Resource Allocation Methodology Research Articles

Analytical Hierarchy Process for Construction Safety Management and Resource Allocation

The construction industry plays a crucial role in contributing to the economy and developing sustainable infrastructures. However, it is known as one of the most dangerous industrial domains. Over the years, special attention has been paid to developing models for managing and planning construction safety. Many research studies have been carried out to analyze the root causes of fatal accidents in construction sites to develop models for preventing them and mitigating their consequences. Root cause identification and analysis are essential for effective risk mitigation. However, implementing mitigation activities is usually limited to the project’s safety budget. The construction sector suffers from a lack of allocation of appropriate safety resources triggered by a dynamic and complex project environment. This study aims to address the gap in safety resource allocation through a comprehensive root cause analysis of construction work accidents. In this paper, we present a comprehensive review of work accident-related research, categorized according to the 5M model into five root factors: medium, mission, man, management, and machinery. A novel methodology for construction safety resource allocation is proposed to mitigate risks analyzed by the 5M model with the aid of advanced technological solutions. Safety resource allocation alternatives are formulated, and their priorities are established based on an analysis of structured criteria that integrate both risk and cost considerations. The Analytical Hierarchy Process (AHP) is employed to select the optimal alternative for safety resource allocation, with the objective of effective risk mitigation. The proposed model underwent validation through two different case studies. The findings indicate that risk aversion is a critical factor in the optimal allocation of safety resources. Furthermore, the results suggest that regulatory measures should prioritize the stimulation of risk motivation in the safety decision-making processes of construction firms.

Distributed resource optimisation using the Q-learning algorithm, in device-to-device communication: A reinforcement learning paradigm

In the context of wireless systems going forward, particularly in the Beyond 5G (B5G) era, where high data rates and low latency are critical, D2D communication is a pivotal technology that has many advantages. In D2D communication, the allocation of resources plays a critical role in achieving higher throughput while ensuring interference management, improved spectrum and energy efficiency, and system fairness. Conventional resource allocation methodologies encounter challenges in dynamically changing and diverse communication environments. To deal with the dynamic and unpredictable character of channel characteristics, we utilize a distributed iterative resource allocation technique based on the reinforcement learning (RL) approach that empowers the system to learn and adapt to the wireless environment autonomously. In this paper, we formulate a distributed Q learning-based RL method as its real-time learning capabilities, reduced communication overhead, and exploration efficiency make it well-suited for adapting to dynamic D2D environments compared to other RL methods. By applying the Q-learning method, the D2D devices act as learning agents striving to maximize the cumulative rewards. Through interactions with the environment and continuous learning from feedback, these agents adapt to real-time resource allocation decisions over time. Comparing our proposed Q-learning method to the state-of-the-art RL techniques, simulation results show improvements in energy and spectrum efficiency, latency, an increase in Jain's fairness index, and improvements in overall system throughput of about 6 %–8 %. The scalability is found to be 1.69 interpreting that Q-learning exhibits a good scalability as the throughput does not abruptly decrease for an increasing number of devices.

Innovative Costing Methodology for Strategic Financial Planning and Resource Allocation: Navigating Budgetary Constraints to Drive Sustainable Growth and Business Leadership

Innovative Costing Methodology for Strategic Financial Planning and Resource Allocation: Navigating Budgetary Constraints to Drive Sustainable Growth and Business Leadership

Enhanced Feature-driven Multi-objective Learning for Optimal Cloud Resource Allocation

