Dynamic Resource Allocation Algorithm Research Articles

Evolutionary Algorithm for Dynamic Resource Allocation and Its Applications

Evolutionary Algorithm for Dynamic Resource Allocation and Its Applications

OPTIMIZING EFFICIENCY AND PERFORMANCE IN 5G NETWORKS THROUGH A DYNAMIC RESOURCE ALLOCATION ALGORITHMIC FRAMEWORK

With the exponential growth of data demand and the advent of 5G networks, the need for efficient resource allocation algorithms has become paramount. This study presents a dynamic resource allocation algorithmic framework aimed at optimizing efficiency and performance in 5G networks. The framework focuses on frequency reuse at the edges while employing fractional pilots for enhanced spectrum utilization. 5G networks promise unprecedented speeds and low latency, enabling a wide array of applications from IoT to augmented reality. However, the efficient allocation of resources remains a challenge, especially at the network edges where interference is high. Traditional static resource allocation schemes fail to adapt to dynamic network conditions, leading to suboptimal performance. The main challenge lies in effectively managing resources to meet the diverse demands of various applications while mitigating interference and maximizing spectral efficiency. The proposed framework employs a dynamic resource allocation algorithm that adapts to changing network conditions in real-time. Leveraging fractional pilots, the algorithm optimizes frequency reuse at the network edges, thereby enhancing spectral efficiency. The framework integrates stochastic learning for predictive analytics to anticipate resource demands and interference patterns. Simulation results demonstrate significant improvements in spectral efficiency and network performance compared to traditional static allocation methods. The utilization of fractional pilots effectively reduces interference, enabling higher throughput and lower latency, especially at the network edges. The dynamic nature of the algorithm ensures adaptability to varying traffic loads, leading to enhanced overall network efficiency.

A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm

This research offers four work and computer tool setups. The dynamic Resource Allocation Algorithm is crucial to the system. This lets you manage changing supply. Once the PWMA knows how much work is coming up, it may divide resources and plan. The Load Balancing Algorithm (LBA) distributes work evenly to avoid over- or under-utilization and it also provides access content faster via the Adaptive Caching Algorithm (ACA). The proposed system surpasses the top alternative in several domains, such as data transmission, reaction time, energy conservation, load distribution effectiveness, and recovery time from failures. This is because the suggested solution incorporates many disparate approaches. Graphs and charts are visual representations that effectively illustrate the similarities and differences between the two methodologies. The hybrid technique is especially beneficial when the workload is unpredictable and prone to fluctuations. To do this, it instructs you on the fundamentals of efficient and adaptable computer resource management.

Retracted: Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

Retracted: Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

Visible light communication and WiFi hybrid networks based on dynamic resource allocation algorithm

Visible light communication and WiFi hybrid networks based on dynamic resource allocation algorithm

An Effective Multi-Agent Ant Colony Optimization Algorithm for QoS-Aware Cloud Service Composition

In order to improve the efficiency of wireless network virtualization resource processing, this paper combines the dynamic resource allocation algorithm to construct a wireless network virtualization resource sharing model. This paper proposes a task-oriented resource management model and uses the task-oriented TRS model to describe resources and service processes, reducing the complexity of formulating resource allocation strategies. Moreover, this paper comprehensively considers factors such as centralized coordination control cost and limited domain topology visibility to improve the dynamic resource allocation algorithm. Through the abstraction and unified representation of network resources, resource sharing, and efficient reuse, the coexistence and integration of heterogeneous wireless networks can be realized. Wireless network virtualization can decouple complex and diverse network management and control functions from hardware and extract them to the upper layer for unified coordination and management, thereby reducing network management costs and improving network management and control efficiency

Throughput maximization and reliable wireless communication in NOMA using chained fog structure and weighted energy efficiency power allocation approach

