Multiple Resource Allocation Research Articles

Multiple Linear Regression-Based Energy-Aware Resource Allocation in the Fog Computing Environment

Fog computing is a promising computing paradigm for time-sensitive Internet of Things (IoT) applications. It helps to process data close to the users, in order to deliver faster processing outcomes than the Cloud; it also helps to reduce network traffic. The computation environment in the Fog computing is highly dynamic and most of the Fog devices are battery powered hence the chances of application failure is high which leads to delaying the application outcome. On the other hand, if we rerun the application in other devices after the failure it will not comply with time-sensitiveness. To solve this problem, we need to run applications in an energy-efficient manner which is a challenging task due to the dynamic nature of Fog computing environment. It is required to schedule application in such a way that the application should not fail due to the unavailability of energy. In this paper, we propose a multiple linear, regression-based resource allocation mechanism to run applications in an energy-aware manner in the Fog computing environment to minimise failures due to energy constraint. Prior works lack of energy-aware application execution considering dynamism of Fog environment. Hence, we propose A multiple linear regression-based approach which can achieve such objectives. We present a sustainable energy-aware framework and algorithm which execute applications in Fog environment in an energy-aware manner. The trade-off between energy-efficient allocation and application execution time has been investigated and shown to have a minimum negative impact on the system for energy-aware allocation. We compared our proposed method with existing approaches. Our proposed approach minimises the delay and processing by 20%, and 17% compared with the existing one. Furthermore, SLA violation decrease by 57% for the proposed energy-aware allocation.

Resource Allocation for Joint Communication and Localization Systems With MU-MIMO

Joint communication and localization is an attractive technique to simultaneously provide data transmission and localization services, and efficient resource allocation could bring a high data rate and high localization accuracy in limited resources, which is significant for multi-user conditions. In this paper, we concentrate on the joint multiple resource allocation to achieve a trade-off between the performance of the communication and localization process in the millimeter multi-user Multiple Input Multiple Output(MU-MIMO) system. We first develop a robust beam scheduling and user grouping approach using the mutual angle information and channel correlation coefficient to reduce the multi-user interference. Then, we formulate a multi-domain resource allocation problem, where the joint impact of beamforming, bandwidth reservation, and power control is considered. Furthermore, an efficient algorithm is proposed to iteratively perform the sub-carrier and power allocation until satisfying the data rate and localization error-bound constraints after decomposing the large-scale problem into two small-scale sub-problems by the difference of the convex function algorithm. Numerical results show that the proposed algorithm achieves a good trade-off between data rate and localization error and has better performance than other previously proposed algorithms in the literature.

Optimizing the resource cost in multiple resources allocation problem with parametric uncertainties

Process industries often utilize a large number of valuable resources. To minimize operating costs and to promote sustainability, it is essential to prevent the overuse of resources. In conjunction with the prioritized cost concept, Pinch Analysis helps in achieving cost-optimal multi-resource allocation networks. Previous works were restricted to optimizing resource allocation networks with deterministic parameters without considering uncertainties associated with them. Uncertainties in source and resource parameters are incorporated in this paper through chance-constrained programming and solved through Pinch-based and numerical optimization-based methods. The notion of prioritized cost is extended to include uncertainties in various network parameters. Based on specified reliability for the multi-resource allocation network, the proposed approach determines the optimal operating cost of the network, and the results are verified through Monte Carlo simulations. Two examples from the field of water conservation and material conservation are solved to establish the utility of the proposed method. Optimal combinations of resources are pictorially represented at different cost ratios through a novel sensitivity diagram.

Stochastic pre-event preparation for enhancing resilience of distribution systems

Extreme weather events are the common causes for power supply interruptions and power outages in electrical distribution systems. Improving the distribution system and enhancing its resilience is becoming crucial due to the increased frequency of extreme weather events. Preparation and allocation of multiple flexible resources, such as mobile resources, fuel resources, and labor resources before extreme weather events can mitigate the effects of extreme weather events and enhance the resilience of power distribution systems. In this paper, a two-stage stochastic mixed-integer linear programming (SMILP) is proposed to optimize the preparation and resource allocation process for upcoming extreme weather events, which leads to faster and more efficient post-event restoration. The objective of the proposed two-stage SMILP is to maximize the served load and minimize the operating cost of flexible resources. The first stage in the optimization problem selects the amounts and locations of different resources. The second stage considers the operational constraints of the distribution system and repair crew scheduling constraints. The proposed stochastic pre-event preparation model is solved by a scenario decomposition method, Progressive Hedging (PH), to ease the computational complexity introduced by a large number of scenarios. Furthermore, to show the impact of solar photovoltaic (PV) generation on system resilience, three types of PV systems are considered during a power outage and the resilience improvements with different PV penetration levels are compared. Numerical results from simulations on a large-scale (more than 10,000 nodes) distribution feeder have been used to validate the effectiveness and scalability of the proposed method.

