Resource Allocation Issues Research Articles

SAT patterns and engineering and computer science college majors: an intersectional, state-level study

BackgroundNumerous efforts worldwide have been made to increase diversity in engineering and computer science (ECS), fields that pay well and promote upward mobility. However, in the United States (U.S.), females and students from underrepresented racial/ethnic minority groups (URM) still pursue ECS training far less than do their peers. The current study explored sex and racial/ethnic differences in ECS college enrollment as a function of math and verbal SAT score patterns (balanced or imbalanced) using an intersectional approach within a U.S. context. Data represented a census of students who took the SAT, graduated from all Virginia public high schools between 2006 and 2015, and enrolled in a 4-year college (N = 344,803).ResultsOur findings show, within each sex, URM students were at least as likely as their non-URM peers to enroll in ECS programs when they scored within similar SAT score ranges. Students were more likely to enroll in ECS programs if their SAT profile favored math, compared to students with similar math and verbal SAT scores (balanced profile). This overall pattern is notably less pronounced for URM female students; their propensity to major in ECS appeared to be largely independent of verbal scores.ConclusionsOur findings inform strategies to diversify ECS enrollment. If programs continue to emphasize SAT scores during admission decisions or if more systemic issues of resource allocation in secondary schools are not addressed, other efforts to broaden participation in ECS programs may fall short of goals. Our findings also highlight the importance of considering the intersection of sex and race/ethnicity for recruitment or other educational promotions.

Fog-GMFA-DRL: Enhanced deep reinforcement learning with hybrid grey wolf and modified moth flame optimization to enhance the load balancing in the fog-IoT environment

Internet of Things (IoT) can facilitate a plethora of data transactions among various servers. In the IoT, fog servers are utilized to achieve effective data transactions from dynamic devices. However, load balancing is still a significant task researcher mainly focus on mitigating the load balancing issue. Some virtual machines may be overloaded when other virtual machines are idle due to a bad scheduling policy. Therefore, the proposed model is based on dynamic load balancing in a fog-IOT environment by utilizing a novel hybrid Grey Wolf Optimization (GWO) with the Modified Moth Flame algorithm (MMFA). In addition, the GMFA mainly helps to enhance Deep reinforcement learning (DRL). The performance of the actor-critic based deep reinforcement learning (DRL) approach is enhanced with the GMFA algorithm, and this combined strategy is named GMFA-DRL. RL offers several advantages regarding resource allocation issues, and simulations demonstrate that it performs better than reactive techniques. The proposed GMFA-DRL approach is implemented through the Python-based platform-Jupyter. The performance is evaluated using performance matrices such as Throughput, Latency, Makespan, Load Balancing Level (LBL), and Energy Consumption. The simulation results illustrate that the proposed model achieves high Throughput, low energy consumption, minimum Latency, minimum Makespan, and load balancing results. Therefore, the proposed approach can be proven more effective than the existing technique.

Intelligent Virtual Resource Allocation of QoS-Guaranteed Slices in B5G-Enabled VANETs for Intelligent Transportation Systems

5G communication technologies and networks help researchers and engineers look into intelligent transportation systems (ITS) with a new eye, including vehicular ad hoc networks (VANET) application. Network function virtualization (NFV) and network slicing (NS) are accepted as two most promising technologies towards the agile and elastic network architecture of 5G and beyond 5G (B5G). However, previous researchers studied NFV and NS separately. In addition, learning technologies, such as reinforcement leaning (RL), graph-based learning, emerge so as to enhance the network intelligence and resource allocation in recent years. Inspired from these, we jointly explore intelligent resource allocation issue within B5G-enabled VANETs. At first, the novel virtual resource allocation framework supporting NFV and NS for providing quality of service (QoS)-guaranteed slices is constructed. Then, we formulate the virtual resource allocation of slices as the optimization problem, having the goals of providing guaranteed QoS performance and maximizing the net profit. Considering the non convex attributes of the formulated optimization problem, we propose one intelligent and feasible algorithm instead, including the details of the proposed intelligent algorithm. We record the results in order to validate the feasibility and highlights of our proposed algorithm. For example, our intelligent algorithm has the slice acceptance advantage of 5%, comparing with the best existing work.