Novel radio access strategies must be developed to support an unprecedented number of connected devices to increase radio spectral efficiency of 5G and outside, and non-orthogonal multiple features (NOMA) arisen as a possible contender. To support cognitive NOMA networks, power allocation and NOMA-secondary user allocation is the effective technique for enhancing the resource utilization proficiency in power and spectrum domain. NOMA approach provides throughput enhancement to fulfil the demands of next group of Wireless Communication Networks. In this manuscript, a Chained Fog Structure (CFS) with Weighted Energy Efficiency Power Allocation (WEE-PA) method is proposed for throughput maximization and reliable wireless communication (CFS-WEE-NOMA). The aim of this study is “to enhance the throughput by involving underlay NOMA with Chained Fog Structure and WEE-PA method”. Here, the proposed WEE-PA approach is applied to multicarrier NOMA with chained fog structure for enhancing the throughput. During this analysis, the computational complexity due to the consideration of high users and different subcarrier values are solved by the proposed CFS-WEE-NOMA approach. Thus, the proper user pairing, power allocation, and bandwidth sharing of the proposed methodology ensure seamless communication. The simulation of this work is done in MATLAB and the performance metrics like throughput, computational complexity, power consumption, energy efficiency, delay, sum rate is analysed. The proposed CFS-WEE-NOMA approach has achieved 8.6%, 7.7%, and 6.7% high throughput based on transmitted power, 13.6%, 8.6%, and 11.7% high throughput based on subcarrier, and 13.6%, 9.6%, and 12.7% high energy efficiency compared with the existing methods, like Dynamic Network Resource Allocation (DNRA) algorithm (DNRA-NOMA), Multiple Carrier Cell-Less Non-Orthogonal Multiple Access (MC-CL-NOMA), dynamic programming (DP) recursion framework based multicarrier NOMA systems (DPRF-MC-NOMA) methods respectively.

An Online Resource Management for Obscured Sensors in Agriculture using UAV

Agriculture-based Internet of Things (Ag-IoT) can generate near-real-time quantitative data to analyze crop growth. Unmanned Aerial Vehicle (UAV) based mobile edge computing systems is widely used in Ag-IoT due to the low cost, fast movement and ease of operation of UAVs. Nevertheless, sensors in agriculture may be under a tall and dense crop canopy or under the soil where they have difficulty harvesting a steady of energy and have too worse a channel condition to transmit more data. This paper considers a UAV-Sensor Collaboration Wireless Network (USCWN) in which each UAV carries a computation module and a wireless power transfer module to provide service and energy to sensors. Then, we propose an online resource management framework with two sub-algorithms to maximize the network throughput of USCWN. The first is the sensor fuzzy selection algorithm, which determines which UAV the sensor transmits data to, considering the different resource properties between sensors and UAVs without prior knowledge. The other is a semi-distributed dynamic resource allocation algorithm in which the sensors compete with UAV’s energy, computation, and communication resources in a stochastic game. The UAV acts as a game manager to manage sensor resources. The stochastic game is solved using Lyapunov optimization, which yields a coarse correlated equilibrium that is better than the nash equilibrium. Performance evaluation shows that our proposed framework has a higher network throughput than other agricultural works and maintains stable sensor energy.

Low-Latency Federated Learning With DNN Partition in Distributed Industrial IoT Networks

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learning without any raw data exchange. However, it is rather challenging for lightweight IIoT devices to perform computation-intensive local model training over large-scale deep neural networks (DNNs). Driven by this issue, we develop a communication-computation efficient FL framework for resource-limited IIoT networks that integrates DNN partition technique into the standard FL mechanism, wherein IIoT devices perform local model training over the bottom layers of the objective DNN, and offload the top layers to the edge gateway side. Considering imbalanced data distribution, we derive the device-specific participation rate to involve the devices with better data distribution in more communication rounds. Upon deriving the device-specific participation rate, we propose to minimize the training delay under the constraints of device-specific participation rate, energy consumption and memory usage. To this end, we formulate a joint optimization problem of device scheduling and resource allocation (i.e. DNN partition point, channel assignment, transmit power, and computation frequency), and solve the long-term min-max mixed integer non-linear programming based on the Lyapunov technique. In particular, the proposed dynamic device scheduling and resource allocation (DDSRA) algorithm can achieve a trade-off to balance the training delay minimization and FL performance. We also provide the FL convergence bound for the DDSRA algorithm with both convex and non-convex settings. Experimental results demonstrate the derived device-specific participation rate in terms of feasibility, and show that the DDSRA algorithm outperforms baselines in terms of test accuracy and convergence time.

