Dynamic Resource Allocation Method Research Articles

Dynamic low earth orbit multi satellite hopping beam resource allocation considering load and dynamic coverage balance (LDCB-MSHBRA)

A dynamic low earth orbit multi satellite hopping beam resource allocation method based on time slicing is proposed to address the issue of changing coverage and beam interference caused by satellite mobility in multi satellite and multi beam communication scenarios. The method involves six processes: (1) Establishing a low earth orbit satellite system with three identical configuration parameters and motion speed parameters; (2)Assign a value to the time slicing weight coefficient and use the principle of load balancing to minimize the difference in demand for dual star services as the optimization objective; (3) Obtain initial power resource allocation with maximum system capacity as the optimization objective; (4) Considering the mobility of low earth orbit satellites, updating weight coefficients and dynamically allocating power resources, a time resource dynamic allocation matrix and the optimal service cell set are obtained; (5) Update the independent coverage cell set of the third low earth orbit satellite, use the Lagrange equation of the weighted fair objective function to obtain the time resource allocation optimization matrix, and use one-way search to determine the optimal service cell set; (6) With the goal of maximizing system capacity, the optimal solution of the power resource allocation matrix for the third low earth orbit satellite is obtained. The system throughput is introduced to intuitively measure system performance, and adjustments are made based on the overlap of Samsung coverage. The resource allocation method is based on a fairness model, considering load balancing and dynamic coverage range changes. On the basis of improving the overall system throughput, it ensures the fairness of resource allocation and allocates time, frequency, and power resources to multiple satellites with overlapping coverage, which helps to improve the throughput and resource utilization of satellite systems.

Resource allocation strategy of space cloud network based on resource clustering

SummarySpace information network (SIN) is difficult to fully utilize the limited on‐board resource due to its dynamic and heterogeneous nature, while the traditional resource management methods cannot adapt to the increasingly diverse task requirements. Space cloud network architecture is an effective technology to reduce the pressure on satellite resources. To effectively manage the space cloud network resources, we design a resources allocation strategy based on resource clustering. Firstly, we propose the space cloud network architecture. Then, we propose a genetic algorithm to cluster the space cloud resources. Finally, we propose a dynamic resource allocation method based on reinforcement learning for the dynamic characteristics of space cloud resources. The method improves the reinforcement learning algorithm through dynamic objective optimization to complete the optimization of multiple objectives in the process of space cloud resources allocation. The simulation results show that the algorithm proposed in this paper reduces the task execution delay by an average of 10.5% compared with the original DQN algorithm and increases the execution success rate by 2.17%.

The dynamic multi project human resource allocation method of manufacturing industry based on multidimensional model

The dynamic multi project human resource allocation method of manufacturing industry based on multidimensional model

The dynamic multi project human resource allocation method of manufacturing industry based on multidimensional model

The dynamic multi project human resource allocation method of manufacturing industry based on multidimensional model

Dynamic Resource Allocation Approach for Interference Mitigation based on NOMA in Network Operating in Both Uplink and Downlink

The new notion of Internet of Vehicles (IoV) supports Vehicle-to-Everything (V2X) communications, which play a very important role in 5G systems. Ultra-Dense-Network (UDN) and the new scheme Non-orthogonal multiple access (NOMA) are two important concepts to meet the requirements of V2X services with their remarkable capacity. However, the concept of NOMA technology, which allows multiple vehicles to be served by the same resource, can lead to interference between different active users. In addition, the extensive placement of small base stations (SBS) in UDN causes severe interference and degrades the network performance. To solve these problems, interference mitigation is widely used to avoid interference in NOMA-based UDN. In this document, we suggested a dynamic resource allocation method named Intra-cell-Inter-cell Interference Mitigation for V2X communication for UDN in uplink and downlink sense (Intra-Inter IMVN). Our algorithm aims to achieve five principals objectives, namely applying the same algorithm for both downlink and uplink modes, reducing the degree of interference, improving network performance in aspects like error rates and throughput, minimizing cost and delay when using this alternative, combining dynamic metrics to support many V2X scenarios and requirements, and dealing with two categories of interference such as inter-cell interference and intra-cell interference. Numerical results demonstrate that our suggested Intra-Inter IMVN approach offers higher performance compared to other existing algorithms as a function of various parameters.

