Integrated Resource Allocation Research Articles

Modernizing cloud computing systems with integrating machine learning for multi-objective optimization in terms of planning and security

Cloud enterprises face challenges in managing large amounts of data and resources due to the fast expansion of the cloud computing atmosphere, serving a wide range of customers, from individuals to large corporations. Poor resource management reduces the efficiency of cloud computing. This research proposes an integrated resource allocation security with effective task planning in cloud computing utilizing a Machine Learning (ML) approach to address these issues. The suggested ML-based Multi-Objective Optimization Technique (ML-MOOT) is outlined below: An enhanced task planning, based on the optimization method, aims to reduce make-span time and increase throughput. An ML-based optimization is developed for optimal resource allocation considering various design limitations such as capacity and resource demand. A lightweight authentication system is suggested for encrypting data to enhance data storage safety. The proposed ML-MOOT approach is tested using a separate simulation setting and compared with state-of-the-art techniques to demonstrate its usefulness. The findings indicate that the ML-MOOT approach outperforms the present regarding resource use, energy utilization, reaction time, and other factors.

Comprehensive multi-benefit planning of sustainable interconnected microgrids

A comprehensive stochastic planning model with multi-objective, integrated sizing and scheduling strategies for distributed energy resources is fabricated and implemented for interconnected grid-tied microgrids in the present work. The optimum capacities of the renewable and dispatchable distributed generators, together with battery energy storage systems in the interconnected microgrids, are ascertained using grey wolf optimizer in the fuzzy domain with the objectives of minimizing the yearly expenses, emissions and energy loss, and maximizing the yearly monetary benefit acquired due to the deferral of network upgrades. The optimal dispatch of multi-energy resources is intended to satisfy price-sensitive, multi-energy demands economically and sustainably under the influence of a price-based demand response strategy. The proposed multi-microgrid planning framework, which is hinged on probabilistic power flow with the probabilistic modelling of renewable and load uncertainties, is targeted to benefit multiple entities, including the utility and microgrid prosumers. The effectiveness of the proposed formulation is demonstrated by the mitigation of the annual expenses, emissions, and energy loss by 51%, 70%, and 56%, respectively, and the deferral of the need for the relevant upgrades by 12 years. Moreover, the performance of the integrated resource allocation and scheduling model is also endorsed using environmental sustainability factors.

Optimization of Resource Allocation and Task Allocation with Project Management Information Systems in Information Technology Companies

This study proposes a novel approach to designing an integrated Resource Allocation and Task Allocation Optimization System (RATAOS) using Enterprise Architecture (EA) to improve project management efficiency. The proposed approach integrates Project Management Information System (PMIS) with an optimization system designed using a random forest model and Natural Language Programming (NLP). The integrated system optimizes resource and task allocation, reducing operational costs by 14% and planning phases by 88.7%. Project completion time increased by 50.80%, demonstrating the effectiveness of the integrated system. The purpose of this study is to find a solution for PMIS to be used as an automatic data-driven Resource and Task Allocation Optimization System. The technique used in this study is service integration between the existing PMIS with the Resource and Task Allocation Optimization System.

The Efficacy of Provider-Based Prenatal Interventions to Reduce Maternal Stress: A Systematic Review.

Exposure to stress during the prenatal period is often associated with adverse maternal and neonatal health outcomes and is increasing in prevalence in the United States. Health care providers play a crucial role in addressing and mitigating this stress, but there is a lack of consensus in effective interventions. This review evaluates the effectiveness of prenatal provider-based interventions that reduce stress for pregnant people, especially those who are disproportionately affected by stress. A search of relevant English-language literature was conducted using PubMed, CINAHL, Web of Science, Embase, and PyscInfo. Inclusion criteria were 1) the target population was pregnant people, 2) the intervention was delivered within the U.S. health care system, and 3) the study intervention had the goal of reducing stress (stress-reducing intervention). A total of 3,562 records were identified in the search and 23 were included in analysis. The four identified categories for provider-led stress-reducing prenatal interventions included in the review are 1) skills-building, 2)mindfulness, 3) behavioral therapy, and 4) group support. Findings suggest an increased overall likelihood of mood and maternal stress improvement among pregnant people who complete provider-based stress-reducing interventions, especially group-based therapies that integrated resource allocation, skills-building, mindfulness, and/or behavioral therapy into an intersectional program. However, the efficacy of each type of intervention varies by category and type of maternal stress targeted. Although few studies demonstrated a significant reduction in stress for pregnant people, this review highlights the critical need for increased research and attention to stress-reducing interventions in the prenatal period, especially as it pertains to minoritized populations.