In cloud networks, especially those with distributed computing setups and data centers, one of the biggest obstacles is allocating resources. This is the key area, and this must be balanced between optimizing system performance on one side and affordability, stability (reliance) of operation, and energy efficiency. The importance of improving resource allocation methodologies in these complex cloud computing systems is recognized, and therefore this paper comes with an appropriate title--“Enhanced Feature-Driven Multi-Objective Learning for Optimal Cloud Resource Allocation” (OCRA), which integrates together both the latest machine learning techniques as well as traditional concepts from research into cloud computing. OCRA capably analyzes historical files on CPU, memory, disk and network usage. In addition to neatly assimilating large data sets such as that was the compliance rate with past SLAs or workload frequencies over certain time periods and resource allocations; even their patterns of service requests are an important piece of information for many busy people’s lives today the adaptive mechanism is one of the defining traits of the model. It can accurately anticipate changes in resource demand and immediately adjust supply, fully able to respond rapidly when fluctuations arise suddenly or unexpectedly. Multi-Objective Random Forests are at the very core of OCRA. Each tree for decision making is specially designed to meet a particular performance objective in mind. Combining these trees into a Random Forest ensemble increases not only the model's predictive accuracy but also its stability. Pareto optimization is wisely used to maintain a balance among performance indicators, without an excessive focus on one effect alone. OCRA is proven empirically through experimental studies where key performance indicators such as Resource Utilization Rate and Quality of Service (QoS) Adherence Rate are taken into account. OCRA is both energy-efficient, an important attribute in today's environmentally conscious world, and does not sacrifice performance. As far as speed, flexibility and overall efficiency are concerned, OCRA has always been superior to the other cloud resources allocation programs of its own day. While it's still not quite ready for users who don't have a firm background in computer science or programming skills (ocra is plotted on 0-x), with sufficient memory and dominant minutes turn into mechanical equipment without configuration services.

CG‐FlexBeamOpt: Advanced solution methodology for high throughput GEO satellite beam laydown and resource allocation

SummaryModern satellite communication systems are designed to serve dispersed users with changing operational requirements. Allocating resources to meet these requirements becomes highly complex when the size, location, and allocated frequency of satellite beams are flexible. We developed solution techniques that generate feasible beam patterns and frequency allocations to maximize the total demand met across all users. In our approach, an integer program selects an optimal set of beams from a heuristically generated pool of feasible candidate beams. The challenge is in how to efficiently build a pool of good quality candidate beams from exponentially many possible solutions. An innovative column generation‐style heuristic to generate mathematically justifiable beams is presented to address this challenge. We also derived two other heuristic candidate beam generation algorithms to compare and contrast performance and robustness characteristics of different algorithmic choices. We tested the performance of our three new approaches on 12 operational instances that vary in user distribution, user numbers, and demand distribution. While the methods performed differently under varying operational scenarios, the column generation‐based methods provided the best trade‐off between computation time and solution quality in most cases. We further tested our two best performing algorithms for scalability. Our column generation‐based methods were able to provide good quality solutions with up to 400 beams and 5,000 users. Our work provides valuable insights for real‐life implementation: an end‐user of our system can select the solution approach based on their business need, computational (time) budget, and the desired solution quality.

Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods

This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.

Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods

This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.

Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks

In high-speed mobile scenarios characteristic of the Fifth Generation Mobile Networks (5G) environment, the user video experience may be compromised due to concurrent access by numerous users and frequent base station transitions. Addressing this issue, this study introduces a cross-layer resource allocation model that integrates environmental awareness and is tailored for the exigencies of high-speed mobile networks. The paper delves into the challenges engendered by rapid mobility and extensive user access within the 5G environment and critiques the constraints of prevalent resource allocation methodologies. The model delineated herein is conceptualized as an optimization challenge and characterized as a nonlinear, NP-hard problem. In response to this challenge, this study advocates a novel streaming media transmission algorithm underpinned by edge computing, in tandem with an environment-aware wireless resource allocation algorithm. The article articulates the foundational principles and operational modalities of these algorithms, underscoring the significance of environmental cognizance in resource distribution and the efficacy of edge computing in increasing video transmission efficiency. Empirical validation, achieved through simulation experiments, corroborates the efficacy of the proposed approach. Comparative analysis reveals that, relative to conventional methodologies, the proposed framework significantly improves video transmission quality and user experience, particularly in contexts characterized by frequent network fluctuations and high user densities. This research contributes novel insights and pragmatic solutions to optimize video transmission in existing 5G and prospective network paradigms.