MT3A : A Novel Multicast Routing, Spectrum and Modulation-Level Assignment in Elastic Optical Networks

Elastic Optical Networks (EONs) were introduced to eliminate the challenges of Wavelength Division Multiplexing (WDM) networks. EONs can improve resource efficiency compared to WDM networks. The issue of resource allocation in EONs is a major challenge for the optimal use of maximum network capacity for each communication demand. On the other hand, multicasting is defined as the transfer of data from one source to multiple destinations and it is one of the most effective methods based on cost and efficiency in order to provide flow in computer networks. In this article, we present a novel Multicast Two-part Two-path Two-direction Allocation (MT3A) scheme in order to routing, spectrum and modulation-level assignment for dynamic multicast traffic in EONs. With this scheme, each sub-request related to a multicast request is divided into two parts considering the property of Sliceable Bandwidth Variable Transponders (SBVTs) and is set up in two different directions in two different light-paths. By using the MT3A scheme compared to comparative schemes, with increasing the data rate, Bandwidth Blocking Probability (BBP) is reduced, which is very important due to increasing demand for contents with high data rate.

Resource Allocation With Multiagent Trading Over the Edge Services

A number of studies have recently emerged to address the issue of resource allocation in edge computing environments. However, there are few works considering how to optimize resource allocation while satisfying market's requirements in multiagent technique for distributed allocation of Edge resources in distributed control. This study use trading-based multiagent resource allocation model as an allocation mechanism to optimal allocate resources through genetic algorithm in an Edge computing environment. The proposed model supports the optimal process between Edge computing cases to apply and allows Edge buyers and Edge providers both to derive their own pricing strategies and to analyze the respective impact to their welfare. The k-pricing schemes are adjustly to meet the Edge users/providers requirement and constraints set by composed services.

Edge computing network resource allocation based on virtual network embedding

SummaryIt is expected that the demand for quality of service (QoS) and quality of experience (QoE) in future 6G scenarios will continue to increase, and edge computing (EC) will continue to receive widespread attention. However, the highly dynamic and connectivity complexity of edge computing networks (ECNs) pose severe challenges to its resource allocation issues. In addition, network virtualization (NV) is widely applied to increase the flexibility of network architectures. Based on the above inspirations, we adopt virtual network embedding (VNE) to make decisions on the resource allocation of ECN. Specifically, we deploy a deep reinforcement learning (DRL)‐based multi‐layer policy network, which is applied to extract environmental information, calculate the available resources of edge nodes, and screen candidate edge nodes that satisfy resource allocation conditions. Second, the resources of edge links are allocated according to the shortest path algorithm. Finally, we build a simulation environment to demonstrate the advantages of the proposed policy network through rich experiments.

Hypergraph-Based Resource Allocation for Ultra-Dense Wireless Network in Industrial IoT

As next generation wireless communication and smart manufacturing evolve, Industrial Internet of Things (IIoT) has emerged. Due to the advantage in coverage and improved capacity of cellular networks, femtocells are densely deployed in macrocell of cellular networks contributing to Ultra-dense Wireless Network (UDWN). Nevertheless, it is faced with the challenge of how to reduce interference to improve spectrum efficiency and system performance for Non-orthogonal Multiple Access (NOMA) in IIoT. To this end, we identify and formulate the channel resource allocation issue as a sum-rate maximization problem under the guarantee of Quality of Service (QoS) and fairness. To solve the problem, we first construct a hypergraph by considering interference relationships. Thereafter, we convert the original problem into a maximum weighted clique set problem in hypergraph theory, based on which a heuristic resource allocation algorithm is proposed. Moreover, we analyse the property of the proposed algorithm and its complexity. Finally, simulation results show that the proposed method outperforms graph-based algorithm and other hypergraph-based algorithm in improving the system throughput.

When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm

Motivated by the increasingly powerful computing capabilities of end-user equipment, and by the growing privacy concerns over sharing sensitive raw data, a distributed machine learning paradigm known as federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing privacy leakage. However, the conventional FL framework relies heavily on a single central server, and it may fail if such a server behaves maliciously. To address this single point of failure, in this work, a blockchain-assisted decentralized FL framework is investigated, which can prevent malicious clients from poisoning the learning process, and thus provides a self-motivated and reliable learning environment for clients. In this framework, the model aggregation process is fully decentralized and the tasks of training for FL and mining for blockchain are integrated into each participant. Privacy and resource-allocation issues are further investigated in the proposed framework, and a critical and unique issue inherent in the proposed framework is disclosed. In particular, a lazy client can simply duplicate models shared by other clients to reap benefits without contributing its resources to FL. To address these issues, analytical and experimental results are provided to shed light on possible solutions, i.e., adding noise to achieve local differential privacy and using pseudo-noise (PN) sequences as watermarks to detect lazy clients.