Integrated Resource Allocation and Task Scheduling for Full-Duplex Mobile Edge Computing

Mobile edge computing (MEC) which enables communication, computation and storage capabilities at the network edge, is an emerging paradigm for 5G and beyond 5G networks. Resource-limited mobile devices can offload their tasks to the MEC server to reduce computation latency and save energy consumption. However, limited wireless spectrum resources make it hard for MEC to guarantee low latency when a large number of users offload the computation tasks or download the computed results. Full-duplex (FD) communication that allows simultaneous transmission and reception of signals over the same frequency band is a promising solution to the shortage of spectrum resources. In this paper, we study a multi-user FD-MEC system involving all the phases of uploading, computing, and downloading. With the help of an FD-capable base station (BS), two half-duplex (HD) users can form an FD pair and share the same time-frequency resource for task uploading and result downloading. We formulate a latency minimization problem considering power control, time scheduling, offloading decision, and user pairing strategy, which is a mixed-integer non-linear programming (MINLP) problem. The problem can be decomposed into two subproblems. The first subproblem is delay minimization for each possible FD pair, and the second one is to decide the offloading and user pairing strategy, and the user scheduling order. We analyze the problem in two cases: (1) the computing delay at the edge server is negligible and (2) the computing delay at the edge server is non-negligible. For both cases, the first subproblem is solved using a bisection algorithm. Then, we solve the second subproblem in the two cases by a relaxing-based algorithm and a concave-convex procedure (CCCP)-based algorithm, respectively. Finally, numerical results demonstrate that with our proposed strategies, the overall latency can be significantly reduced by integrating FD communication with the MEC system.

Algorithm for Energy Resource Allocation and Sensor-Based Clustering in M2M Communication Systems

Recent years have seen a surge in curiosity in machine-to-machine (M2M) collaborations between academics and industry. Machine-to-machine communication devices (MTCDs) are able to communicate automatically and with minimum human intervention in an M2M communications infrastructure. While MTCDs are anticipated to deliver a range of services, resource allocation and clustering approaches in M2M transceivers face issues and limits due to the diverse quality of service (QoS) needs in various network conditions. A major issue in M2M communication systems is how to distribute and cluster resources. This article presents a clustering technique and collaborative resource allocation for MTCD resource management. The clustering and integrated resource allocation challenge is characteristic as a maximization of energy efficiency problem. As a consequence of the original optimization model’s inability to tackle nonlinear fractional utilizations, we separate the issue into two subproblems: power redistribution and cluster. We begin by obtaining the optimal power distribution plan through an iterative energy efficiency maximization algorithm and then offer a modified K -means technique for clustering. The effectiveness of the proposed approach is shown by the numerical solution.

An integrated resource allocation and tolerance allocation optimization: A statistical-based dimensional tolerancing

Today's industrial world is facing rising demand for highly reliable and safe products. Complex industries, such as automobiles, medical, and aircraft, require a well-designed engineering plan which has a comprehensive understanding of the various certainties and uncertainties that occur in reality. Consequently, the need for reliable and precise parts has impacted the tolerancing activity. Key functions of complex products can often be realized by high precision part use. Thus, producers are confronted with high-quality requirements, cost pressure, and a rising number of demands. The introduction of new technologies and the need to meet the requirements have broadened the scope of tolerancing. In this paper, a statistical tolerance allocation model is developed to study the economic impact of allocated tolerances on an assembled product. The problem is aimed at optimizing the allocated tolerances to each part of the product while minimizing manufacturing costs. A modular cost model is proposed to determine the manufacturing costs related to each activity and part. The manufacturing costs include processing cost, inspection cost, scrap cost, assembly cost, and warranty cost. Furthermore, a genetic algorithm is adapted to study the applicability of the model developed on an exemplary assembled product.

Impact of Informal Care on Health Care Utilization Among Older People in China

Population aging has become a challenge to long-term care and health care for the society. Using China as a case study, this paper assesses allocative efficiency of resources in informal care and health care, to explore the effectiveness of the policy, i.e., encouraging informal care as a more cost-effective way to reduce public health care spending. Drawing data from the 2011, 2014, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey, this study examines the impact of informal care on utilization of health care as well as amount of health care expenditures among older people with functional limitations in China. Using random effects model with instrumental variable approach, our findings suggest that informal care significantly reduces the utilization of health care, primarily by reducing the utilization of outpatient care. However, informal care significantly increases the amount of inpatient care expenditures for inpatient care users. We do not observe significant association between informal care and amount of outpatient care expenditures for outpatient care users. This study highlights a pressing need for the Chinese government to support informal caregivers by taking economic values of informal caregiving into consideration, and to improve efficiency in inpatient care by a more integrated resource allocation mechanism