Analysis of Some Communication-Related Queuing System Modeling and Performance

The paper presents research into the analysis of queuing systems in the context of communication, with the goal of creating precise models and evaluating their efficacy. The importance of queueing systems cannot be overstated in fields as disparate as telecommunications, computer networks, and the service sector. Analyzing how these systems function allows us to better allocate resources, boost performance, and provide a better experience for end users. The main goals are to create mathematical models that precisely capture the queuing behavior and to assess the effectiveness of these models in terms of metrics like queue length, waiting time, and system utilization. To do so, it is necessary to perform an extensive analysis of the relevant literature and theoretical frameworks. The properties of communication-related systems are examined, and many queuing models, including the M/M/1, M/M/c, and M/G/1, are modified accordingly. In addition, simulation methods are used to verify the accuracy of the suggested models and assess how they would fare in practical applications. The research yields useful information on the dynamics of queuing systems used in the field of communication. They show how changes to the arrival rate, service rate, and number of servers affect the system's most important performance indicators. Different scheduling policies, routing tactics, and resource allocation methodologies are also investigated to determine their impact on system performance.

Resource allocation optimization for mitigating multi-jammer in underwater sensor network

<p class="16">Wireless underwater sensor networks (UWSNs) are used for coastal area monitoring and military monitoring applications, such as tsunami prevention and target tracking. In UWSNs, jamming is considered to be a serious problem where the intruder affects the lifetime of sensor motes and impacts the performance of the packet transmission. This paper considers that the jammer device is capable of reducing battery life and preventing the trustworthy UWSN mote from communication. Considering the presence of multiple jammers, the existing resource utilization model is not effective. This work presents an efficient resource allocation design to mitigate multiple jammers in UWSNs to overcome research problems. The resource allocation (ERA) model adopts a cross-layer design and can interact in a cooperative manner using direct and hop-based communication to maximize the quality of resource use. Compared to existing resource allocation methodologies, considering the presence of multiple jammer motes, the ERA achieves a much better detection rate, resource utilization, packet drop, and energy efficiency performance.</p>

A GERT Network Model for input-output optimization of general aviation industry chain based on value flow

In view of resources, demand-supply mismatch in the process of building a general aviation industry chain, considering input-output optimization, this paper designs a Graphical Evaluation and Review Technique (GERT) model based on value flow maximization under limited resource constraints by defining the general aviation industry chain GERT network basic unit with such resources flow as logistics, information flow, and cash flow. Through the value transfer GERT network model, this paper, based on the input and output statistics, studies and analyzes how to rationally allocate resources to maximize the value flow of logistics, information flow, and cash flow in the design process of general aviation industry chain under the constraint of limited resources. The result shows that the general aviation industry chain's input-output optimization GERT network model is based on value flow by contrasting the chain's value appreciation under the scenario of whether there is optimal resource allocation when limited resources are constrained. This study established a new research methodology for resource allocation by demonstrating that the optimum resource allocation exists and can be achieved by coordinating logistics, information flow, and capital flow in the general aviation sector's value generation and resource flow.

Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system

Recently, many concepts in technology has been changed. According to the digital transformation trends, Internet of Things (IoT) represents an interested research issue. As the IoT grows, the data and the processes will need more space. The data in cases like healthcare, smart cities, autonomous vehicles, smart agriculture, etc. needs to be analyzed and processed in real-time. Cisco refers to the dependence of edge and cloud as “The Fog”. The data can be analyzed at the fog layer to maximize data utilization. This paper presents a new Effective Prediction and Resource Allocation Methodology (EPRAM) for Fog environment, which is suitable for Healthcare applications. Resource Allocation (RA) represents a hard mission as it involves a set of various resources and fog nodes to achieve the required computations for IoT systems. EPRAM tries to achieve effective resource management in Fog environment via real-time resource allocating as well as prediction algorithm. EPRAM is composed of three main modules, namely: (i) Data Preprocessing Module (DPM), (ii) Resource Allocation Module (RAM) and (ii) Effective Prediction Module (EPM). The EPM uses the PNN to predict a target field, using one or more predictors. In order to detect the probability of the heart attack, PNN is trained using the training dataset. Then PNN will be tested using the user’s sensing data coming from the IoT layer to predict the probability of heart attack and then take the most appropriate action accordingly. The main goal of the system is to achieve a low latency while improving the Quality of Service (QoS) metrics such as (the allocation cost, the response time, bandwidth efficiency and energy consumption). Unlike other RA techniques, EPRAM employs deep Reinforcement Learning (RL) algorithm in a new manner. It also uses the PNN for the prediction algorithm. It has achieved such acceptable performance due to using deep RL and PNN. Deep RL has shown impressive promises in resource allocation. PNN generates accurate predicted target and is much faster than multilayer perceptron networks. Comparing the EPRAM with the state-of-the-art algorithms, EPRAM achieved the minimum Makespan as compared to previous LB algorithms, while maximizing the Average Resource Utilization (ARU) and the Load Balancing Level (LBL). Accordingly, EPRAM is a suitable algorithm in the case of real-time systems in FC which leads to load balancing. ERAM is effective in monitoring and predicting the status of the patient accurately and quickly.