Resource Allocation for Text Semantic Communications

Semantic communications have shown its great potential to improve the transmission reliability, especially in the low signal-to-noise regime. However, resource allocation for semantic communications still remains unexplored, which is a critical issue in guaranteeing the semantic transmission reliability and the communication efficiency. To fill this gap, we investigate the spectral efficiency in the semantic domain and rethink the semantic-aware resource allocation issue. Specifically, taking text semantic communication as an example, the semantic spectral efficiency (S-SE) is defined for the first time, and is used to optimize resource allocation in terms of channel assignment and the number of transmitted semantic symbols. Additionally, for fair comparison of semantic and conventional communication systems, a transform method is developed to convert the conventional bit-based spectral efficiency to the S-SE. Simulation results demonstrate the validity and feasibility of the proposed resource allocation method, as well as the superiority of semantic communications in terms of the S-SE.

Heterogeneous Markov Decision Process Model for Joint Resource Allocation and Task Scheduling in Network Slicing Enabled Internet of Vehicles

In the Internet of Vehicles (IoV) with network slicing functions, both the inter-slice resource allocation and the intra-slice task scheduling have received extensive attention. However, the joint optimization of these two problems has not been fully studied. This letter focuses on the complexity issue of the joint resource allocation and task scheduling problem in the IoV with both ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) slices. A heterogeneous Markov decision process (HMDP) which models the resource allocation process and the task scheduling process in two different layers is proposed. Benefiting from the two-layer structure of the HMDP, the size of action space is significantly reduced, and the asynchronous decisions of different sub-MDPs are enabled. The corresponding layered deep reinforcement learning (DRL) architecture is also designed to solve the HMDP-based optimization problem. Simulation results show that the layered DRL-based algorithm outperforms other methods.

Applications of Auction and Mechanism Design in Edge Computing: A Survey

Edge computing as a promising technology provides lower latency, more efficient transmission, and faster speed of data processing since the edge servers are closer to the user devices. Each edge server with limited resources can offload latency-sensitive and computation-intensive tasks from nearby user devices. However, edge computing faces challenges such as resource allocation, energy consumption, security and privacy issues, etc. Auction mechanisms can well characterize bidirectional interactions between edge servers and user devices under the above constraints in edge computing. As demonstrated by the existing works, auction and mechanism design approaches are outstanding on achieving optimal allocation strategy while guaranteeing mutual satisfaction among edge servers and user devices, especially for scenarios with scarce resources. In this paper, we introduce a comprehensive survey of recent researches that apply auction approaches in edge computing. Firstly, a brief overview of edge computing including three common edge computing paradigms, i.e., cloudlet, fog computing and mobile edge computing, is presented. Then, we introduce fundamentals and backgrounds of auction schemes commonly used in edge computing systems. After then, a comprehensive survey of applications of auction-based approaches applied for edge computing is provided, which is categorized by different auction approaches. Finally, several open challenges and promising research directions are discussed.

COVID19 epidemic outbreak: operating rooms scheduling, specialty teams timetabling and emergency patients' assignment using the robust optimization approach.

The health care system is characterized by limited resources, including the physical facilities as well as skilled human resources. Due to the extensive fixed cost of medical facilities and the high specialization required by the medical staff, the problem of resource scarcity in a health care supply chain is much more acute than in other industries. In the pandemic of the Coronavirus, where medical services are the most important services in communities, and protective and preventive guidelines impose new restrictions on the system, the issue of resource allocation will be more complicated and significantly affect the efficiency of health care systems. In this paper, the problem of activating the operating rooms in hospitals, assigning active operating rooms to the COVID-19 and non-COVID-19 patients, assigning specialty teams to the operating rooms and assigning the elective and emergency patients to the specialty teams, and scheduling their operations is studied by considering the new constraints of protective and preventive guidelines of the Coronavirus. To address these issues, a mixed-integer mathematical programming model is proposed. Moreover, to consider the uncertainty in the surgery duration of elective and emergency patients, the stochastic robust optimization approach is utilized. The proposed model is applied for the planning of operating rooms in the cardiovascular department of a hospital in Iran, and the results highlight the role of proper management in supplying sufficient medical resources effectively to respond to patients and scheduled surgical team to overcome the pressure on hospital resources and medical staff results from pandemic conditions.

QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks

The Long-Term Evolution (LTE) system was a result of the 3rd-Generation Partnership Project (3GPP) to assure Quality-of-Service (QoS) performance pertaining to non-real-time and real-time services. An effective design with regards to resource allocation scheduling involves core challenges to realising a satisfactory service in an LTE system, particularly with the growing demand for network applications. The continuous rise in terms of the number of network users has resulted in impacts on the performance of networks, which also creates resource allocation issues when performing downlink scheduling in an LTE network. This research paper puts forward a review of optimisation pertaining packet scheduling performance through the LTE downlink system by introducing a new downlink-scheduling algorithm for serving video application through LTE culler networks, and also accounts for QoS needs and channel conditions. A comparison of the recommended algorithms’ performances was made with regards to delay, throughput, PLR, and fairness by utilising the LTE-SIM simulator for video flow. On the basis of the outcomes obtained, the algorithms recommended in this research work considerably enhance the efficacy of video streaming compared against familiar LTE algorithms.

Toward Tailored Resource Allocation of Slices in 6G Networks With Softwarization and Virtualization

Compared with 5G networks, 6G networks are guaranteed to provide various tailored end-to-end network services and emerging cloud-edge applications. Network slicing (NS) is regarded as the key enabler of 6G networks. Softwarization and virtualization technologies, such as software-defined networking and network function virtualization, are accelerating the way toward NS of 6G networks. The resource allocation issue in 6G NS is very crucial, worthy more research attention. In this article, we propose one efficient resource allocation algorithm, labeled as <i>TailoredSlice-6G</i>, so as to realize the tailored slices in 6G. When receiving one slice request, our <i>TailoredSlice-6G</i> will identify the slice resource type in the first place. Then, our <i>TailoredSlice-6G</i> will select its most suitable subalgorithm to do the resource allocation and slicing deployment. Each type of slice corresponds to its specific resource allocation subalgorithm, inserted in the <i>TailoredSlice-6G</i> algorithm. In addition, each subalgorithm in <i>TailoredSlice-6G</i> is guaranteed to run within polynomial time. Thus, <i>TailoredSlice-6G</i> having the potential to be promoted to real networking application. To highlight the merits of <i>TailoredSlice-6G</i>, we do the comprehensive simulation. Simulation results vividly reveal that our <i>TailoredSlice-6G</i> outperforms the selected heuristics that are representative in the literature.

Using resource allocation for seamless service provisioning in cloud computing

A Resources allocation is one of the most important things in cloud computing because the environment in which work depends is heterogeneous environment and there are many who request the same service at the same time. Two criteria are used to be considered. First, the time for completing all the required tasks on the available resources and secondly the energy consumed and is extracted from the number of processes that used to complete those tasks. While uploading an application to the Cloud, load balancer allocates the appealed instances for physical computers to balance the computational load of several applications through physical computers. this paper presents some resource allocation problems and issues that can solve with help of load balancing techniques and algorithms. In addition, the performance of the algorithm was measured on seven mathematical functions, and the best results were for the algorithm pyogenes.

Healthcare: A priority-based energy harvesting scheme for managing sensor nodes in WBANs

The eHealth service has been considered a potential resource issue for industry and academia and is remarkably similar to a promising technology for continuous monitoring of biomedical signals in the human body. Indeed, it deployed modern digital technologies to maintain patient health data in digital environments such as the Internet of Things (IoT). In this vein, Wireless Body Area Networks (WBANs) are essential components of eHealth systems for early detection and successful treatment. Because sensor batteries in WBANs are usually operated and inconvenient to recharge, an energy-efficient resource allocation scheme is critical to extending the length of networks while still meeting the stringent quality of service requirements inherent in WBANs. As a result, this paper investigates resource allocation issues for WBAN. Our objective is to maximize energy efficiency by considering the effect of data transmission, relay selection, power consumption, and each sensor's energy constraints. Due to the current problems' sophistication, we present a Q-learning Agent (QLA) system to obtain the optimal allocation approach. A Q-Sensor Network Management Unit (Q-SNMU) is implemented and designed to synchronize all body sensors appropriately. The results show that the proposed scheme works well and that the proposed Q-SNMU approach is very efficient at running.