Resource allocation to a structural biomaterial: Induced production of byssal threads decreases growth of a marine mussel

Abstract The biomechanics of specialized mechanical structures produced by organisms provides crucial fitness advantages. The energetic cost associated with producing these structural materials and the resulting energetic trade‐off with growth, however, is rarely quantified. We integrate resource allocation to structural material production with an energetic framework by combining an experimental manipulation with an energetic model. Mytilid bivalves produce byssus, a network of collagen‐like threads that tethers individuals to hard substrate. We hypothesized that a manipulation that induces the production of more byssal threads would result in increased energetic cost and decreased growth of the species Mytilus trossulus. In month‐long field experiments in spring and autumn, we severed byssal threads across a range of frequencies (never, weekly, daily), and measured shell and tissue growth. We then quantified the costs associated with the production of byssal threads using a Scope for Growth model. We found that byssal thread removal increased byssal thread production and decreased growth. The cost calculated per byssal thread was similar in the spring and autumn (~1 J/thread), but energy budget calculations differed by season, and depended on thread quantity and seasonal differences in assumptions of metabolic costs. This work demonstrates that the cost of producing a structural material has a substantial effect on mussel energetic state. The energetic cost of producing byssal threads was 2%–8% percent of the energy budget in control groups that had low byssal thread production, and increased six to 11‐fold (up to 47%) in mussels induced to produce threads daily. We propose that characterizing the trade‐off between the cost of biomaterial production and growth has implications for understanding the role of trade‐offs in adaptive evolution, and improved natural resource management and conservation practices. A free Plain Language Summary can be found within the Supporting Information of this article.

Cardiothoracic surgery during the Covid-19 pandemic: Perioperative care, safety, and surgical results.

BackgroundCovid‐19 was declared a pandemic by the World Health Organization (WHO) on 11 March 2020. Responses to this crisis integrated resource allocation for the increased amount of infected patients while maintaining an adequate response to other severe and life‐threatening diseases. Though cardiothoracic patients are at high risk for Covid‐19 severe illness, postponing surgeries would translate to increased mortality and morbidity. We reviewed our practice during the initial time of the pandemic, with emphasis on safety protocols.MethodsFrom 11 March to 15 May 2020, 148 patients underwent surgery at the Department of Cardiothoracic Surgery of Centro Hospitalar Universitário de São João. The clinical characteristics of the patients were retrospectively registered, along with novel containment and infection prevention measures targeting the new coronavirus.ResultsThe majority of adult cardiac patients were operated on an emergency basis. Hospital mortality was 1.9% (n = 2 patients). Most adult thoracic patients were admitted from home, with a diagnosis of neoplastic disease in 60% of patients. Hospital mortality was 3.3% (1 patient). Fifteen children underwent cardiothoracic surgery. There was no mortality. The infection prevention procedures applied, totally excluded the transmission of Covid‐19 in the department.ConclusionWhile guaranteeing a prompt response to emergent, urgent, and high priority cases, novel safety measures in individual protection, patients circuits, and preoperative diagnoses of symptomatic and asymptomatic infection were adopted. The surgical results corroborate that it was safe to undergo cardiothoracic surgery during the initial time of the Covid‐19 pandemic. The new policies will be maintained while the virus stays in the community.

Millimeter Wave Integrated Access and Backhaul in 5G: Performance Analysis and Design Insights

With the emergence of integrated access and backhaul (IAB) in the fifth generation (5G) of cellular networks, backhaul is no longer just a passive capacity constraint in cellular network design. In fact, this tight integration of access and backhaul is one of the key ways in which 5G millimeter wave (mm-wave) heterogeneous cellular networks (HetNets) differ from traditional settings where the backhaul network was designed independently from the radio access network (RAN). With the goal of elucidating key design trends for this new paradigm, we develop a stochastic geometry-based analytical framework for a millimeter wave (mm-wave) two-tier HetNet with IAB where only the macro BSs (MBSs) have fiber access to the core network and the small cell BSs (SBSs) are wirelessly backhauled by the MBSs over mm-wave links. For this network, we derive the downlink rate coverage probability for two types of resource allocations at the MBS: 1) integrated resource allocation (IRA): where the total bandwidth (BW) is dynamically split between access and backhaul, and 2) orthogonal resource allocation (ORA): where a static partition is defined for the access and backhaul communications. Our analysis concretely demonstrates that offloading users from the MBSs to SBSs may not provide similar rate improvements in an IAB setting as it would in a HetNet with fiber-backhauled SBS. Our analysis also shows that it is not possible to improve the user rate in an IAB setting by simply densifying the SBSs due to the bottleneck on the rate of wireless backhaul links between MBS and SBS.