A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges

The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.

Cloud based Resource Scheduling Methodology for Data-Intensive Smart Cities and Industrial Applications

For the data-intensive applications, resource planning and scheduling has become an important part for smart cities. The cloud computing techniques are being used for planning and scheduling of resources in data-intensive applications. The regular methodologies being used are adequately successful for giving the asset allotment yet they do not provide time effectiveness during task execution. This article presents an effective and time prioritization based smart resource management platform employing the Cuckoo Search based Optimized Resource Allocation (CSO-RA) methodology. The opensource JStorm platform is utilized for dynamic asset planning while using big data analytics and the outcomes of the experimentation are observed using various assessment parameters. The proposed (CSO-RA) system is compared with the current methodologies like particle swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA) based optimization methodologies and the viability of the proposed framework is established. The percentage of optimality observed for CSO-RA algorithm is 97\% and overall resource deployment rate of 28\% is achieved using CSO-RA method which is comparatively much better than PSO, GA and ACO conventional algorithms. Feasible outcomes are obtained by using the CSO-RA methodology for cloud computing based large scale optimization-based data intensive industrial applications.

Channel-level resource allocation decision in multichannel retailing: A U.S. multichannel company application

The purpose of this paper is to explore the managerial implications of a customer-driven resource allocation methodology in multichannel retailing. A new resource allocation metric is proposed to measure the value of each channel structure in a company with multiple channels. In addition, we have validated the model by implementing it in a multichannel company. The prescriptive model proposed in this research, therefore, contributes to filling the literature gap in four main areas: (1) It incorporates market segment, customer wants, channel operating costs, and interrelationships among channels for effective resource allocation decisions. (2) The model is flexible to include omnichannel options as an alternative (e.g., buy-online-pickup-in-store). (3) It includes operating costs and customer perspectives together to evaluate the channel structures. (4) This study uses a real-world case study approach to implement the proposed model in a U.S. multichannel company producing scientific, biotechnology cameras. The feedback from the managers who participated in the budget allocation process confirmed the utility of this approach.

Efficient Resource Allocation Design for Mitigating Multi-Jammer in Underwater Wireless Sensor Network

Underwater wireless sensor networks (UWSNs) are used for monitoring costal area and military surveillance applications, for example tsunami prevention and target tracking etc. Jamming is considered to be a serious problem in UWSNs where the intruder affects the lifetime of sensor motes and impact the packet transmission performance. This paper consider that the jammer device has capability of reducing battery lifetime and preventing communication of trustworthy UWSN mote. Existing resource utilization model are not efficient considering presence of multiple jammers. For overcoming research issues this work present efficient resource allocation design for mitigating multiple jammers in UWSNs. The ERA model adopts cross layer design and can communicate in cooperative manner using direct and hop based communication that maximize resource utilization quality specifier. Experiment outcome shows the ERA achieves much better detection rate, resource utilization, packet drop, and energy efficiency performance compared with existing resource allocation methodologies considering presence of multiple jammer motes.

Lower Complex Resource Allocation Methodology based on Graph Theory and Index Matrix Rearrangement for OFDMA/MIMO-OFDMA Systems

Resource allocation assigns subcarriers and power at the Base Station (BS) to different users, and is an important aspect in multiuser OFDMA downlink (DL) transmission. Various resource allocation approaches differ in the objectives and constraints in defining the problem and in the algorithms developed to solve the respective problems. Complexity of resource allocation mainly depends on the algorithm developed. This paper intends to fill gaps in literature by introducing graph based lower complex methodology to solve a per-symbol rate maximization problem for three different scenarios in which perfect Channel State Information (CSI) is available. Firstly, a Single Input Single Output (SISO) OFDMA system with Sub-band Carrier Assignment Scheme (SCAS) which supports only a single modulation scheme per user is considered. A SISO-OFDMA system with Generalized Carrier Assignment Scheme (GCAS) which supports multiple modulation schemes within a users’ sub-band is considered next, and finally a Multiple Input Multiple Output (MIMO) OFDMA system is considered. A new parameter is introduced to evaluate fairness of resource allocation, and extensive performance evaluation of developed algorithms based on average SNR values and fairness is carried out. MATLAB simulations show that algorithms perform well and allocate resources fairly even when the channel is extremely frequency selective.

