Joint Allocation Research Articles

Joint allocation of computing and connectivity resources in survivable inter-datacenter elastic optical networks

Joint allocation of computing and connectivity resources in survivable inter-datacenter elastic optical networks

Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network

Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network

An improved resource allocation method for mapping service function chains based on A3C

AbstractNetwork function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

Joint Allocation of Buffer Sizes and Service Times in Unreliable Assembly-Disassembly Systems

Joint Allocation of Buffer Sizes and Service Times in Unreliable Assembly-Disassembly Systems

Hierarchical Reinforcement Learning-Based Joint Allocation of Jamming Task and Power for Countering Networked Radar

Hierarchical Reinforcement Learning-Based Joint Allocation of Jamming Task and Power for Countering Networked Radar

Evaluation of sustainability development of the eco-economic system of water and soil resources based on emergy analysis

Abstract Due to the swift progress of the economy and society and the rapid escalation of urbanization rates, the sustainable examination of individual water or land resources is no longer adequate to satisfy the rational allocation demands of urban development concerning the water and soil resource framework. To enhance the efficiency of utilizing water and land resources comprehensively and the rationality of the joint allocation of these resources, this paper presents a comprehensive analysis of the eco-economic system of water and land resources, encompassing six key aspects: system emergy flow, water resources subsystem, land resources subsystem, economic subsystem, ecological subsystem, and social subsystem. By considering three types of land use (construction land, agricultural land, and unused land), we constructed a sustainable evaluation index system for the eco-economic system of water and land resources based on the emergy theory. We employ the emergy theory analysis method to provide a comprehensive assessment of the sustainable development status and development trend of the Luoyang City from 2011 to 2020. The results show that Luoyang belongs to the consumer economy and the development of water and soil resources of the ecological economic system in Luoyang is in a critical state of unsustainable and mild sustainability from 2011 to 2020.

Research on demand forecasting and distribution of emergency medical supplies using an agent-based model

The global health crisis caused by SARS-CoV-2 since 2019 has emphasized the critical significance of effective disease detection and treatment in minimizing infection rates and fatalities, as well as halting the spread of pandemics. During an outbreak, individuals suspected of being infected require a significant amount of testing resources, while those confirmed to be infected demand substantial treatment resources. Hence, this paper is dedicated to presenting a new pandemic model that enables joint forecasting and allocation of resources for testing and treatment. The proposed model in this paper is an innovative agent-based epidemic compartmental model, which also incorporates a mixed integer model. It integrates novel features based on crucial disease characteristics, such as self-healing for asymptomatic or mild-symptomatic cases, varying infection risk levels among different groups, and the inclusion of secondary infections. Moreover, the solutions of the joint allocation model are compared with those of the independent allocation model, which entails considering resource interactions rather than allocating each resource independently. Furthermore, the validity of this model was confirmed through real-world data obtained during the SARS-CoV-2 outbreak in China. The findings offer valuable insights into the impact of intervention levels and duration, joint allocation schemes, as well as optimal allocation of test and treatment resources on cross-regional transmission of the pandemic.

Spreading Factor and Coding Rate Allocation Method for LoRa Network

Aiming at the problems of frequent packet loss and high energy consumption in large-scale LoRa networks, this paper proposes a joint allocation method of Spreading Factor (SF) and Coding Rate (CR). Firstly, we establish the effect relationship of SF and CR on Frame Error Rate(FER), and pre-allocate SF and CR based on the node position to minimize the energy consumption of the node while meeting the FER requirement. Then, according to the collision model, the collision probability of each SF group is calculated, and the sequential water injection method is used to equalize the collision probability within each SF group, to improve the average packet arrival rate of the entire network. The simulation results show that compared to mainstream algorithms, the proposed algorithm obtains a 13% increment in terms of the network average packet success probability, and a 104% one in terms of the average energy efficiency. The proposed algorithm has high application value in many application scenarios such as smart agriculture and smart cities.

