Combined Resource Allocation Research Articles

Joint mode selection and resource allocation for cellular V2X communication using distributed deep reinforcement learning under 5G and beyond networks

Vehicle-to-everything (V2X) communication via cellular networks is a promising technique for 5G and beyond networks. The cars interact directly with one another, as well as with the infrastructure and various vehicles on the road, in this mode. It enables the interchange of time-sensitive and safety-critical data. Despite these benefits, unstable vehicle-to-vehicle (V2V) communications, insufficient channel status information, high transmission overhead, and the considerable communication cost of centralized resource allocation systems all pose challenges for defense applications. To address these difficulties, this study proposes a combined mode selection and resource allocation system based on distributed deep reinforcement learning (DRL) to optimize the overall network sum rate while maintaining the reliability and latency requirements of V2V pairs and the data rate of V2R connections. Because the optimization issue is non-convex and NP-hard, it cannot be solved directly. To tackle this problem, the defined problem is first translated into machine learning form using the Markov decision process (MDP) to construct the reward function and decide whether agent would conduct the action. Following that, the distributed coordinated duelling deep Q-network (DDQN) method based on prioritized sampling is employed to improve mode selection and resource allocation. This approach learns the action-value distribution by estimating both the state-value and action advantage functions using duelling deep networks. The results of the simulation show that the suggested scheme outperforms state-of-the-art decentralized systems in terms of sum rate and QoS satisfaction probability.

Developing a combined framework for priority setting in integrated health and social care systems

BackgroundThere is an international move towards greater integration of health and social care to cope with the increasing demand on services.. In Scotland, legislation was passed in 2014 to integrate adult health and social care services resulting in the formation of 31 Health and Social Care Partnerships (HSCPs). Greater integration does not eliminate resource scarcity and the requirement to make (resource) allocation decisions to meet the needs of local populations. There are different perspectives on how to facilitate and improve priority setting in health and social care organisations with limited resources, but structured processes at the local level are still not widely implemented. This paper reports on work with new HSCPs in Scotland to develop a combined multi-disciplinary priority setting and resource allocation framework.MethodsTo develop the combined framework, a scoping review of the literature was conducted to determine the key principles and approaches to priority setting from economics, decision-analysis, ethics and law, and attempts to combine such approaches. Co-production of the combined framework involved a multi-disciplinary workshop including local, and national-level stakeholders and academics to discuss and gather their views.ResultsThe key findings from the literature review and the stakeholder workshop were taken to produce a final combined framework for priority setting and resource allocation. This is underpinned by principles from economics (opportunity cost), decision science (good decisions), ethics (justice) and law (fair procedures). It outlines key stages in the priority setting process, including: framing the question, looking at current use of resources, defining options and criteria, evaluating options and criteria, and reviewing each stage. Each of these has further sub-stages and includes a focus on how the combined framework interacts with the consultation and involvement of patients, public and the wider staff.ConclusionsThe integration agenda for health and social care is an opportunity to develop and implement a combined framework for setting priorities and allocating resources fairly to meet the needs of the population. A key aim of both integration and the combined framework is to facilitate the shifting of resources from acute services to the community.

LPLSG: Prediction of lncRNA-protein Interaction Based on Local Network Structure

Background: The interaction between RNA and protein plays an important role in life activities. Long ncRNAs (lncRNAs) are large non-coding RNAs, and have received extensive attention in recent years. Because the interaction between RNA and protein is tissue-specific and condition-specific, it is time-consuming and expensive to predict the interaction between lncRNA and protein based on biological wet experiments. Objective: The contribution of this paper is to propose a method for prediction based on the local structural similarity of lncRNA-protein interaction (LPI) network. Methods: The method computes the local structure similarity of network space, and maps it to LPI space, and uses an innovative algorithm that combined Resource Allocation and improved Collaborative Filtering algorithm to calculate the potential LPI. Conclusion: AUPR and AUC are significantly better than the five popular baseline methods. In addition, the case study shows that some results of LPLSG prediction on the actual data set have been verified by NPInterV4.0 database and some literatures.

Emergency shelter allocation planning technology for large-scale evacuation based on quantum genetic algorithm.

