Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks

In this paper, maximizing energy efficiency (EE) through radio resource allocation for renewable energy powered heterogeneous cellular networks (HetNet) with energy sharing, is investigated. Our goal is to maximize the network EE, conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources. We define the objective function as a sum weighted EE of all links in the HetNet. We formulate the resource allocation problem in terms of subcarrier assignment, power allocation and energy sharing, as a mixed combinatorial and non-convex optimization problem. We propose an energy efficient resource allocation scheme, including a centralized resource allocation algorithm for iterative subcarrier allocation and power allocation in which the power allocation problem is solved by analytically solving the Karush-Kuhn-Tucker (KKT) conditions of the problem and a water-filling problem thereafter and a low-complexity distributed resource allocation algorithm based on reinforcement learning (RL). Our numerical results show that both centralized and distributed algorithms converge with a few times of iterations. The numerical results also show that our proposed centralized and distributed resource allocation algorithms outperform the existing reference algorithms in terms of the network EE.     

多项目管理中企业资源配置效率模型

针对信息环境下企业多项目管理中资源配置这一核心问题,应用随机理论确定了企业资源多项目并行配置中的资源等效效率概念和效率转换系数概念,建立了资源配置效率模型,通过对其数学方程的分析给出了相应的算法.通过资源配置效率模型实现企业资源的合理配置,有效支持多项目管理.     

Task-Based Resource Allocation Bid in Edge Computing Micro Datacenter

Edge computing attracts online service providers (SP) to offload services to edge computing micro datacenters that are close to end users. Such offloads reduce packet-loss rates, delays and delay jitter when responding to service requests. Simultaneously, edge computing resource providers (RP) are concerned with maximizing incomes by allocating limited resources to SPs. Most works on this topic make a simplified assumption that each SP has a fixed demand; however, in reality, SPs themselves may have multiple task-offloading alternatives. Thus, their demands could be flexibly changed, which could support finer-grained allocations and further improve the incomes for RPs. Here, we propose a novel resource bidding mechanism for the RP in which each SP bids resources based on the demand of a single task (task-based) rather than the whole service (service-based) and then the RP allocates resources to these tasks with following the resource constraints at edge servers and the sequential rule of task-offloading to guarantee the interest of SPs. We set the incomes of the RP as our optimization target and then formulate the resource allocation problem. Two typical greedy algorithms are adopted to solve this problem and analyze the performance differences using two different bidding methods. Comprehensive results show that our proposal optimizes resource utilization and improves the RP’s incomes when resources in the edge computing datacenter are limited.     

A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy consumption.     

Machine Learning Based Resource Allocation of Cloud Computing in Auction

Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to those of the optimal allocation solution. The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.     

Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

The emergence of Beyond 5G (B5G) and 6G networks translated personal and industrial operations highly effective, reliable, and gainful by speeding up the growth of next generation Internet of Things (IoT). Industrial equipment in 6G encompasses a huge number of wireless sensors, responsible for collecting massive quantities of data. At the same time, 6G network can take real-world intelligent decisions and implement automated equipment operations. But the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be taken. This has become mandatory for optimal resource allocation in 6G-enabled industrial applications. In this scenario, the current research paper introduces a new metaheuristic resource allocation strategy for cluster-based 6G industrial applications, named MRAS-CBIA technique. MRAS-CBIA technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial applications. The proposed MRAS-CBIR technique involves three major processes. Firstly, Weighted Clustering Technique (WCT) is employed to elect the optimal Cluster Heads (CHs) or coordinating agents with the help of three parameters namely, residual energy, distance, and node degree. Secondly, Decision Tree-based Location Prediction (DTLP) mechanism is applied to determine the exact location of Management Agent (MA). Finally, Fuzzy C-means with Tunicate Swarm Algorithm (FCM-TSA) is used for optimal resource allocation in 6G industrial applications. The performance of the proposed MRAS-CBIA technique was validated and the results were examined under different dimensions. The resultant experimental values highlighted the superior performance of MRAS-CBIR technique over existing state-of-the-art methods.     

Efficient UAV Communications: Recent Trends and Challenges

Unmanned Ariel Vehicles (UAVs) are flying objects whose trajectory can be remotely controlled. UAVs have lot of potential applications in the areas of wireless communications, internet of things, security, traffic management, monitoring, and smart surveying. By enabling reliable communication between UAVs and ground nodes, emergency notifications can be efficiently and quickly disseminated to a wider area. UAVs can gather data from remote areas, industrial units, and emergency scenarios without human involvement. UAVs can support ubiquitous connectivity, green communications, and intelligent wireless resource management. To efficiently use UAVs for all these applications, important challenges need to be investigated. In this paper, we first present a detailed classification of UAVs based on factors such as their size, communication range, weight, and flight altitude. We also explain the hardware system configuration and uses of these UAVs. We present a brief overview of recent work done related to three major challenges in UAVs. These challenges include trajectory control, energy efficiency and resource allocation. We also present three open challenges and future opportunities for efficient UAV communications. These include use of learning algorithms for resource allocation and energy efficiency in UAVs, intelligent surfaces-based communications for enhanced reliability in UAVs, and security algorithms to combat malicious attacks against UAVs.     