Digital Twin Power Grid Oriented Mobile Edge Network Resource Allocation Model

The integration of vast quantities of clean energy causes intense impact on the power grid reliability. Digital Twin can be used to handle the high‐growth data flow generated in the power grid, and thus accelerate its digitalization process to fulfill the clean energy consumption target. As the traditional central cloud network structure can no longer effectively support the operation of digital twin power grid, the mobile edge network can be used as a reasonable substitute. To support the information interaction process of the mobile edge network, considering total profit, time delay and energy consumption, this paper proposes the mobile edge network communication and computing resource joint allocation model. On the basis, the GA‐PSO algorithm is proposed to solve the resource allocation optimization function. The feasibility and effectiveness of the proposed model is verified by the related simulation experiment. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Approximately Lossless Model Compression-Based Multilayer Virtual Network Embedding for Edge–Cloud Collaborative Services

Edge-cloud collaboration integrated with network virtualization is indispensable for diversified edge services. Meanwhile, the multilayer elastic optical network (ML-EON) is a promising underlying network for virtual network requests (VNRs) customized for edge-cloud collaborative services. However, the joint allocation of computing resources and high-dimensional ML-EON resources in virtual network embedding (VNE) will pose great computational complexity for online service deployment. In this paper, we propose an approximately lossless model compression mechanism to ease the computing burden of the VNE over ML-EON for edge-cloud collaborative services. An integer quadratic constraint programming (IQCP) model is established for the problem. Model compression based on Virtual Link Mapping Cost Estimation (VLMCE) is investigated to shield the variables and constraints related to ML-EON. In particular, the resource metric and topology metric are introduced into VLMCE to cope with resource contentions among virtual links in the same VNR and improve estimation accuracy. The model solving relies on Hopfield neural network (HNN) is further studied, where optimizing the compressed model is losslessly converted to minimizing the energy function of HNN. The experimental results reveal that the proposed mechanism guarantees an approximately lossless algorithm performance and a high time efficiency compared with the original IQCP model. The performances of VNR cost and blocking ratio are also promoted compared with the benchmarks.

Minimizing energy consumption by joint radio and computing resource allocation in Cloud-RAN

Cloud-RAN is a key 5G enabler; it centralizes the baseband processing of several base stations by executing the baseband functions in a centralized, virtualized, and shared entity known as the Base Band Unit (BBU)-Pool. Cloud-RAN paves the way for joint management of the radio and computing resources of multiple base stations. In fact, centralization and virtualization allow for decreasing energy consumption which decreases Capital Expenditure (CAPEX) and Operational Expenditure (OPEX). Cloud-RAN architecture permits jointly allocating the radio and computing resources of multiple base stations. The radio resources include the Resource Blocks (RBs), the transmission power, and the Modulation Coding Scheme (MCS), whereas the computing resources include the CPUs resources. This paper investigates the potential benefits that could be scored thanks to the joint allocation of these two types of resources, with respect to energy consumption and overall throughput, when radio resources are finite and computing resources are not. The latter is an effect of the C-RAN architecture, which allows scalability and fast computing resource provisioning. Due to the unconstrained availability of computing resources, the joint allocation of radio and computing resources has a negligible impact when the objective is throughput maximization. However, it is highly beneficial when the target is energy consumption minimization in comparison to the sequential allocation that consists of allocating radio resources first, and then computing resources are allocated. For that, we formulate a Mixed Integer Linear Programming (MILP) problem having the objective of minimizing energy consumption. When the goal is to minimize energy consumption, the joint allocation of radio and computing resources reduces the total energy consumption by up to 21.3% when compared to the case where radio and computing resources in the BBU pool are allocated sequentially. Furthermore, given the NP-hardness of solving a MILP problem, we propose a two-step low-complexity matching game-based algorithm with a transmission power adjustment mechanism that aims at performing close to the MILP solver. The results show that our proposed matching game algorithm is a good alternative for solving the joint-allocation MILP problem, producing results that are very close to the MILP optimal solutions.