Shelter allocation is one of the most important measures in urban disaster prevention and mitigation planning. Meanwhile, it is essentially a comprehensive planning problem combining resource allocation and traffic routing. A reasonable allocation scheme can avoid congestion, improve evacuation efficiency, and reduce the casualty rate. Owing to the large region and large evacuation population demand, quickly solving the complex allocation problem is somewhat challenging, and thus, the optimal results are difficult to obtain with the increase of evacuation scale by traditional allocation methods. This article aims to establish a shelter allocation model for large-scale evacuation, which employs an improved quantum genetic algorithm (IQGA) based on spreading operation and considering the total evacuation distance, the capacity constraint of evacuation sites, and the dispersion of allocation results, and compare allocation schemes of the spreading model with those of models that consider different constraints. Results show that the allocation model with the spreading operation has better allocation results than that without the spreading operation. For the allocation model with spreading operation, the spreading model with different spreading speeds is more reasonable than that with the same spreading speed, and the allocation results are closer to the ideal results with the increase of constraints. In addition, according to the allocation results, the evacuation route map and the evacuation heat map are drawn to intuitively understand the distribution scheme of each shelter.

A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques.

The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization’s profit as well as users’ satisfaction. A customer’s request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.

Joint precoding schemes for flexible resource allocation in high throughput satellite systems based on beam hopping

Beam hopping technology provides a foundation for the flexible allocation and efficient utilization of satellite resources, which is considered as a key technology for the next generation of high throughput satellite systems. To alleviate the contradiction between resource utilization and co-frequency interference in beam hopping technology, this paper firstly studies dynamic clustering to balance traffic between clusters and proposes cluster hopping pool optimization method to avoid inter-cluster interference. Then based on the optimization results, a novel joint beam hopping and precoding algorithm is provided to combine resource allocation and intra-cluster interference suppression, which can make efficient utilization of system resources and achieve reliable and near-optimal transmission capacity. The simulation results show that, compared with traditional methods, the proposed algorithms can dynamically adjust to balance demand traffic between clusters and meet the service requirements of each beam, also eliminate the co-channel interference to improve the performance of satellite network.

Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.

The purposes are to improve the server deployment capability under Mobile Edge Computing (MEC), reduce the time delay and energy consumption of terminals during task execution, and improve user service quality. After the server deployment problems under traditional edge computing are analyzed and researched, a task resource allocation model based on multi-stage is proposed to solve the communication problem between different supporting devices. This model establishes a combined task resource allocation and task offloading method and optimizes server execution by utilizing the time delay and energy consumption required for task execution and comprehensively considering the restriction processes of task offloading, partition, and transmission. For the MEC process that supports dense networks, a multi-hybrid intelligent algorithm based on energy consumption optimization is proposed. The algorithm converts the original problem into a power allocation problem via a heuristic model. Simultaneously, it determines the appropriate allocation strategy through distributed planning, duality, and upper bound replacement. Results demonstrate that the proposed multi-stage combination-based service deployment optimization model can solve the problem of minimizing the maximum task execution energy consumption combined with task offloading and resource allocation effectively. The algorithm has good performance in handling user fairness and the worst-case task execution energy consumption. The proposed hybrid intelligent algorithm can partition tasks into task offloading sub-problems and resource allocation sub-problems, meeting the user’s task execution needs. A comparison with the latest algorithm also verifies the model’s performance and effectiveness. The above results can provide a theoretical basis and some practical ideas for server deployment and applications under MEC.

Combined Resource Allocation for Fronthaul and Backhaul to Improve Throughput and Fairness in HetNet Environment

Combined Resource Allocation for Fronthaul and Backhaul to Improve Throughput and Fairness in HetNet Environment

Two-Side Coalitional Matching Approach for Joint MIMO-NOMA Clustering and BS Selection in Multi-Cell MIMO-NOMA Systems

Resource management in multi-cell multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is challenged by computational complexity, flexible clustering, and potential channel correlation. In this paper, we focus on a combined resource allocation problem: NOMA mobile user (MU) clustering and the base station (BS) selection, to improve system data rate. Different from sum data rate maximization and max-min fairness, we introduce a new objective function, i.e., relative fairness, which integrates MU fairness into system data rate optimization to overcome the domination effect of BS in advantaged situations of sum data rate improving. Moreover, we derive the closed form solution of MIMO-NOMA resource allocation for a single cluster, and it can be employed for any size of cluster. Furthermore, we propose a new two-side coalitional matching approach to jointly optimize MIMO-NOMA clustering and BS selection, which is able to balance the tradeoff between MUs’ individual benefits and the overall network performance. The proposed approach is core stable. Pauta-criterion is employed on system performance evaluation to provide a judgement on win-win solutions. In simulation, extensive comparisons provide insightful understanding of our proposed MIMO-NOMA clustering strategy, relative fairness, and the proposed two-side coalitional matching approach.