Dynamic Resource Allocation in Cloud Environments

Abstract: The provisioning and use of computing resources has been completely transformed by cloud computing, which provides previously unheard-of levels of scalability, flexibility, and cost-effectiveness. In order to maximize resource utilizatio and guarantee peak performance in cloud settings, dynamic resource allocation is essential. In order to provide a thorough grasp of the most recent developments in this subject, this survey paper offers an in-depth review of dynamic resource allocation methods in cloud computing. The survey includes a wide range of dynamic resource allocation techniques, including as cost-optimization methods, resource provisioning and scheduling techniques, and load-balancing algorithms. Each method is examined in terms of its advantages, drawbacks, and applicability to particular cloud deployment scenarios. The research also investigates evaluation approaches and performance indicators for evaluating the efficacy of dynamic resource allocation systems. It emphasizes the need for continued research and innovation to address the evolving demands of cloud computing environments.

Core and spectrum allocation to achieve graceful degradation of inter-core crosstalk with generalized hierarchical core prioritization on space-division multiplexing elastic optical networks

Space-division multiplexing elastic optical networks (SDM-EONs) with multi-core fibers (MCFs) are expected to meet the exponentially increasing demand for traffic. However, it is inevitable that inter-core crosstalk (IC-XT) will hinder efforts to increase capacity. Thus, attempts have been made to leverage SDM-EONs by solving the route, modulation format, core, and spectrum assignment problems, but the effects of IC-XT still need to be mitigated. Existing methods such as XT-Aware first fit and core prioritization methods are insufficient for addressing this issue. In this paper, we propose generalized hierarchical core prioritization for the graceful degradation of IC-XT in core and spectrum allocation. Simulation results establish that our approach mitigates adverse influences from IC-XT and has high flexibility in network environments. This study shows that the dynamic resource allocation method, despite being heuristic and computationally inexpensive, can sufficiently reduce the blocking probabilities in any SDM-EONs with hexagonal MCFs.

Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission

The optimal allocation of manufacturing resources plays an essential role in the production process. However, most of the existing resource allocation methods are designed for standard cases, lacking a dynamic optimal allocation framework for resources that can guide actual production. Therefore, this paper proposes a dynamic allocation method for discrete job shop resources in the Internet of Things (IoT), which considers the uncertainty of machine states, and carbon emission. First, a data-driven job shop resource status monitoring framework under the IoT environment is proposed, considering the real-time status of job shop manufacturing resources. A dynamic configuration mechanism of manufacturing resources based on the configuration threshold is proposed. Then, a real-time state-driven multi-objective manufacturing resource optimization allocation model is established, taking machine tool energy consumption and tool wear as carbon emission sources and combined with the maximum completion time. An improved imperialist competitive algorithm (I-ICA) is proposed to solve the model. Finally, taking an actual production process of a discrete job shop as an example, the proposed algorithm is compared with other low-carbon multi-objective optimization algorithms, and the results show that the proposed method is superior to similar methods in terms of completion time and carbon emissions. In addition, the practicability and effectiveness of the proposed dynamic resource allocation method are verified in a machine failure situation.

A Layer & Request Priority-based Framework for Dynamic Resource Allocation in Cloud- Fog - Edge Hybrid Computing Environment