D2D Communication Mode Selection and Resource Optimization Algorithm With Optimal Throughput in 5G Network

Aiming at the problem of device-to-device (D2D) communication mode selection and resource optimization under the joint resource allocation mode in the 5G communication network, a probabilistic integrated resource allocation strategy and a quasi-convex optimization algorithm based on channel probability statistical characteristics are proposed. This strategy and algorithm guarantee D2D. Communication maximizes total system throughput while maximizing access. The analysis results show that this algorithm can significantly optimize the total throughput of the system and reduce the communication interference between the users, which proves the rationality and efficiency of the communication model. The research results obtained in Muwen can provide a theoretical basis for analyzing more complex D2D communication systems and provide a numerical basis for designing heuristic algorithms.

A review of key strategies in realizing power system resilience

The world is witnessing increasing frequency of extreme events. The power system is the backbone critical infrastructure of our economy and is under treat of such events. The resilient power system is intended to cope with low probability, high risk extreme events including extreme natural disasters and man-made attacks. Realizing resilience in the power system has been an unprecedented mission. Equipped with today’s smart grid technologies, power system can be rendered more resilient by the strategies taken before, during and after a disruptive event erupts. Based on a thorough review of existing works, we present the most-investigated problems and solving measures according to their application stage. In the preparation stage, innovative planning frameworks considering disaster scenarios are discussed; after the event, the system can alter the topology and integrate resource allocation to alleviate load shedding. The characteristics of different disasters are investigated to facilitate enhancing resilience. The review provides a summary of resilience strategies in the power system and can shed light to future research and application.

A simulation optimization approach for integrated resource allocation in an emergency department, pharmacy, and lab

In this paper, we presented a simulation optimization approach toward optimizing resource allocation in Firoozgar Emergency Department, Tehran, Iran. Most of the departments have interactions in healthcare systems. Therefore, modeling one isolated department such as the emergency department is not acceptable. Since laboratories, radiology departments, and pharmacies have high interactions with emergency departments, clinical pathway was used as a key for the integrated simulation of the emergency department in this paper. Simulation model was validated using real data. In emergency departments, there are various patients with different priorities. Thus, there should be different response variables, i.e. a multi-response optimization problem. Here, a new approach using the design of experiments, data envelopment analysis, multi-layer perceptron artificial neural network, and radial basis function was presented for optimizing the resource allocation problem. Since resources in healthcare are costly and limited, budget and resource constraints were applied in the model. The framework presented in this study is applicable to any healthcare department.

Integration of resource allocation and task assignment for optimizing the cost and maximum throughput of business processes

To improve efficiency and keep an edge in today’s increasingly competitive global business environments, this study aims to integrate resource allocation and task assignment for optimizing the cost and maximum throughput of business processes with many-to-many relationships between resources and activities using numerical analysis approaches and improved genetic algorithm. Firstly, a formal business process model for analyzing cost and maximum throughput is presented based on set theory. Secondly, the mathematic models of integrating resources allocation and task assignment for optimizing the cost and maximum throughput of business process are proposed respectively and solved by the improved genetic algorithm. Finally, the effectiveness and viability of the proposed methods are verified in numerical and practical cases respectively.

An integrated resource allocation and distribution model for pre-disaster planning

In this paper, a three-echelon network model is proposed for integrated emergency preparedness and response planning for the distribution of emergency supplies. The model minimizes the social cost to identify a set of potential supply points (SPs) at the highest echelon, where supply items are consolidated and sent to the prepositioning facilities. The sum of logistics and deprivation costs incurred by the population due to the lack of access to goods or services, is considered as the social cost in this model. The deprivation cost is assumed to increase exponentially with the deprivation time. The model also considers pre-disaster and post-disaster purchasing decisions at the SPs, and allows direct shipments from SPs and prepositioned facilities to the demand points. Numerical analysis shows that multiple supply sources can ensure efficient distribution of the supplies and reduce the deprivation costs. The results also indicate that partial prepositioning and post-disaster purchasing can reduce the shortage in emergency supplies.