A combined resource allocation framework for PEVs charging stations, renewable energy resources and distributed energy storage systems

The massive deployment of plug-in electric vehicles (PEVs), renewable energy resources (RES), and distributed energy storage systems (DESS) has gained significant interest under the smart grid vision. However, their special features and operational characteristics have created a paradigm shift in distribution network resource allocation studies. This paper presents a combined model formulation for the concurrent optimal resource allocation of PEVs charging stations, RES and DESS in distribution networks. The formulation employs a general objective function that optimizes the total Annual Cost of Energy (ACOE). The decision variables in the formulation are the locations and capacities of PEVs charging stations, RES, and DESS units. A Markov Chain Monte Carlo (MCMC) simulation model is utilized to account for the uncertainties of PEVs charging demand and output generation of RES units. Also, in order to enhance the accuracy of the resource allocation problem, the coordinated control of PEVs charging, RES output power, and DESS charging/discharging are incorporated in the formulated model. The formulation is decomposed into two interdependent sub-problems and solved using a combination of metaheuristic and deterministic optimization techniques. A sample case study is presented to illustrate the performance of the algorithm.

Curvilinear Relationship between Corporate Innovation and Environmental Sustainability

This paper proposes and tests the impacts of firms’ innovation activities, such as patents, on their involvement in environmental sustainability, such as environmental performance. Combining resource allocation and eco-innovation perspectives assuming constant levels of trade-off costs, this study proposes diminishing trade-off costs between corporate innovation and environmental sustainability. Specifically, this research hypothesizes a U-shaped relationship, suggesting that a firm will suffer trade-off costs between innovation and environment-oriented activities up to a certain level, and that once a firm accumulates an adequate level of innovation, it may reduce trade-off costs, caring more for environmental issues. A proposed hypothesis is supported by empirical testing of a sample of 11,657 firm-year observations with 1564 firms, spanning from 1991 to 2010. We also found that corporate patenting activities are relevant to undesirable impacts on environmental performance overall, instead of satisfying outcomes. We suggest that firms and managers should care for environmental sustainability issues once they accumulate an adequate level of innovation assets through patenting activities.

Systematic Literature Review on Resource Allocation and Resource Scheduling in Cloud Computing

The objective the work is intend to highlight the key features and afford finest future directions in the research community of Resource Allocation, Resource Scheduling and Resource management from 2009 to 2016. Exemplifying how research on Resource Allocation, Resource Scheduling and Resource management has progressively increased in the past decade by inspecting articles, papers from scientific and standard publications. Survey materialized in three fold process. Firstly, investigate on the amalgamation of Resource Allocation, Resource Scheduling and then proceeded with Resource management. Secondly, we performed a structural analysis on different author’s prominent contributions in the form of tabulation by categories and graphical representation. Thirdly, huddle with conceptual similarity in the field and also impart a summary on all resource allocations. In cloud computing environments, there are two players: cloud providers and cloud users. On one hand, providers hold massive computing resources in their large datacenters and rent resources out to users on a per-usage basis. On the other hand, there are users who have applications with fluctuating loads and lease resources from providers to run their applications. Further, delivers conclusions by conferring future research directions in the field of cloud computing, such as reduce clouds early in the Internet, combining Resource Allocation, Resource Scheduling and Resource management rather than a Cloud model for providing high quality results, etc.

Combined Resource Allocation System for Device-to-Device Communication towards LTE Networks

The LTE networks are being developed to grant mobile broadband services in the fourth generation (4G) systems and allow operators to use spectrum more efficiently.D2D communication is a promising technique to provide wireless services and enhance spectrum exploitation in the LTE Heterogeneous Networks (HetNets).D2D communication in HetNets allows users to communicate with each other directly by reusing the resources when communicating via the base stations. But during the downlink period, both the D2D receiver and the Heterogeneous Users equipment’s (HUE) experience interference caused by resource allocation. In this article, we identify and analyze the interference problem of HetNets caused by D2D transmitter during download. We propose a combined resource allocation and resource reuse method for LTE HetNets, where resource allocation to HUEs is employed on the basis of comparative fair algorithm and resource reuse to D2D users is employed on acquisitive empirical algorithm. This approach evaluates whether D2D mode is suitable or not by path loss evaluation, after that decreases the interference to HUE by selection of the minimum channel gain between HUE and D2D transmitter each time to mitigate interference. Our simulation results show that the efficiency and throughput of HetNets is improved by using the proposed method.