How to allocate multi-period research resources? Centralized resource allocation for public universities in China using a parallel DEA-based approach

In many real scenarios, all decision-making units (DMUs) fall under the umbrella of a central unit, such as banks, supermarkets and public universities, that has the power to supervise and control them. In such centralized decision-making circumstances, how to allocate fixed resources across DMUs when there is competition for resources has always been an important problem for managers. Data envelopment analysis (DEA) offers a new performance-based solution to resource allocation issues. In this paper, we propose a parallel DEA-based approach to reallocate multi-period resources among all DMUs by treating individual periods (e.g., years) as divisions operating at the parallel level. To balance the goals of the organization and the individual preferences of each DMU in resource allocation, we introduce the concept of cross-efficiency. Hence, we combined the operations of individual periods and the concept of cross-efficiency in one method simultaneously. Such allocations for multi-period resources are more acceptable by both the central manager and the evaluated DMUs. Finally, we assessed the R&D performance of each one of the 61 public universities in China and reallocated government funding resources among them.

Energy and spectral efficient resource allocation in 5G HetNet using optimized deep bi‐BRLSTM model

AbstractThe heterogeneous deployment of ultra‐dense femto cell dispenses an enhanced coverage and capacity for the users in the 5G wireless communication system. Due to the substantial growth in wireless data traffic and the inflation of energy consumption, the design of energy and spectrum efficient wireless network is seems to be an extreme concern. Therefore, there is a need to develop an energy and spectrum efficient resource allocation model. The joint contribution of the maximization of energy efficiency (EE) and spectral efficiency (SE) with its constraints is formulated as a multiobjective optimization problem. The resource allocation (RA) issue in the network is handled by the hybrid deep bidirectional battle royale long short term memory (Deep bi‐BRLSTM) model. The loss function in the network model is taken as a consideration to optimize the SE and EE. Fuzzy based energy efficient adaptive atom search optimizer (FEEAAS) is instigated to optimize the trade‐off of EE and SE consecutively. It is embedded with high convergence rate with maximum efficiency which is suitable for power optimization with maximum resource allocation. The performance of the proposed model is implemented in Matlab Simulink platform. The simulation result reveals that the presented model attains an optimal trade‐off between the EE and SE and also improves the overall throughput and delay when compared with existing solutions.

Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (FL), which enables data owners to conduct model training without having to transmit their raw data to third-party servers. However, the FL network is envisioned to involve thousands of heterogeneous distributed devices. As a result, communication inefficiency remains a key bottleneck. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this article, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange for the data owners' participation, and the data owners are free to choose which cluster to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, each cluster head can choose to serve a model owner, whereas the model owners have to compete amongst each other for the services of the cluster heads. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing properties of the deep learning based auction.

A food-energy-water nexus meta-model for food and energy security

Optimal allocation of the food, energy and water (FEW) resources is of emergent concern owing to depleting supply of the natural resources. Increasing demand for the FEW resources is attributable to growing population, migration, economic development, technological advancements and climate change. The FEW nexus (FEW-N) is an intricate system that requires robust quantitative decision-making tools to investigate the links between the various system components and sustainability. This study proposes a meta-model-based FEW-N system for addressing the issue of natural resource allocation for food and energy security. It incorporated an integrated model consisting of the Techno-Economic and Input/Output models in an Optimisation framework with maximum economic benefit as its objective function. The COIN OR Branch and Cut (CBC) and CPLEX solvers in the Advanced Interactive Multidimensional Modelling System (AIMMs) were used to formulate and solve the optimisation problems. To validate the developed framework, the scenario analysis was performed on three cases in South Africa. First, it was found that using FEW resources for food production in dryland open fields, undercover greenhouses, and irrigated open fields was more profitable than for production of electrical energy from bioenergy, solar/wind-based hybrid renewable energy, and hydropower production systems. Second, the revenue of the sub-sector determined the percentage use of the FEW resources and the percentage contribution of technology options to food and energy security. Third, open fields, greenhouses, and irrigated open fields contributed significantly to food security. The holistic framework developed provided enhanced understanding of the FEW-N system. Resource security has significantly improved due to the ability of various technologies in each subsector to meet the food and energy demands of the specific population. Besides providing scientific support for national decisions regarding food, energy, and water policy, the proposed framework will also contribute to sustainable development at the subnational level.