The Integration Model of Closed-Loop Supply Chain Resource Allocation Considering Remanufacturing

Logistics resource planning is an integration model of materials requirement planning and distribution resource planning which is a resource allocation technology. It is a technology of satisfying both production material supply and resource allocation optimization which is based on inventory management. For the remanufacturing supply chain, recycling and rebuilding of products form a reverse materials movement loop which challenges the traditional logistics resource planning system. For the characteristics of reverse logistics of remanufacturing supply chain, we propose a closed-loop supply chain resource allocation model based on autonomous multi-entity. We focus on integration resource allocation model of materials requirement planning and distribution resource planning considering remanufacturing.     

基于工作流网的应急资源配置与路径规划集成优化

应急管理决策通常包括站点选址、资源配置、运输调度等内容,如何从应急处置整体流程控制的视角对决策内容进行集成建模及优化,是应急管理研究付诸实际应用的关键。本文提出具有资源和不确定时间约束的应急工作流网模型,通过三类库所（状态库所、动作库所、资源库所）及三类时间属性（可视时间、静态时间、动态时间）,揭示多部门联合应急中的作业时序与资源占用关系。在给定整体流程最大完成时间的条件下,以资源消耗与占用成本、资源运输与惩罚成本总和为目标函数,建立应急资源配置与路径规划的集成问题模型,并采用遗传粒子群混合算法对问题进行求解。根据遗传优化得到的应急资源配置方案,借助应急工作流网计算各动作库所、状态库所的时间参数,以此作为约束条件利用嵌套的粒子群算法进行资源运输策略优化。     

资源配置有效性分析的RPM方法

中国企业的资源配置集约化水平不高,应对经营资源投入效果予以改善就成为企业的战略重点。RPM是一种有效的分析工具,可用于分析企业内资源配置有效性,也可用于分析企业间竞争态势。     

Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected, so the problem is modeled as a stochastic non-cooperative game. Hence, each agent becomes a player and they make decisions together to achieve global optimization. Thereby, the multi-agent Q-learning algorithm based on game theory is established. Secondly, in order to accelerate the convergence rate of multi-agent Q-learning, we consider a power allocation strategy based on Fuzzy Cmeans (FCM) algorithm. The strategy firstly groups the D2D users by FCM, and treats each group as an agent, and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users. The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system. In particular, FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.     

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.     

Stackelberg Game-Based Resource Allocation with Blockchain for Cold-Chain Logistics System

Cold-chain logistics system (CCLS) plays the role of collecting and managing the logistics data of frozen food. However, there always exist problems of information loss, data tampering, and privacy leakage in traditional centralized systems, which influence frozen food security and people’s health. The centralized management form impedes the development of the cold-chain logistics industry and weakens logistics data availability. This paper first introduces a distributed CCLS based on blockchain technology to solve the centralized management problem. This system aggregates the production base, storage, transport, detection, processing, and consumer to form a cold-chain logistics union. The blockchain ledger guarantees that the logistics data cannot be tampered with and establishes a traceability mechanism for food safety incidents. Meanwhile, to improve the value of logistics data, a Stackelberg game-based resource allocation model has been proposed between the logistics data resource provider and the consumer. The competition between resource price and volume balances the resource supplement and consumption. This model can help to achieve an optimal resource price when the Stackelberg game obtains Nash equilibrium. The two participants also can maximize their revenues with the optimal resource price and volume by utilizing the backward induction method. Then, the performance evaluations of transaction throughput and latency show that the proposed distributed CCLS is more secure and stable. The simulations about the variation trend of data price and amount, optimal benefits, and total benefits comparison of different forms show that the resource allocation model is more efficient and practical. Moreover, the blockchain-based CCLS and Stackelberg game-based resource allocation model also can promote the value of logistic data and improve social benefits.     