SC-FDMA-based resource allocation and power control scheme for D2D communication using LTE-A uplink resource

Abstract Device-to-device (D2D) communication-enabled cellular networks allow cellular devices to directly communicate with each other without any evolved NodeB (eNB). D2D communication aims to improve the spectral efficiency and increases the overall system capacity. For future mobile networks, intelligent radio resource allocation and power control schemes are required to accommodate the increasing number of cellular devices and their growing demand of data traffic. In this paper, a combined resource allocation and power control scheme for D2D communication is proposed. In the proposed scheme, D2D communication reuses the uplink (UL) resources of conventional cellular user equipments (CUEs); therefore, we have adopted single-carrier frequency division multiple access (SC-FDMA) as UL transmission scheme. The proposed scheme uses fractional frequency reuse (FFR)-based architecture to efficiently allocate the resources and mitigate the interference between CUEs and D2D user equipments (DUEs). In order to guarantee the user fairness, the proposed scheme uses the well-known proportional fair (PF) scheduling algorithm for resource allocation. We have also proposed an intelligent power control scheme which provides equal opportunity to both CUEs and DUEs to achieve a certain minimum signal-to-interference and noise ratio (SINR) value. The performance evaluation results show that the proposed scheme significantly improves the overall cell capacity and achieves low peak-to-average power ratio (PAPR).

Energy efficient transmission techniques for LTE

The goal of the European ICT Project EARTH is to enhance energy efficiency of current LTE cellular networks by 50 percent, enabling operators to provide more extensive coverage, namely to less densely populated areas, while ensuring high levels of efficiency, hence, reducing the carbon footprint. In order to achieve this objective, different strategies, at different levels, are being addressed. Energy efficient transmission techniques for LTE, proposed within the framework of EARTH, are addressed and discussed, with an emphasis on beamforming and MIMO. It is concluded that the strategic objective of EARTH is achievable. Combined MIMO mode selection, resource allocation, scheduling and precoding strategies will allow an energy consumption reduction of more than 50 percent in macro-cells. A reduction in MIMO power consumption of 91.7 percent (factor of 12) can be achieved in pico-cells.

Primary Care Spatial Density and Nonurgent Emergency Department Utilization: A New Methodology for Evaluating Access to Care

ObjectiveTo determine the spatial and demographic characteristics of pediatric patients who make nonurgent visits (NUVs) to an urban pediatric emergency department (ED). We hypothesized that the rate of NUVs would be inversely associated with the spatial density of primary care providers (PCPs). MethodsA retrospective, cross-sectional analysis was conducted for all visits to Washington, DC’s principal pediatric ED between 2003 and 2006. NUVs were defined by a unique algorithm combining resource allocation, ambulatory-sensitive diagnoses, and billing data. Multivariate linear regression analysis was used to determine the association of PCP density and demographic variables on the spatial rate of NUVs. ResultsOver the 4-year period, 35.1% (52,110) of the 148,314 ED visits by Washington, DC, residents were nonurgent. NUVs were most associated with neighborhood median household income <$40,000 and low spatial density of PCPs. For every 1-unit increase in PCP density, the spatial rate of NUVs decreased by 9%. The odds of a visit being nonurgent were significantly higher for African Americans and Hispanics than for whites (odds ratio [OR] 2.4, 95% confidence interval [CI] 2.19–2.64; and OR 2.6, 95% CI 2.36–2.86, respectively), for patients using public insurance versus private (OR 1.46, 95% CI 1.42–1.50), and for patients age <5 years (OR 2.66, 95% CI 2.60–2.72). ConclusionsLow spatial density of primary care is strongly associated with nonurgent ED utilization. Improving spatial distribution of primary care may decrease ED misuse and improve access to the medical home.

Vision of Resource, Structure, System and Chinese Food Security

Chinese food security has important strategic significance not only for native sustained economic and social development, but also has an important impact on world food security. At present, Chinese food security is facing three major issues which are serious resource constraints, complex structural contradictions and lag system supply. This paper presents the analytical framework of “resource • structure • System” and conducts theoretical and empirical analysis on the issue of food security in China by the method of system theory. The studies shows that the realization of food security in China should be based on its specific national conditions and reasonably and effectively combine resource allocation, structural optimization and system relief in order to provide continuous power for further enhancing the comprehensive support capacity of national food security.