Simultaneous heat integrated resource allocation network targeting for total annual cost considering non-isothermal mixing

This paper introduces a new approach for targeting minimum total annual operating cost of heat integrated resource allocation network. This cost includes fresh resource and utility costs. Primary analyses are led to specify an adequate search space for the variables. A mixed integer linear programming (MILP) model is presented to design the heat integrated resource allocation network. The objective function includes fresh resource, waste discharge and utilities. Heat integration is realized with a modified transshipment model, where a predefined temperature scale is built in order to account for non-isothermal mixing.

Integrated Resource Allocation and Binding in Clock Mesh Synthesis

The clock distribution network in a synchronous digital circuit delivers a clock signal to every storage element, that is, clock sink in the circuit. However, since the continued technology scaling increases PVT (process-voltage-temperature) variation, the increase of clock-skew variation is highly likely to cause performance degradation or system failure at runtime. Recently, to mitigate the clock-skew variation, many researchers have taken a profound interest in the clock mesh network. However, though the structure of the clock mesh network is excellent in tolerating timing variations, it demands significantly high power consumption due to the use of excessive mesh wire and buffer resources. Thus, optimizing the resources required in the mesh clock synthesis while maintaining the variation tolerance is crucially important. The three major tasks that greatly affect the cost of the resulting clock mesh are: (1) mesh segment allocation , (2) mesh buffer allocation and sizing , and (3) clock sink binding to mesh segments . Previous clock mesh optimization approaches solve the three tasks sequentially, one by one at a time, to manage the runtime complexity of the tasks at the expense of losing the quality of results. However, since the three tasks are tightly interrelated, simultaneously optimizing all three tasks is essential, if the runtime is ever permitted, to synthesize an economical clock mesh network. In this work, we propose an approach that is able to tackle the problem in an integrated fashion by combining the three tasks into an iterative framework of incremental updates and solving them simultaneously to find a globally optimal allocation of mesh resources while taking into account the clock-skew tolerance constraints. The core parts of this work are a precise analysis on the relation among the resource optimization tasks and an establishment of a mechanism for effective and efficient integration of the tasks. In particular, to handle the runtime problem, we propose a set of speedup techniques, that is, modeling the RC circuit for eliminating redundant matrix multiplications, exploiting a sliding-window scheme, and quickly estimating the buffer sizing effect, which are fitted into our context of fast clock-skew estimation in mesh resource optimization as well as an invention of early decision policies. Through extensive experiments with benchmark circuits, it is shown that our proposed clock mesh synthesizer is able to reduce the worst-case clock skew, total mesh wirelength, total size of mesh driving buffers, and total clock mesh power consumption including short-circuit power by 25.0%, 13.2%, 10.9%, and 11.0% on average compared to that produced by the best-known clock mesh synthesis method (MeshWorks), respectively.

The angiosperm phloem sieve tube system: a role in mediating traits important to modern agriculture

The plant vascular system serves a vital function by distributing water, nutrients and hormones essential for growth and development to the various organs of the plant. In this review, attention is focused on the role played by the phloem as the conduit for delivery of both photosynthate and information macromolecules, especially from the context of its mediation in traits that are important to modern agriculture. Resource allocation of sugars and amino acids, by the phloem, to specific sink tissues is of importance to crop yield and global food security. Current findings are discussed in the context of a hierarchical control network that operates to integrate resource allocation to competing sinks. The role of plasmodesmata that connect companion cells to neighbouring sieve elements and phloem parenchyma cells is evaluated in terms of their function as valves, connecting the sieve tube pressure manifold system to the various plant tissues. Recent studies have also revealed that plasmodesmata and the phloem sieve tube system function cooperatively to mediate the long-distance delivery of proteins and a diverse array of RNA species. Delivery of these information macromolecules is discussed in terms of their roles in control over the vegetative-to-floral transition, tuberization in potato, stress-related signalling involving miRNAs, and genetic reprogramming through the delivery of 24-nucleotide small RNAs that function in transcriptional gene silencing in recipient sink organs. Finally, we discuss important future research areas that could contribute to developing agricultural crops with engineered performance characteristics for enhance yield potential.

Impact of Transponder Architecture on the Scalability of Optical Nodes in Elastic Optical Networks

The elastic optical network has proven to be a promising network architecture for handling the rapid growth in IP traffic in the optical layer in a spectrum-efficient manner. This study reveals the optical node requirements in elastic optical networks by comparing multiple network architectures using an integrated resource allocation scheme that considers both network and node parameters. Evaluations show that the multi-flow (MF) transponder-based elastic optical network architecture is superior to two other architectures in terms of scalability of optical nodes and network costs.