Resource Allocation for D2D Communication in Multiservice Cellular Network

Allowing different services with different requirements to coexist is a hot topic for future communication. In order to meet the different communication requirements of users, this paper proposes a cellular communication system based on filtered orthogonal frequency division multiplexing (filtered-OFDM) supporting device-to-device (D2D) communication. Further, in order to improve the system performance, we study the user resource allocation problem in the system and propose a dynamic iterative resource allocation algorithm based on Hungarian algorithm (DRABH). The algorithm allocates resource blocks for D2D user pairs and all cellular users in the cell under the constraint of the lowest threshold of signal to interference plus noise ratio (SINR), and cellular users have high priority. Under the condition of ensuring that all cellular users can communicate, resource blocks are allocated to as many D2D user pairs as possible to maximize the number of user links in the network. Simulation results show that considering the interference introduced by filtered-OFDM and D2D communication, the proposed resource allocation scheme can significantly improve the number of user links.

Distributed Q-Learning Algorithm for Dynamic Resource Allocation With Unknown Objective Functions and Application to Microgrid.

Dynamic resource allocation problem (DRAP) with unknown cost functions and unknown resource transition functions is studied in this article. The goal of the agents is to minimize the sum of cost functions over given time periods in a distributed way, that is, by only exchanging information with their neighboring agents. First, we propose a distributed Q -learning algorithm for DRAP with unknown cost functions and unknown resource transition functions under discrete local feasibility constraints (DLFCs). It is theoretically proved that the joint policy of agents produced by the distributed Q -learning algorithm can always provide a feasible allocation (FA), that is, satisfying the constraints at each time period. Then, we also study the DRAP with unknown cost functions and unknown resource transition functions under continuous local feasibility constraints (CLFCs), where a novel distributed Q -learning algorithm is proposed based on function approximation and distributed optimization. It should be noted that the update rule of the local policy of each agent can also ensure that the joint policy of agents is an FA at each time period. Such property is of vital importance to execute the ε -greedy policy during the whole training process. Finally, simulations are presented to demonstrate the effectiveness of the proposed algorithms.

Dynamic Manager Selection Assisted Resource Allocation in URLLC With Finite Block Length for 5G-V2X Platoons

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

Dynamic Resource Allocation and Task Offloading for NOMA-Enabled IoT Services in MEC

Integrating nonorthogonal multiple access (NOMA) and edge computing into the Internet of Things (IoT) for resource allocation and computing offloading can effectively reduce delay and energy consumption and improve spectrum efficiency. Computation tasks can be split into several independent subtasks and can be locally processed by IoT devices or offloaded to the MEC servers to process. The limited computing resources deteriorate the system performance. Thus, it is crucial to design the reasonable allocation strategies of computation resource and transmission power resource. In this paper, we jointly determine the CPU-cycle frequency allocation and transmission power allocation and formulate a stochastic optimization to minimize the energy consumption of IoT devices. Based on the Lyapunov optimization theory, we decompose the optimization problem into two deterministic subproblems to solve separately. One of them is obtained by seeking the first derivative, and the other is solved by using the best response idea after establishing the game model. Meanwhile, we propose a dynamic resource allocation and task offloading (DRATO) algorithm. Moreover, the simulation experiments show that the proposed algorithm effectively improves system performance and reduces energy consumption compared to three other benchmark methods.