A new multi-objective self-organizing optimization algorithm (MOSOA) for spatial optimization problems

Spatial planning is an important and complex activity. It includes land use planning and resource allocation as basic components. An abundance of papers can be found in the literature related to each one of these two aspects separately. On the contrary, a much smaller number of research reports deal with both aspects simultaneously. This paper presents an innovative evolutionary algorithm for treating combined land use planning and resource allocation problems. The new algorithm performs optimization on a cellular automaton domain, applying suitable transition rules on the individual neighbourhoods. The optimization process is multi-objective, based on non-domination criteria and self-organizing. It produces a Pareto front thus offering an advantage to the decision maker, in comparison to methods based on weighted-sum objective functions. Moreover, the present multi-objective self-organizing algorithm (MOSOA) can handle both local and global spatial constraints. A combined land use and water allocation problem is treated, in order to illustrate the cellular automaton optimization approach. Water is allocated after pumping from an aquifer, thus contributing a nonlinearity to the objective function. The problem is bi-objective aiming at (a) the minimization of soil and groundwater pollution and (b) the maximization of economic profit. An ecological and a socioeconomic constraint are imposed: (a) Groundwater levels at selected places are kept above prescribed thresholds. (b) Land use quota is predefined. MOSOA is compared to a standard multi-objective genetic algorithm and is shown to yield better results both with respect to the Pareto front and to the degree of compactness. The latter is a highly desirable feature of a land use pattern. In the land use literature, compactness is part of the objective function or of the constraints. In contrast, the present approach renders compactness as an emergent result.

Utility-directed resource allocation in virtual desktop clouds

User communities are rapidly transitioning their “traditional desktops” that have dedicated hardware and software installations into “virtual desktop clouds” (VDCs) that are accessible via thin-clients. To allocate and manage VDC resources for Internet-scale desktop delivery, existing works focus mainly on managing server-side resources based on utility functions of CPU and memory loads, and do not consider network health and thin-client user experience. Resource allocations without combined utility-directed information of system loads, network health and thin-client user experience in VDC platforms inevitably results in costly guesswork and over-provisioning of resources. In this paper, we develop an analytical model viz., “Utility-Directed Resource Allocation Model (U-RAM)” to solve the combined utility-directed resource allocation problem within VDCs. Our solution involves an iterative algorithm that leverages utility functions of system, network and human components obtained using a novel virtual desktop performance benchmarking toolkit viz., “VDBench” that we developed. The combined utility functions are used to direct decision schemes based on Kuhn–Tucker optimality conditions for creating user desktop pools and determining optimal resource allocation size/location. We deploy VDBench in a VDC testbed featuring: (a) popular user applications (Spreadsheet Calculator, Internet Browser, Media Player, Interactive Visualization), and (b) TCP/UDP based thin-client protocols (RDP, RGS, PCoIP) under a variety of user load and network health conditions. Simulation results based on the utility functions obtained from the testbed demonstrate that our solution maximizes VDC scalability i.e., ‘VDs per core density’, and ‘user connections quantity’, while delivering satisfactory thin-client user experience.

Pricing, Allocation, and Overbooking in Dynamic Service Network Competition When Demand Is Uncertain

We study the problem of combined pricing, resource allocation, and overbooking by service providers involved in dynamic noncooperative oligopolistic competition on a network that represents the relationships of the providers to one another and to their customers when service demand is uncertain. We propose, analyze, and compute solutions for a model that is more general than other models reported in the revenue management literature to date. In particular, previous models typically consider only three or four of five key revenue management features that we have purposely built into our model: (1) pricing, (2) resource allocation, (3) dynamic competition, (4) an explicit network, and (5) uncertain demand. Illustrative realizations of the abstract problem we study are those of airline revenue management and service provision by companies facing resource constraints. Under fairly general regularity conditions, we prove existence and uniqueness of a pure strategy Nash equilibrium for dynamic oligopolistic service network competition described by our model. We also show, for an appropriate notion of regularity, that competition leads to the underpricing of network services, a finding numerically illustrated by an example of intermediate size. Our proposed algorithm can be implemented using well‐known off‐the‐shelf commercial software.