One of the most promising frameworks is the fog computing paradigm for time-sensitive applications such as IoT (Internet of Things). Though it is an extended type of computing paradigm, which is mainly used to support cloud computing for executing deadline-based user requirements in IoT applications. However, there are certain challenges related to the hybrid IoT -cloud environment such as poor latency, increased execution time, computational burden and overload on the computing nodes. This paper offers A Layer & Request priority-based framework for Dynamic Resource Allocation Method (LP-DRAM), a new approach based on layer priority for ensuring effective resource allocation in a fog-cloud architecture. By performing load balancing across the computer nodes, the suggested method achieves an effective resource allocation. Unlike conventional resource allocation techniques, the proposed work assumes that the node type and the location are not fixed. The tasks are allocated based on two constrain, duration and layer priority basis i.e, the tasks are initially assigned to edge computing nodes and based on the resource availability in edge nodes, the tasks are further allocated to fog and cloud computing nodes. The proposed approach's performance was analyzed by comparing it to existing methodologies such as First Fit (FF), Best Fit (BF), First Fit Decreasing (FFD), Best Fit Decreasing (BFD), and DRAM techniques to validate the effectiveness of the proposed LP-DRAM.

Recursive grouping and dynamic resource allocation method for large-scale multi-objective optimization problem

For large-scale multi-objective optimization problems (LSMOPs), the core problem is to overcome the curse of dimensionality. Cooperate coevolution has been proven to overcome this difficulty to a certain extent, which decomposes the decision variables into a number of groups and optimizes them in a cooperative coevolutionary manner, so a good decomposition method is particularly important. However, existing decomposition methods are usually computationally expensive. In this paper, a method for detecting the interaction of decision variables in LSMOPs is proposed. It first transforms a multi-objective optimization problem into a single-objective problem. Then, recursive grouping is applied, which can detect the relationship between a decision variable and the other ones recursively and put all interacting decision variables into the same group to get better grouping results with fewer function evaluations. Once groups are determined, by analyzing the contribution of each group to the problem, the group with higher contribution will be allocated more function evaluations to perform optimization. Experimental results show that the proposed framework has competitive performance compared with four state-of-the-art algorithms.

Dynamic resource allocation of UAV swarms based on cooperative and competitive public goods game

The dynamic resource allocation of unmanned aerial vehicle (UAV) swarms is a challenging key technical problem in mission planning. In this paper, a dynamic resource allocation method based on a cooperative and competitive mechanism is proposed. The average income of the system is improved by designing regulation rules for special individuals. Finally, the feasibility and effectiveness of the proposed method for UAV dynamic resource allocation are verified according to the network structure, model parameters, and steady-state results of the system under different excitation mechanisms through simulation experiments of the UAV game model.

Adaptive Access Control and Resource Allocation for Random Access in NGSO Satellite Networks

In non-geostationary orbit (NGSO) satellite networks, users perform random access (RA) to establish connections. Due to the non-uniform traffic demands of RA in time and space, dynamic access control and resource allocation methods with low signaling overhead and high RA efficiency are required, where the RA efficiency is denoted as the number of successfully accessed users per resource block. Existing dynamic adjustment methods provide a feasible approach to improve RA efficiency with short update intervals, but the frequent updates of these methods result in high signaling overhead. Compared with the short-term update, update with a given longer interval could reduce update frequency, while it will lead to a reduction in RA efficiency for the accuracy of long-term prediction decreases. Hence, to improve RA efficiency and decrease signaling overhead at the same time, we propose a new adaptive access control and resource allocation scheme in this paper, where the length of update interval is adaptive to the changes in traffic. First, average random access efficiency (ARAE) is defined to evaluate the RA efficiency between each update. Then, to decrease signaling overhead while keeping high ARAE, adaptive access control and resource allocation scheme is proposed, where the adaptive traffic prediction step is introduced to forecast future traffic with adaptively selected update interval, and the access control and resource allocation step is designed to maximize ARAE based on forecasted traffic. Finally, the effectiveness of the proposed scheme is corroborated by simulations, which indicate our scheme is adaptive to varying demands and outperforms other methods with high ARAE and low signaling overhead.

Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm.