RETRACTED ARTICLE: An advanced artificial intelligence technique for resource allocation by investigating and scheduling parallel-distributed request/response handling

Cloud computing is an emerging technology undergoing various challenges that integrate parallel and distributed computing together. In the multi-tenant environment cloud applications can be utilized as a service. User request are enormous and therefore the attributes to be concerned about are scalability, reliability, and resource availability and server response. Utilization of software, platform and infrastructure increases in this environment paving way for resource consumption. This scenario arises various types of issues through collision, traffic jam, data loss, request dropout and delay in response. The past research provides solutions for aspects like scalability, resource allocation, scheduling, load balancing and optimized request and response handling, resource management through virtualization. The process of virtualization and migration of environment is difficult. The cost for allocating VM for a single user is less. The paper proposed a novel scheduling approach for handling unlimited incoming request with quality of service through energy and throughput. The allocated resource focus on maintaining incoming job request, request for dispatch to the server and an acknowledgement for the receipt of response. The paper provides resource allocation methodology through scheduling approaches called integrating of AI techniques namely Genetic Algorithms (GA) and Artificial Neural Networks (ANN). The property of the request is analyzed and priorityis applied for scheduling the request using resource allocation.

Dynamic Sectorization and parallel processing for device-to-device (D2D) resource allocation in 5G and B5G cellular network

Beyond Fifth Generation (B5G) network aims to provide a very high data rate with minimum latency to an ultra-dense user environment. To achieve this demand, the possible approaches are network-centric and device-centric approach. In a network-centric approach, a new frequency band is introduced, and the existing network infrastructure is modified. The device-centric approach does not require any modification in the existing network infrastructure, and the demand of B5G network is achieved through optimum resource allocation methodology. Device-to-Device (D2D) is an effective device-centric approach that supports the B5G network. In this paper, we propose a dynamic sectorization technique in which eNodeB (eNB) varies the number of sectors dynamically in the network and allocates the Resource Block (RB) to D2D users. Sectoring improves Signal-to-Interference-Noise-Ratio (SINR) and network performance. We derive an expression for the probability of successful transmission and threshold to make a decision on the number of sectors based on available RBs and D2D users in the network. Further, dynamic sectoring helps eNB to perform parallel processing for reducing the denial of the request. Simulation results validate the probability of successful transmission of D2D pairs with available RBs and the effectiveness of parallel processing to reduce the denial of request with improved SINR.

Resource Allocation and Sharing in URLLC for IoT Applications Using Shareability Graphs

The current development trend of wireless communications aims at coping with the very stringent reliability and latency requirements posed by several emerging Internet-of-Things (IoT) application scenarios. Since the problem of realizing ultrareliable low-latency communications (URLLCs) is becoming more and more important, it has attracted the attention of researchers, and new efficient resource allocation algorithms are necessary. In this article, we consider a challenging scenario where the available spectrum might be fragmented across nonadjacent portions of the band, and channels are differently affected by interference coming from surrounding networks. Furthermore, channel state information (CSI) is assumed to be unavailable, thus requiring an allocation of resources-based only on topology information and channel statistics. To address this challenge in a dense smart factory scenario, where devices periodically transmit their data to a common receiver, we present a novel resource allocation methodology based on a graph-theoretical approach originally designed to allocate mobility resources in on-demand, shared transportation. The proposed methodology is compared with two benchmark allocation strategies, showing its ability of increasing spectral efficiency of as much as 50% with respect to the best performing benchmark. Contrary to what happens in many resource allocation settings, this increase in spectrum efficiency does not come at the expense of fairness, which is also increased as compared to benchmark algorithms.