DRL-assisted joint allocation of antenna, radio, and front-haul resources in TWDM-PON-based NG-RANs with mMIMO-enabled beamforming

The high spectrum efficiency of massive multiple input multiple output (mMIMO)-enabled beamforming is attractive for solving the massive number of connections in next-generation radio access networks (NG-RANs). However, the beamforming based on multiple antenna sub-arrays to support multi-connections would introduce extra 2D antenna sub-array selection and radio resource block (RB) allocation; meanwhile, considering the front-haul bandwidth consumption would reduce as much as possible the redundant data transmitting over front-haul in the NG-RANs. Therefore, a key issue is how to coordinate 2D antenna sub-array selection and radio RB allocation to minimize the front-haul bandwidth and maximize the radio RB utilization when accommodating a set of beam antenna array (BAA) requests. In this paper, we first establish a 3D BAA mapping model to jointly optimize 2D antenna sub-array selection and radio RB allocation, which would in turn affect the allocation of the front-haul wavelength bandwidth in a time and wavelength division multiplexed passive optical network (TWDM-PON)-based front-haul with the mMIMO antenna system. An integer linear programming mathematical model is formulated, and a novel deep reinforcement learning (DRL)-based algorithm is proposed to optimize the front-haul bandwidth and radio RB utilization. Besides, two heuristic algorithms are developed with different antenna sub-array selection policies as the benchmarks. The extensive simulation results show that our proposed DRL-based algorithm can attain the lowest average cost (AC) for different numbers of BAA requests considered by seeking the optimal trade-off between the front-haul bandwidth consumption and the number of the used antennas. Compared with the inter-sub-array benchmark, the DRL-based algorithm can achieve up to 7.5% AC reduction, which is mainly attributed to the 21.2% reduction of the front-haul bandwidth without increasing the number of used antennas.

Resource Allocation for Multiuser Edge Inference With Batching and Early Exiting

The deployment of inference services at the network edge, called edge inference, offloads computation-intensive inference tasks from mobile devices to edge servers, thereby enhancing the former’s capabilities and battery lives. In a multiuser system, the joint allocation of communication-and-computation (C2) resources (i.e., scheduling and bandwidth allocation) is made challenging by adopting efficient inference techniques, batching and early exiting, and further complicated by the heterogeneity in users’ requirements on accuracy and latency. Batching groups multiple tasks into a single batch for parallel processing to reduce time-consuming memory access and thereby boosts the throughput (i.e., completed task per second). On the other hand, early exiting allows a task to exit from a deep-neural network without traversing the whole network, thereby supporting a tradeoff between accuracy and latency. In this work, we study optimal C2 resource allocation with batching and early exiting, which is an NP-complete integer programming problem. A set of efficient algorithms are designed under the criterion of maximum throughput by tackling the challenge. First, consider the case with batching but without early exiting. The target problem is solved optimally using a proposed best-shelf-packing algorithm that nests a threshold-based scheme, which selects users with the best channels and meeting the computation-time constraints, in a sequential search for the maximum batch size. Next, consider the general case with batching and early exiting. A low-complexity sub-optimal algorithm for C2 resource allocation is developed by modifying the preceding algorithm to exploit early exiting for latency reduction. On the other hand, the optimal approach is developed based on nesting a depth-first tree-search with intelligent online pruning into a sequential search for the maximum batch size. The key idea is to derive pruning criteria based on the simple greedy solution for the target problem without a bandwidth constraint and apply the result to designing an intelligent online pruning scheme. Experimental results demonstrate that both optimal and sub-optimal C2 resource allocation algorithms can leverage integrated batching and early exiting to double the inference throughput compared with conventional schemes.