Cloud computing is an important milestone in the development of distributed computing as a commercial implementation, and it has good prospects. Infrastructure as a service (IaaS) is an important service mode in cloud computing. It combines massive resources scattered in different spaces into a unified resource pool by means of virtualization technology, facilitating the unified management and use of resources. In IaaS mode, all resources are provided in the form of virtual machines (VM). To achieve efficient resource utilization, reduce users' costs, and save users' computing time, VM allocation must be optimized. This paper proposes a new multiobjective optimization method of dynamic resource allocation for multivirtual machine distribution stability. Combining the current state and future predicted data of each application load, the cost of virtual machine relocation and the stability of new virtual machine placement state are considered comprehensively. A multiobjective optimization genetic algorithm (MOGANS) was designed to solve the problem. The simulation results show that compared with the genetic algorithm (GA-NN) for energy saving and multivirtual machine redistribution overhead, the virtual machine distribution method obtained by MOGANS has a longer stability time. Aiming at this shortage, this paper proposes a multiobjective optimization dynamic resource allocation method (MOGA-C) based on MOEA/D for virtual machine distribution. It is illustrated by experimental simulation that moGA-D can converge faster and obtain similar multiobjective optimization results at the same calculation scale.

SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

Network function virtualization is a promising technology for providing personalized services via agile service function chains (SFCs). Flexible SFC orchestration and rational resource allocation are pivotal for improving the SFC's quality of service (QoS). However, the requirements for computational load and resources have frequently been changing. Consequently, static resource allocation can result in resource insufficiency when SFCs turn busy and resource waste due to resource overplus when SFCs are idle. Since a dynamic resource allocation is necessary, the existing dynamic resource allocation methods' responses have often been delayed. This paper proposes an SFC active reconfiguration mechanism (SARM) based on computational load and resource demand. The SARM predicts nodes' computation loads and SFCs' resource demands and uses these predictions to estimate future QoS and develop the SFC reconfiguration strategy. The SARM considers multiple factors and applies a heuristic algorithm to achieve the tradeoff between migration cost and QoS preservation. The experiments demonstrate that the SARM can effectively predict the nodes' load and the resource demand of SFCs. In addition, the SARM can successfully identify the SFCs to reconfigure and reduce the QoS maintenance costs. The simulation results indicate that the average delays of the SFCs can be reduced by at least 26%.

A Stackelberg Game-Based Dynamic Resource Allocation in Edge Federated 5G Network

Mobile Edge Computing (MEC) plays key role for providing fast-response and high interactivity in the emerging 5G network. Edge service providers (ESP) are responsible for serving edge users or IoT devices running latency-critical applications. An MEC server provides faster response to ESPs but has limited computation resources, hence, it can be overloaded due to extensive resource demand. Thus, federation of multiple MEC servers offers an opportunity for dynamic resource allocation in a distributed manner. The federation objective is to maximize the usage of underutilized edge resources and reduction of service provision time simultaneously. As all the ESPs and MECs act autonomously, it is quite impossible for all the individuals to achieve optimal behavior simultaneously. In this paper, we develop a Stackelberg Game ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SBG$ </tex-math></inline-formula> ) based dynamic resource allocation method to reach the expected performance. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SBG$ </tex-math></inline-formula> analyzes the pricing of MECs and the resource-purchasing problem of ESPs. We also develop a many-to-many matching algorithm for resource sharing among the MECs and a one-to-many matching algorithm for that between an MEC server and ESPs. The results from an extensive performance evaluation demonstrate effectiveness of the proposed system in increasing utilities for MECs and ESPs, reducing the turnaround time of application tasks, and ensuring fair resource distribution compared to state-of-the-art works.

Interference Management with Dynamic Resource Allocation Method on Ultra-Dense Networks in Femto-Macrocellular Network

Ultra-Dense Network (UDN) which is formed from femtocells densely deployed is known as one of key technologies for 5th generation (5G) cellular networks. UDN promises for increased capacity and quality of cellular networks. However, UDN faces more complex interference problems than rarely deployed femtocells, worse on femtocells that are located on cell edge area of macrocell. Therefore, mitigating or reducing effects of interferences is an important issue in UDN. This paper focuses on interference management using dynamic resource allocation for UDN. Types of interference considered in this study are cross-tier (macrocell-to-femtocell) and co-tier (femtocellto-femtocell) interferences for uplink transmission. We consider several scenarios to examine the dynamic resource allocation method for UDN in case of femtocells deployed in the whole area of microcell and in the cell edge area of macrocell. Simulation experiment using MATLAB program has been carried out. The performance parameters that are collected from the simulation are Signal to Interference and Noise Ratio (SINR), throughput, and Bit Error Rate (BER). The obtained simulation results show that system using dynamic resource allocation method outperforms conventional system and the results were consistent for the collected performance parameters. The dynamic resource allocation promises to reduce the effects of interference in UDN.