Optimization Model for Multiple Backup Resource Allocation With Workload-Dependent Failure Probability

This paper proposes a multiple backup resource allocation model with a workload-dependent failure probability to minimize the maximum expected unavailable time (MEUT) under a protection priority policy. The workload-dependent failure probability is a non-decreasing function which reveals the relationship between the workload and the failure probability. The proposed model adopts hot backup and cold backup strategies to provide protection. For protection of each function with multiple backup resources, it is required to adopt a suitable priority policy to determine the expected unavailable time. We analyze the superiority of the protection priority policy for multiple backup resources in the proposed model; we provide the theorems that clarify the influence of policies on MEUT. We formulate the optimization problem as a mixed integer linear programming (MILP) problem. We provide a lower bound of the optimal objective value in the proposed model. We prove that the decision version of the multiple resource allocation problem in the proposed model is NP-complete. A heuristic algorithm inspired by the water-filling algorithm is developed with providing an upper bound of the expected unavailable time obtained by the algorithm. The numerical results show that the proposed model reduces MEUT compared to baselines. The priority policy adopted in the proposed model suppresses MEUT compared with other priority policies. The developed heuristic algorithm is approximately 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> times faster than the MILP approach with 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> performance penalty on MEUT.

Reproductive allocation in plants: a reappraisal of the currency issue

Reproductive allocation (RA), the proportion of total resources that are invested in reproductive structures, is a key component of plant life histories. A major methodological issue is to identify the allocation currency that better estimates RA: biomass, energy or some mineral nutrient. Despite considerable effort to ascertain whether biomass could serve as an integrated estimate of the allocation of other resources, no clear guideline has emerged from the currency issue. Here, we reappraised this problem at an interspecific scale. We compiled a data base of 38 studies that estimated RA in terms of two or more different resources for 82 plant species. We tested the equivalence of allocation currencies by means of both bi‐ and multivariate correlations among currencies. We also tested for differences in allocation patterns between growth forms. RA was estimated in 18 different currencies, including biomass, energy and 16 mineral nutrients. Nitrogen, P, K, Mg and Ca were the main nutrients used as allocation currency. Growth form accounted for 20–30% of differences in the resource allocation of multiple resources. The six resources most frequently used as estimates RA were roughly equivalent at an interspecific scale. Nevertheless, covariation patterns among currencies suggested that RA in plants should be better considered as a multivariate trait and not estimated using a single currency. In particular, multivariate estimates of RA can contribute to the ongoing comparative studies of life histories and functional traits, and can shed light on resource allocation within flowers.

Lane and speed allocation mechanism for autonomous vehicle agents on a multi-lane highway

Following the arc of automation, it is expected that many driverless vehicles will soon be on the road. The need for autonomous traffic to self-organize necessitates the development of advanced algorithms for unsupervised machine-to-machine negotiation.Mechanisms rooted in game theory detect and resolve contradictions among agents with differing objectives and enable automated collaboration with a socially beneficial outcome. A case study of cyber-enabled vehicles following private objectives and using private ranked preferences to bid for lane and speed changes on a multi-lane highway illustrates interactions that rely on mechanism design. The presented approach constitutes a strategyproof mechanism and thus induces the truthfullness of bids. A lane position and speed setting allocation rule detects and resolves conflict and provides an option for monetization. A NetLogo simulation implements the methodology described. The model allows for additional resources or resource dimensions. Minor domain specific alterations to the distance operator can translate to many application areas and scenarios providing multiple or multi-dimensional resource allocation for a fee.