Dynamic energy efficient task offloading and resource allocation for NOMA-enabled IoT in smart buildings and environment

Nowadays, the Internet of Things (IoT) devices are widely deployed and applied in smart buildings and environment, and play an important part in people’s daily life. With more and more IoT applications adopted, a number of computation-intensive tasks are generated. Due to the limitations of computation, storage resources , and battery capacities of IoT devices, some tasks need to be offloaded to edge servers for processing. Besides, the non-orthogonal multiple access (NOMA) techniques are also proposed to solve the problem brought by the limitation of wireless transmission resources. In this paper, we study the energy efficient task offloading and resource allocation problem for NOMA-enabled IoT in smart buildings and environment. We formulate the optimization problem with the goal of minimizing the energy consumption while meeting the delay constraints. However, solving this task offloading and resource allocation problem faces several challenges. Firstly, the statistical information of task generation process on IoT devices in smart buildings and the states of wireless channels are hard to predict precisely. Secondly, as the numbers of IoT devices and applications increase, the solution space of the optimization problem grows exponentially. To address these challenges, we take advantage of advanced stochastic optimization techniques, and transform the original problem into a deterministic problem. Then, we decompose the transformed problem into three subproblems and propose the dynamic energy efficient task offloading and resource allocation (NTORA) algorithm. Experiments include both parameter analysis and comparison experiments are conducted, and the results validate the effectiveness of our NTORA algorithm.

Intelligent Sensing Scheduling for Mobile Target Tracking Wireless Sensor Networks

Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in mobile target tracking wireless sensor networks (MTT-WSNs). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is intractable to execute dynamic and critical missions in the MTT-WSN network due to static property. Besides, the network cannot ensure consecutive tracking with limited energy. To address the problems, this article proposes a new hierarchical tracking structure based on the edge intelligence (EI) technology. The structure can integrate the computing resource of both mobile nodes and edge servers to provide high-efficient computing for real-time tracking. Based on the proposed structure, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource scheduling solution for accurate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -learning over 14.5% in terms of systematic energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the noncooperative scheme.

Wireless Network Virtualization Resource Sharing considering Dynamic Resource Allocation Algorithm

In order to improve the efficiency of wireless network virtualization resource processing, this paper combines the dynamic resource allocation algorithm to construct a wireless network virtualization resource sharing model. This paper proposes a task-oriented resource management model and uses the task-oriented TRS model to describe resources and service processes, reducing the complexity of formulating resource allocation strategies. Moreover, this paper comprehensively considers factors such as centralized coordination control cost and limited domain topology visibility to improve the dynamic resource allocation algorithm. Through comparative research, it can be seen that the wireless network virtualization resource sharing method proposed in this paper considering the dynamic resource allocation algorithm can effectively improve the processing efficiency of wireless network virtualization resources.

Dynamic Resource Allocation With Decentralized Multi-Task Assignment Approach for Perimeter Defense Problem

In this article, a dynamic resource allocation with decentralized multi-task assignment (DREAM) approach using a dynamic sized team of defenders is presented to protect a perimeter of a high-security area from heterogeneous intruders. In the DREAM approach, the spatio-temporal problem of neutralizing the heterogeneous intruders is converted into a decentralized time-ordered multi-task assignment problem. A single-step dynamic resource allocation algorithm based on the computed assignments determines an optimal number of defenders required. The DREAM approach adds more defenders to the team from the reserve stations or removes the excess defenders from the team such that it always utilizes minimal resources for protecting convex territory. A trajectory computation algorithm converts the optimal multi-task assignments to trajectories for the defenders. The working of the DREAM approach for a typical perimeter defense problem is illustrated using a simulated convex territory protection problem. Based on the results from the ablation study, it is seen that one needs to deploy a higher number of defenders in the team for a better success rate to handle highly maneuvering intruders. Furthermore, a higher defender-to-intruder speed ratio helps in better resource utilization. These results clearly indicate that the DREAM approach can protect any convex territory efficiently with minimal resources.

Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

In recent years, with the continuous development of IT industry, network virtualization technology has gradually developed into a hotspot of research and application in the field of communication because IT allows the construction of customized virtual network (VN) on shared physical infrastructure. In particular, the proposal of wireless network virtualization for the development of the enhanced Internet. Because these technologies require effective algorithms named virtual network mapping (VNE) to instantiate virtual networks on the SN, on the basis of satisfying the isolation between wireless network slices, a resource allocation mechanism based on bilateral auction is proposed. At present, many researchers have proposed that network virtualization technology is a feasible way to solve the rigid Internet architecture. In the field of existing research focuses on the cable network virtualization, mainly related to the data center network and backbone, then, the side of the wireless access network virtualization the key technology research is still in its infancy, in order to accelerate the process of wireless skill for innovation and meet the demand of the difference of the emerging business, wireless network virtualization becomes the research hotspot and focus of academia and industry. However, no one has applied the dynamic resource allocation algorithm to wireless network virtualization resource sharing field. Here, this paper analyzes and summarizes the existing virtual network resource allocation schemes at home and abroad. The wireless virtual network resource allocation model and cross-domain virtual network resource allocation model are established, and the network centrality theory in social network and complex network is introduced to analyze the topology properties of virtual network and physical network, and two efficient virtual network resource allocation methods are proposed.

TARA: temperature aware online dynamic resource allocation scheme for energyoptimization in cloud data centres

Cloud data centres, which are characteristic of dynamic workloads, if not optimized for energy consumption, may lead to increased heat dissipation and eventually impact the environment adversely. Consequently, optimizing the usage of energy has become a hard requirement in today's cloud data centres wherein the major part of energy consumption is mostly attributed to computing and cooling systems. Motivated by which this paper proposes an online algorithm for dynamic resource allocation, namely, temperature aware online dynamic resource allocation algorithm (TARA). TARA demonstrates a novel algorithm design to adapt dynamic resource allocation based on the temperature of a data centre using computational fluid dynamics (CFD). Also, TARA demonstrates a new dynamic resource reclaim strategy for making efficient resource allocations leading to efficient energy consumptions in dynamic environments. The proposed algorithm provides optimal resource allocation considering energy efficiency without being overwhelmed by online dynamic workloads. The optimal energy-efficient dynamic resource allocation for online workloads eventually optimizes the computing and cooling energy consumption. We show through theoretical analysis the correctness, efficiency and optimality bounds given as $TARA(P) \leq 2OPT(P)$, relative to the optimal solution provided by offline dynamic resource allocation algorithm $(OPT(P))$. We show through empirical analysis that the proposed method is efficient and significantly saves energy by 26\% when the data centre utilization is 100\% compared to batched reclaim. The performance analysis shows significant improvement in optimizing computing and cooling efficiency. TARA can be used in multiple areas of on-demand dynamic resource allocation in cloud computing like resource allocation for virtual machine creation, resource allocation for virtual machine migrations, and virtual resources assignment for elastic cloud applications.

E-Commerce Logistics System Based on Internet of Things

In order to improve the efficiency of the e-commerce logistics system, this paper analyzes the application of dynamic bandwidth resource allocation algorithm in intelligent logistics tracking scenarios and distribution scenarios, and achieves the purpose of optimizing the allocation of bandwidth resources by changing the sampling rate of the control signal. Moreover, this paper designs and implements an e-commerce logistics information system based on the Internet of Things, and starts with each functional module to introduce in detail the various functions realized by the system in this paper. This paper changes the traditional logistics operation mode, through the realization of various functional modules, users can grasp personal historical orders and view the list of historical orders. Finally, this paper analyzes the performance of this system through experimental research. The results of the research show that the e-commerce logistics system based on the Internet of Things proposed in this paper is effective.