficient use of limited resources has been proved. With this in mind, recommendations are given on the development and implementation of a technological recovery strategy as a tool not only to strengthen the technological security of the state, but also to overcome the global contradictions generated by scientific and technological progress.

Multi-Dimensional Resource Allocation in Distributed Data Centers Using Deep Reinforcement Learning

With the development of edge-cloud computing technologies, distributed data centers (DCs) have been extensively deployed across the global Internet. Since different users/applications have heterogeneous requirements on specific types of ICT resources in distributed DCs, how to optimize such heterogeneous resources under dynamic and even uncertain environments becomes a challenging issue. Traditional approaches are not able to provide effective solutions for multi-dimensional resource allocation that involves the balanced utilization across different resource types in distributed DC environments. This paper presents a reinforcement learning based approach for multi-dimensional resource allocation (termed as NESRL-MRM) that is able to achieve balanced utilization and availability of resources in dynamic environments. To train NESRL-MRM’s agent with sufficiently quick wall-clock time but without the loss of exploration diversity in the search space, a natural evolution strategy (NES) is employed to approximate the gradient of the reward function. To realistically evaluate the performance of NESRL-MRM, our simulation evaluations are based on real-world workload traces from Amazon EC2 and Google datacenters. Our results show that NESRL-MRM is able to achieve significant improvement over the existing approaches in balancing the utilization of multi-dimensional DC resources, which leads to substantially reduced blocking probability of future incoming workload demands.

Learn with Curiosity: A Hybrid Reinforcement Learning Approach for Resource Allocation for 6G enabled Connected Cars

Learn with Curiosity: A Hybrid Reinforcement Learning Approach for Resource Allocation for 6G enabled Connected Cars

Cooperative Resource Allocation for Computation-Intensive IIoT Applications in Aerial Computing

Unmanned aerial vehicles (UAVs) will be a vital part of the massive Industrial Internet of Things (IIoT) in the 5G and 6G paradigms. The UAVs are required to collaborate with each other to deal with some computation-intensive IIoT applications in an autonomous UAV system. However, due to the limited processing capacity of UAVs, they are occasionally unable to handle certain tasks adequately (e.g., crowd sensing). Therefore, it is an important issue to realize efficient offloading of these computation-intensive IIoT applications. In this paper, we first partition the computation-intensive IIoT application into a directed acyclic graph with multiple collaborative tasks. Then, we establish a joint optimization problem based on the models of the processor resources and energy consumption for the task offloading scheme. Thirdly, we propose a cooperative resource allocation approach to optimize the joint optimization problem under the constraints of resource and communication latency, and then can migrate more computation-intensive tasks to the edge clouds. Finally, we build an aerial computing simulation system, and make a comparative evaluation and analysis of our proposed cooperative resource allocation approach in terms of effectiveness and performance. The experimental results show that our proposed approach performs better than other related approaches.

Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks

In Heterogeneous Vehicle-to-Everything Networks (HVNs), multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services. However, the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources. Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs. In this paper, we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy. To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle (V2V) user pairs, we propose a data-driven Multi-Agent Deep Reinforcement Learning (MADRL) resource allocation scheme for the HVN. Simulation results demonstrate that compared to existing algorithms, the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission, while providing close-to-limit performance.

Federated Deep Reinforcement Learning for Energy-Efficient Edge Computing Offloading and Resource Allocation in Industrial Internet

Industrial Internet mobile edge computing (MEC) deploys edge servers near base stations to bring computing resources to the edge of industrial networks to meet the energy-saving requirements of Industrial Internet terminal devices. This paper considers a wireless MEC system in an intelligent factory that has multiple edge servers and mobile smart industrial terminal devices. In this paper, the terminal device has the choice of either offloading the task in whole or in part to the edge server, or performing it locally. Through combined optimization of the task offload ratio, number of subcarriers, transmission power, and computing frequency, the system can achieve minimum total energy consumption. A computing offloading and resource allocation approach that combines federated learning (FL) and deep reinforcement learning (DRL) is suggested to address the optimization problem. According to the simulation results, the proposed algorithm displays fast convergence. Compared with baseline algorithms, this algorithm has significant advantages in optimizing the performance of energy consumption.