A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security

The current development of 5G technology is flourishing with widespread deployment across the world at a rapid pace. However, there is still a demand concerning 5G research for service and performance improvement. Research tasks include but are not limited to quality-of-service (QoS), energy efficiency, massive connectivity, reliable communications, and security. Due to the advancement of deep learning, numerous such research has utilized this technique. This article provides a comprehensive review of 5G communications research using deep learning. Specifically, we address the issues of low-density parity-check (LDPC) coding, massive multiple-input multiple-output (MIMO), non-orthogonal multiple access (NOMA), resource allocation, and security.

Joint MU-MIMO Precoding and Resource Allocation for Mobile-Edge Computing

Mobile edge computing is considered as a promising method to release the computation burden of mobile devices (MDs) by transferring the computation tasks to the nearby edge server. In this paper, we address the computation offloading problem by jointly optimizing offloading-decision making, multi-user multiple input and multiple output (MU-MIMO) precoding and computation resource allocation. The optimization problem is formulated as the minimization of the weighted sum of energy consumption and time delay of MDs, which is a mixed-integer non-linear programming problem. Due to the complexity of offloading time delay, we consider two special cases namely, the lower bound and upper bound of offloading time delay for the original problem, and exploit semidefinite relaxation and rounding methods to obtain the offloading decisions. Specially, we adopt the quadratic transform based fractional programming and the weighted minimum mean square error methods to solve the MU-MIMO precoding design problem for the two cases of offloading time delay, respectively. Simulation results confirm the effectiveness of the proposed method, and show that the application of multi-antenna MU-MIMO communication into MEC can sufficently reduce the energy consumption and time delay during computation offloading.

Resource Allocation for Cooperative Transmission in Optical Wireless Cellular Networks With Illumination Requirements

Visible light communication (VLC) often suffers from line-of-sight path blockages and high levels of inter-cell interference. Thus, the analysis and design of cooperation techniques become crucial to address these key impairments. This paper studies the performance of different resource allocation schemes that are suitable for multi-cell cooperative transmission when tri- and tetra-chromatic light-emitting diodes (LEDs) and optical orthogonal frequency-division multiple access are utilized. Firstly, guidelines are derived for maintaining the same spatial distribution of the signal-to-interference-plus-noise ratio (SINR) in every sector of the multi-cell environment in case of stand-alone (non-cooperative) and cooperative transmission. Secondly, the possible resource allocation configurations for both stand-alone and cooperative transmission modes are identified for different LED types and available orthogonal resources (i.e., frequency sub-bands per color and sectors per cell). Finally, the data rate gain of the multiple resource allocation configurations are also analyzed, while verifying the illumination constraints. The obtained results confirm that the proper design of cooperative transmission configurations will be of paramount importance to provide reliable wireless link in ultra-dense VLC deployments.

Evaluation on regional science and technology resources allocation in China based on the zero sum gains data envelopment analysis

Regional science and technology (S&T) resource allocation is an important supporting means of Intelligent Manufacturing in the future. Research on the efficiency of S&T resource allocation is helpful to judge the potential of Intelligent Manufacturing in a specific region. S&T performance evaluation and resource allocation are critical administrative activities for a country or region. Due to resource scarcity, it is necessary to consider the constraint of limited total resources in the process of evaluation and allocation. Thus, the zero sum gains data envelopment analysis models and the associated uniform frontier (UF) method are more suitable for this issue. Comparing with the existing methods, we propose a new algorithm for solving the UF method in this article, which simplifies the procedure of calculation and extends from single to multiple resource allocation. In the empirical application, we evaluate the S&T performances and allocate R&D personnel and intramural expenditure among 31 administrative regions in China. There are 10 high-performance regions. Results can provide specific reference meanings to policy making and analysis.

An Optimal Solution for Eco-reliability Stochastic Modeling of Simultaneous Reconfiguration of Distribution Networks and Allocation of Renewable Energy Resources, Capacitors and D-facts

Objective: A new method based on stochastic modeling of simultaneous optimal reconfiguration of distribution networks and allocation of multiple Distribution STATic COMpensator and renewable energy resources as new developed technologies is suggested in this paper. Background: This problem involves vital uncertain parameters such as: intermittent nature of renewable energy sources, load forecasting and market price errors. Krill Herd Algorithm as a powerful optimization solution is applied to solve suggested problem. Methods: Several effective cost based consequences can be achieved by proposed technique like: DG units installation and operation costs, D-Statcom installation cost, capacitor costs, cost of purchased energy from the transmission network, active and reactive power losses cost, line upgrade cost and distribution system reconfiguration cost. Results: In addition, energy not supplied as a vital reliability index incorporated in the cost function aims to improve network’s reliability efficiently. Conclusion: IEEE 33 bus radial distribution system is used here to check the practicability of the proposed scheme.