Dynamic Resource Pricing and Allocation in Multilayer Satellite Network

The goal of delivering high-quality service has spurred research of 6G satellite communication networks. The limited resource-allocation problem has been addressed by next-generation satellite communication networks, especially multilayer networks with multiple low-Earth-orbit (LEO) and non-low-Earth-orbit (NLEO) satellites. In this study, the resource-allocation problem of a multilayer satellite network consisting of one NLEO and multiple LEO satellites is solved. The NLEO satellite is the authorized user of spectrum resources and the LEO satellites are unauthorized users. The resource allocation and dynamic pricing problems are combined, and a dynamic game-based resource pricing and allocation model is proposed to maximize the market advantage of LEO satellites and reduce interference between LEO and NLEO satellites. In the proposed model, the resource price is formulated as the dynamic state of the LEO satellites, using the resource allocation strategy as the control variable. Based on the proposed dynamic game model, an open-loop Nash equilibrium is analyzed, and an algorithm is proposed for the resource pricing and allocation problem. Numerical simulations validate the model and algorithm.     

基于实数编码遗传算法的项目组合规模决策

指出了应用二进制编码遗传算法进行项目组合选择的局限性,提出利用实数编码代替二进制编码进行项目组合规模决策.借鉴双赌论选择遗传算法的思想建立了基于实数编码的项目组合规模决策遗传算法,最后通过算例探讨了实数编码和二进制编码两种遗传算法在项目组合决策中的优劣.     

A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper, we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment. In general, the different slices for different task scenarios exist in the same edge layer synchronously. A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity. In the condition, the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment. Based on the slicing and container concept, we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme (TDACP). The scheme divides the resource allocation and management work into three stages in this paper: In the first stage, it designs reasonably strategy to allocate resources to different task slices according to demand. In the second stage, it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement (SLA) of the virtual machine in different slices. In the third stage, it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers. Thus, it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost. The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy. It adjusts the number of equivalent virtual machines based on the SLA range of system parameter, and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear. The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices.     

Optimal Resource Allocation and Quality of Service Prediction in Cloud

In the present scenario, cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients. Resources are in self-administration; consequently, clients can adjust their usage according to their requirements. Resource usage is estimated and clients can pay according to their utilization. In literature, the existing method describes the usage of various hardware assets. Quality of Service (QoS) needs to be considered for ascertaining the schedule and the access of resources. Adhering with the security arrangement, any additional code is forbidden to ensure the usage of resources complying with QoS. Thus, all monitoring must be done from the hypervisor. To overcome the issues, Robust Resource Allocation and Utilization (RRAU) approach is developed for optimizing the management of its cloud resources. The work hosts a numerous virtual assets which could be expected under the circumstances and it enforces a controlled degree of QoS. The asset assignment calculation is heuristic, which is based on experimental evaluations, RRAU approach with J48 prediction model reduces Job Completion Time (JCT) by 4.75 s, Make Span (MS) 6.25, and Monetary Cost (MC) 4.25 for 15, 25, 35 and 45 resources are compared to the conventional methodologies in cloud environment.     

云制造模式下多服务组合结构的资源分配

云制造环境下，如何提高平台型企业分配资源的效率，解决多种服务组合结构中同时出现的复杂制造任务问题亟待解决。本文综合权衡平台和需求方的利益，建立了需求方服务质量指标和平台柔性指标的多属性评价方法，构建了资源分配的双层结构优化决策模型，设计了求解稳定分配方案的算法，并论证了云制造平台供应链的Pareto最优性。最后，通过算例验证整个分配机制的可行性和有效性。     

A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning

One of the most effective technology for the 5G mobile communications is Device-to-device (D2D) communication which is also called terminal pass-through technology. It can directly communicate between devices under the control of a base station and does not require a base station to forward it. The advantages of applying D2D communication technology to cellular networks are: It can increase the communication system capacity, improve the system spectrum efficiency, increase the data transmission rate, and reduce the base station load. Aiming at the problem of co-channel interference between the D2D and cellular users, this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning, which creates multi-agent learners from multiple D2D users, and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users. The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted. The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users. Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.     

A Method for Assessing the Fairness of Health Resource Allocation Based on Geographical Grid

The assessment of the fairness of health resource allocation is an important part of the study for the fairness of social development. The data used in most of the existing assessment methods comes from statistical yearbooks or field survey sampling. These statistics are generally based on administrative areas and are difficult to support a fine-grained evaluation model. In response to these problems, the evaluation method proposed in this paper is based on the query statistics of the geographic grid of the target area, which are more accurate and efficient. Based on the query statistics of hot words in the geographic grids, this paper adopts the maximum likelihood estimation method to estimate the population in the grid region. Then, according to the statistical yearbook data of Hunan province, the estimated number and actual number of hospitals in each grid are analyzed and compared to measure the fairness of health resource allocation in the target region. Experiments show that the geographical grid population assessment based on hot words is more accurate and close to the actual value. The estimated average error is only about 17.8 percent. This method can assess the fairness of health resource allocation in any scale, and is innovative in data acquisition and evaluation methods.