Joint allocation of transmit-receive resource for MIMO-STAP

Aiming to address the issue of target detection performance degradation due to limited resources of airborne multiple-in multiple-out (MIMO) radar, a joint allocation method of transmit-receive spatial-temporal resources is developed to ameliorate the detection performance of MIMO radar-based space-time adaptive processing (STAP). A MIMO-STAP model containing space-time resources to be configured is firstly constructed. Then, with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR), a problem to jointly design the transmitting and receiving elements, transmitting pulses, baseband waveform, and receiving weight is formulated. To tackle the resultant complex nonlinear joint optimization issue, it is decomposed into four independent sub-problems based on the alternating iteration strategy, then the sub-problems are efficiently solved by corresponding optimization techniques, i.e., the optimal antenna pulse subset can be selected by employing the sequential convex approximation (SCA) method, the transmitting waveform is optimized using the semi-positive definite program (SDP) and randomization method, and the receiving weight is designed under the minimum variance distortionless response (MVDR) criteria, which work iteratively to achieve an effective solution to the joint optimization problem. Extensive simulation results verify the effectiveness of the developed algorithm.

Joint Allocations of Radio and Computational Resource for User Energy Consumption Minimization Under Latency Constraints in Multi-Cell MEC Systems

This paper investigates the joint allocation of radio resources (i.e., base stations (BSs), sub-channels, and uplink transmission powers) and computational resources for a multicell mobile edge computing (MEC) system, aimed at minimizing the sum energy consumption of all mobile terminals under strict delay and signal-to-interference-plus-noise ratio (SINR) constraints. The problem is challenging to solve due to the highly coupled mixed-integer variables and the complicated expressions of the constraints. To tackle the difficulty, we decompose the problem into two sub-problems: the power and computational resource allocation sub-problem and the user-BS-subchannel association sub-problem. For the first sub-problem, we derive the optimal computational resource as a function of the optimal transmission power. Then the power optimization problem is distributed to each cell, and the local-optimal power solution within each cell is derived in closed-form expressions. For the second sub-problem, we design an optimal modified-cutting-plane (MCP) algorithm with exponential time-complexity. A polynomial-time near-optimal pivoting-and-subgradient (PS) algorithm is further proposed to reduce the complexity. Simulation results demonstrate that the PS algorithm achieves comparable performance to the optimal MCP algorithm, while they both outperform the benchmark schemes in most cases.

Joint Aperture and Power Allocation Strategy for a Radar Network Localization System Based on Low Probability of Interception Optimization

In the process of using the Distributed Radar Network Localization System (DRNLS) further to improve the survivability of a carrier platform, the random characteristics of the system's Aperture Resource Allocation (ARA) and Radar Cross Section (RCS) are often not fully considered. However, the random characteristics of the system's ARA and RCS will affect the power resource allocation of the DRNLS to a certain extent, and the allocation result is an essential factor determining the performance of the DRNLS's Low Probability of Intercept (LPI). Therefore, a DRNLS still has some limitations in practical application. In order to solve this problem, a joint allocation scheme of aperture and power for the DRNLS based on LPI optimization (JA scheme) is proposed. In the JA scheme, the fuzzy random Chance Constrained Programmin model for radar antenna aperture resource management (RAARM-FRCCP model) can minimize the number of elements under the given pattern parameters. The random Chance Constrained Programmin model for minimizing Schleher Intercept Factor (MSIF-RCCP model) built on this basis can be used to achieve DRNLS optimal control of LPI performance on the premise of ensuring system tracking performance requirements. The results show that when RCS has some randomness, its corresponding uniform power distribution result is not necessarily the optimal scheme. Under the condition of meeting the same tracking performance, the required number of elements and power will be reduced to a certain extent compared with the number of elements in the whole array and the power corresponding to the uniform distribution. The lower the confidence level is, the more times the threshold is allowed to pass, and the lower the power is, so that the DRNLS can have better LPI performance.