Fairness criteria for allocating scarce resources

We develop an optimization model to provide a fair allocation of multiple resources to multiple users. All resources might not be suitable to all users. We develop a notion of fairness, and then provide a general class of functions achieving it. Next, we develop more restricted notions of fairness—special cases of which exist in literature. Finally, we distinguish between scarce and abundant resources, and show that if a resource is abundant, all users seeking it achieve the maximum possible coverage.

Probabilistic allocation and scheduling of multiple resources for emergency operations; a Victorian bushfire case study

The optimization of scheduling and sequencing of multiple resources during disaster management is a challenge due to substantial uncertainty. This paper presents an emergency operation model that aims to facilitate the scheduling and sequencing resources using multiple stochastic scenarios. The proposed model integrates GIS and Mixed Integer Programming (MIP) approaches. The ultimate goal of this paper is to provide a solution framework to identify the most persistent best compromised plan with a specified confidence level. The scheduling of multiple resources under uncertainty (MRSU) model is applied to a case study using data from the Black Saturday bushfires in 7 February 2009 in Victoria, Australia. Several probabilistic scenarios are analyzed to determine the most frequent emergency operation plan. Several probabilistic scenarios are analyzed to determine the most persistent best compromised emergency operation plan. The results indicate that the model can generate plans to schedule multiple resources, thus providing effective service in most emergency scenarios.

A Stackelberg game approach to multiple resources allocation and pricing in mobile edge computing

Mobile edge computing is a new paradigm that can enhance the computation capability of end devices and alleviate communication traffic loads during transmission. Mobile edge computing is highly useful for emerging resource-hungry mobile applications. However, a key challenge for mobile edge computing systems is multiple resources allocation between Mobile Edge Clouds (MECs) and End Users (EUs), especially for multiple heterogeneous MECs and EUs. To address this problem, we propose a Stackelberg game-based framework in which EUs and MECs act as followers and leaders, respectively. The proposed framework aims to compute a Stackelberg equilibrium solution in which each MEC achieves the maximum revenue while each EU obtains utility-maximized resources under budget constraints. We decompose the multiple resources allocation and pricing problem into a set of subproblems in which each subproblem only considers a single resource type. The Stackelberg game framework is constructed for each subproblem wherein each player (i.e., an EU) can selfishly maximize its utility by selecting an appropriate strategy in the strategy space. We prove the existence of the subgame Stackelberg equilibrium and develop algorithms to determine the Stackelberg equilibrium for each resource type, including an optimal demand computation algorithm, to determine the best resource demand strategy for an EU and an iterative algorithm to find an equilibrium price. The equilibrium solutions of all subgames constitute the equilibrium solution of the original problem. We also conduct simulation experiments of our game, such as numerical data for the Stackelberg equilibrium, numerical data for the convergence of the Stackelberg equilibrium, and numerical data as the system size increases. Finally, we demonstrate that an EU with idle resources can play the role of an MEC.

On Virtual Resource Allocation of Heterogeneous Networks in Virtualization Environment: A Service Oriented Perspective

Nowadays, heterogeneous networks (HetNets) aims at achieving a huge amount of data rates and implementing customized service demands. Virtualization technology is the most promising approach for implementing HetNets. In the virtualization environment, physical network resources (e.g. CPU, storage, caching) are virtualized and abstracted in order to be managed and allocated by telecommunication providers flexibly. The virtual resource allocation issue is generally called as virtual network embedding (VNE), attracting extensive attention from academic community. Correspondingly, multiple resource allocation algorithms are proposed. However, existing allocation algorithms adopt ‘one fits all’ mode to serve all requested VN services. All VN services are different and customized, in terms of resource and QoS demands. Adopting the ‘one fits all’ mode to serve all different VNs will eventually lead to inefficient resource utilization and low revenues of telecommunication providers. Hence, it is essential to research the service oriented virtual resource allocation issue in virtualization environment, aiming at efficiently mapping each customized VN service. In this paper, we research the service oriented virtual resource allocation problem. A novel framework, labeled as SerOri , is proposed. The SerOri framework mainly consists of two parts: the VN service classification part SerOri-Class and the VN embedding part SerOri-Embed . By adopting the SerOri , each requested VN service can be implemented efficiently. In order to highlight our SerOri framework, we conduct the experiments. Existing latest or typical embedding algorithms are selected for performance comparison. Experiment results are plotted and discussed.