CNS: Application of Dynamic Resource Allocation Based on 5G Communication in Technology Innovation of Complex High-Tech Industries

In order to improve the technological innovation effect of compound high-tech industry, this paper studies its dynamic resource allocation technology with the support of 5G communication technology, and analyzes the application of 5G communication technology in technological innovation of compound high-tech industry. In the uplink and downlink of wireless energy-carrying communication system, this paper studies the joint optimization of subcarrier allocation and power allocation of uplink and downlink to maximize the uplink and downlink weighted sum rate of the system. Moreover, this paper adopts the joint optimization algorithm of power and sub-carrier based on Lagrangian dual method and ellipsoid method to obtain the maximum information transmission rate of uplink and downlink. In addition, this paper constructs an intelligent innovation management system. The research results show that the dynamic resource allocation method based on 5G communication proposed in this paper has a very good effect in the application of technological innovation in complex high-tech industries.

Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

M2FBalancer: A mist-assisted fog computing-based load balancing strategy for smart cities

Nowadays, cities are intended to change to a smart city. According to recent studies, the use of data from contributors and physical objects in many cities play a key element in the transformation towards a smart city. The ‘smart city’ standard is characterized by omnipresent computing resources for the observing and critical control of such city’s framework, healthcare management, environment, transportation, and utilities. Mist computing is considered a computing prototype that performs IoT applications at the edge of the network. To maintain the Quality of Service (QoS), it is impressive to employ context-aware computing as well as fog computing simultaneously. In this article, the author implements an optimization strategy applying a dynamic resource allocation method based upon genetic algorithm and reinforcement learning in combination with a load balancing procedure. The proposed model comprises four layers i.e. IoT layer, Mist layer, Fog layer, and Cloud layer. Authors have proposed a load balancing technique called M2F balancer which regulates the traffic in the network incessantly, accumulates the information about each server load, transfer the incoming query, and disseminate them among accessible servers equally using dynamic resources allocation method. To validate the efficacy of the proposed algorithm makespan, resource utilization, and the degree of imbalance (DOI) are considered as the scheduling parameter. The proposed method is being compared with the Least count, Round Robin, and Weighted Round Robin. In the end, the results demonstrate that the solutions enhance QoS in the mist assisted cloud environment concerning maximization resource utilization and minimizing the makespan. Therefore, M2FBalancer is an effective method to utilize the resources efficiently by ensuring uninterrupted service. Consequently, it improves performance even at peak times.

AI-Driven Collaborative Resource Allocation for Task Execution in 6G-Enabled Massive IoT

In the foreseeable future, the rapid growth of devices in the Internet of Things (IoT) will make it difficult for 5G networks to ensure sufficient network resources. 6G technology has attracted increasing attention, bringing new design concepts to the dynamic real-time resource allocation. The resource requirements of devices are usually variable, so a dynamic resource allocation method is needed to ensure the smooth execution of tasks. Therefore, this article first designs a 6G-enabled massive IoT architecture that supports dynamic resource allocation. Then, a dynamic nested neural network is constructed, which adjusts the nested learning model structure online to meet training requirements of dynamic resource allocation. An AI-driven collaborative dynamic resource allocation (ACDRA) algorithm is proposed based on the nested neural network combined with Markov decision process training for 6G-enabled massive IoT. Extensive simulations have been carried out to evaluate ACDRA in terms of several performance criteria, including resource hit rate and decision delay time. The results validated that ACDRA improves the average resource hit rate by about 8% and reduces the average decision delay time by about 7% compared with three reference existing algorithms.