Joint allocation of transmit power and signal bandwidth for distributed cognitive tracking radar network using cooperative game

The radar network can achieve high target tracking accuracy by felicitously distributing the transmitting resources of each node. However, the influence of multiple transmitting resources of each radar node on target tracking accuracy is coupled with each other. We propose a joint allocation strategy of transmit power and signal bandwidth based on the cooperative game for the distributed cognitive tracking radar network. We employ the determinant of the Fisher information matrix to evaluate the target tracking accuracy and analyze the influence of each radar node's transmit power and signal bandwidth on target tracking accuracy. The joint allocation problem of transmit power and signal bandwidth is modeled as a cooperative game. We use the algorithm combining the cyclic minimization with the Shapley value to solve the cooperative game model. The weighted graph is used to simplify the calculation of the Shapley value. Simulation experiments are carried out in single-target tracking and multi-target tracking scenes. The results demonstrate that the proposed method can approach the centralized resource allocation method on the target tracking performance with a lower computational load.

A Joint Allocation Method of Multi-Jammer Cooperative Jamming Resources Based on Suppression Effectiveness

This paper studies the resource allocation problem when multiple jammers follow the aircraft formation to support ground penetration. A joint optimization allocation method of multi-jammer beam-power based on the improved artificial bee colony (IABC) algorithm is proposed. The air-to-ground “many-to-many” assault of the multi-jammer cooperative suppression jamming model is given. The constant false alarm probability detection model of the networked radar is used to evaluate the suppression effect, and a coordinated control model of multi-jammer jamming beams and emitting power is established. The optimal allocation scheme under different combat scenarios is solved by using the IABC algorithm. The search efficiency of the ABC algorithm is improved by cross mutation operation and the replacement of the worst nectar source, and the search performance of the algorithm is enhanced by the random key encoding. Due to the infeasible solution generated by the special random key encoding method, the feasible adjustment strategy is adopted. By changing the jamming parameters, the effect on the detection probability of the radar network is analyzed. Compared to the GWO, SCA, BBO and ABC algorithms, the jamming resource allocation scheme obtained by the proposed IABC algorithm makes the radar detection probability lower. The IABC algorithm has better global search capability and robustness.

A Power-Frequency Joint Optimization Frequency Hopping Strategy for Covert Communication

The security of wireless communication has been a paramount concern. In this letter, we propose a novel power-frequency joint allocation strategy (PFJAS), which can achieve high anti-jamming capability and covert performance for wireless communication systems. Specifically, a joint transmit power and frequency optimization problem is formulated to maximize the detection error probability at the warden, under constraints of transmission reliability and frequency selection uniformity. Then, the formulated optimization problem is solved by utilizing an alternating algorithm, where the power allocation and frequency selection (PAFS) algorithm based on Block Coordinate Descent (BCD) is iteratively performed until satisfying the performance requirement. Numerical results show our proposed PFJAS can achieve high anti-jamming capability and covert performance by exploiting the background noise in the environment.

Deep Reinforcement Learning-Based Resource Allocation for Content Distribution in IoT-Edge-Cloud Computing Environments

With the emergence of intelligent terminals, the Internet of Vehicles (IoV) has been drawing great attention by taking advantage of mobile communication technologies. However, high computation complexity, collaboration communication overhead and limited network bandwidths bring severe challenges to the provision of latency-sensitive IoV services. To overcome these problems, we design a cloud-edge cooperative content-delivery strategy in asymmetrical IoV environments to minimize network latency by providing optimal computing, caching and communication resource allocation. We abstract the joint allocation issue of heterogeneous resources as a queuing theory-based latency minimization objective. Next, a new deep reinforcement learning (DRL) scheme works in each network node to achieve optimal content caching and request routing on the basis of the perceptive request history and network state. Extensive simulations show that our proposed strategy has lower network latency compared with the current solutions in the cloud-edge collaboration system and converges fast under different scenarios.