Proactive VNF Scaling with Heterogeneous Cloud Resources: Fusing Long Short-Term Memory Prediction and Cooperative Allocation

Network function virtualization (NFV) is designed to implement network functions by software that replaces proprietary hardware devices in traditional networks. In response to the growing demand of resource-intensive services, for NFV cloud service providers, software-oriented network functions face a number of challenges, such as dynamic deployment of virtual network functions and efficient allocation of multiple resources. This study aims at the dynamic allocation and adjustment of network multiresources and multitype flows for NFV. First, to seek a proactive approach to provision new instances for overloaded VNFs ahead of time, a model called long short-term memory recurrent neural network (LSTM RNN) is proposed to estimate flows in this paper. Then, based on the estimated flow, a cooperative and complementary resource allocation algorithm is designed to reduce resource fragmentation and improve the utilization. The final results demonstrate the advantage of the LSTM model on predicting the network function flow requirements, and our algorithm achieves good results and performance improvement in dynamically expanding network functions and improving resource utilization.

Code Division Multiple Access Channel Resources Allocation with Applied Token Sub-Queuing for Wireless Multi-Service Packet Switch Traffics

The purpose of this paper is to determine the method of Channel Resources Allocation of wireless Multi-Service Packet Switch CDMA based. As it has been known, CDMA cell has a fluctuated capacity mainly due to its varying co-channel interference. The simulation is performed to shows the characteristic for Multi-Service Packet Switch traffic such combine CBR, VBR, and ABR users is applied on CDMA system. The idea is to deliver the traffics when capacity increase from assigned minimum value, by implementing Proposed Token Sub-Queue and Multi-Service Traffic Channel Resources Algorithm. In the first time, this method is aim for non-real time traffic such ABR traffic, but the simulation shows that it can be applied further to the real-time connection by prioritizing delaying the drop calls of certain agreed QoS values to re-seize into services. The simulation of proposed method shows better CDMA channel or bandwidth utilization, and suppresses drop call probability for real-time traffic.

Sparse Code Multiple Access-Based Edge Computing for IoT Systems

In this paper, a sparse code multiple access (SCMA)-based edge computing scheme is proposed for Internet-of-Things (IoT) systems. The aim of implementing SCMA, which is a nonorthogonal multiple access resource allocation technique, is to improve network connectivity and maximize data rate provision. The proposed edge-IoT system is investigated under different SCMA configurations to explore the various performance aspects such as connectivity, throughput, task completion time, and complexity. First, the problem is formulated as a data rate maximization problem for SCMA-based heterogeneous networks under power constraints. Then, the problem is subdivided into a power allocation problem, which is solved using the water filling approach, and a codebook allocation problem that is solved using a heuristic algorithm. The results show that the SCMA scheme can significantly improve the IoT performance compared to the conventional orthogonal frequency-division multiple access resource allocation scheme in terms of connectivity, throughput, and task completion time provided that SCMA configurations are suitable with IoT processing capabilities to avoid undesired detection latency.

Multi-Phase and Integrated Multi-Objective Cyclic Operating Room Scheduling Based on an Improved NSGA-II Approach

The operating room (OR) is an important department in a hospital, and the scheduling of surgeries in ORs is a challenging combinatorial optimization problem. In this paper, we address the problem of multiple resource allocation of ORs and propose a surgery scheduling scheme for OR units. To solve this problem, a multi-phase and integrated multi-objective linear programming model is proposed. The first phase of the proposed model is a resource allocation model, which mainly focuses on the allocation of ORs for each surgical specialty (SS). Based on the results of the first phase, the second phase is the cyclic Master Surgical Schedule model, which aims to schedule the surgeries in each SS. The proposed models are solved by the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which was improved. Finally, two numerical experiments based on practical data are provided to verify the effectiveness of the proposed models as well as to evaluate the performance of the improved NSGA-II. Our final results illustrate that our proposed model can provide hospital managers with a series of “optimal” solutions to effectively allocate relevant resources and ORs for surgeries, and they show that the improved NSGA-II has high computational efficiency and is more suitable in solving larger-scale problems.