DEA‐based centralized resource allocation with network flows

This paper we consider graphs with reachability restrictions of three types: barrier, valve and magnetic. As a part of the maximum flow problem in networks with considered restrictions, simple examples are given. These examples demonstrate a significant difference between flows in networks with reachability restrictions and flows in the same networks without any reachability restrictions. In each case under consideration there is a decrease of the maximum flow value. Wherein for magnetic reachability it is shown the possibility to control the network flow, whereas for barrier and valve reachability it is shown the reducing of the maximum flow due to the effect of superimposing of flow to itself, which is never observed in the classical theory of flows in networks.

This paper uses the data envelopment analysis method to construct an emerging resource allocation optimization algorithm to deeply analyze and study preschool education resource allocation. This paper combines information related to distributed systems and database application practices, and HBase, a distributed data storage system, is selected as the research object. The relationship between hardware configuration parameters and the throughput and response latency of HBase is modeled using the random forest algorithm, an improved particle swarm algorithm is designed and implemented to optimize the mathematical model of the relationship between resource allocation and capital cost, and the optimization results are verified in a real environment to realize the optimization of HBase resource allocation scheme. In this paper, duplicate solution sets are removed based on the objective function in the resource allocation model. Finally, NSGA-II is compared with SPEA2, MOEA/D, and IBEA in this model for simulation experiments to verify the efficient solution of NSGA-II to the constructed model through subjectivity presentation as well as objective performance analysis and to provide a decision solution with a theoretical support for resource allocation optimization. This study provides countermeasure suggestions for each kindergarten to improve the efficiency of resource management, optimize the scale of operation, and increase technological progress, which is based on the findings of the empirical analysis and field interviews, and thus can effectively help kindergartens to improve the efficiency of resource allocation.

This paper aims to develop a data envelopment analysis (DEA) based model for allocating input resources and deciding output targets in organizations with a centralized decision-making environment, for example, banks, police stations, and supermarket chains. The central decision-maker has an interest in maximizing the total output production and at the same time minimizing the total input consumption. Traditionally, all decision-making units (DMUs) can be easily projected to the efficient frontier, which is a mathematical feasibility; however, it does not guarantee the managerial feasibility during the planning period. In this paper, we will take potential limitations of input-output changes into account by building a difficulty coefficient matrix of modifying their production in the current production possibility set so that the solution guarantees managerial feasibilities. Three objectives, namely, maximizing aggregated outputs, minimizing the consumption of input resources, and minimizing the total difficulty coefficient, are proposed and incorporated into the formation of resource allocation and target setting scheme. Building on this, we combine DEA and multiobjective programming to solve the resource allocation and target setting problem. In the end, we apply our proposed approach to a real-world problem of sixteen chain hotels to illustrate the efficacy and usefulness of the proposed approach.

In next generation wireless systems such as Long Term Evolution (LTE), Radio Resource Management with Inter-cell Interference Coordination (ICIC), is a key issue to be addressed. Although centralized resource allocation (RA) and collaborative processing can optimally perform ICIC, the overall required complexity suggests the consideration of distributed techniques. In this paper we investigate the behavior of distributed RA strategies aimed at maximizing the weighted average sum-rate of a multi-cell clustered system in presence of power constraints by evaluating throughput gap and fairness with respect to their respective centralized implementations. In the distributed RA strategies the inter-cell interference is partially coordinated through the use of power planning schemes (fractional frequency reuse - FFR - is the case considered here), leading to a reduction in both signaling and feedback requirements. The results obtained show that the distributed RA with aggressive frequency reuse is able to approach t performance of the centralized RA when the number of users is large. However, FFR is useful to improve fairness (or cell-edge user performance) at the expense of sum-rate when the scheduling weights are those of proportional fair scheduling (PFS), whereas FFR and in general power planning techniques are able to improve throughput while preserving the fairness when the scheduling weights are set to guarantee equal average rate to users.

Combining ranking scales and selecting variables in the DEA context: The case of industrial branches

In recent decades, there exists a wide increase in traffic of clouds due to the enormous increase of media content. The popularity of cloud is gaining its attention due to its ease of use and flexible model but suffers from poor resource2 management and its minimal extendibility of service portfolio. However, with recent advancement, the services are effectively managed, and its discovery is made further possible. To handle larger amounts of multimedia contents in a standalone cloud, deployment of wide operable systems is yet required that handles the data effectively with increasing demands of the user. A resource allocation framework is designed in this paper that uses a gray wolf optimization (GWO) architecture to effectively learn the operation of resource allocation in an optimal manner. For optimal service provisioning and scalability, the cloud at times communicates with each other based on the resource allocated by the deep neural network, and then the resources are shared. Such a scenario forms the multi-cloud computing, and the resource management using the deep neural network ensures trivial solutions on poor scalability. The deep neural network acts as a model for controlling the routing capabilities based on the input data rate and the storage space available in the multi-clouds. The deep neural network operates in such a way that it reduces the delay in processing and storage of data to cloud that ensures flexible operations across the cloud. The entire operation is divided into two modules: the first module operates on data processing and routing operations, and the second module acts as a control plane using the deep neural network that ensures optimal allocation of resources based on the data obtained and processed in the first module. These two models ensure better delivery of data to the cloud with proper allocation and storage of resources in the multi-cloud environment. The simulation is conducted using Java (netbeans) platform, and it is evaluated further using CloudSim toolkit. The results are experimented on various performance metrics that includes time delay and cost of resource allocation on multi-cloud.

Integrating insights from the literature on the multinational corporation into current perspectives on resource allocation, we argue that the ability of headquarters to create value through resource allocation to subsidiaries within the multinational corporation is contingent on the complementary fit between the resource allocation strategy and the dominant behavior of the receivers of the resources. We expound on a theory and an explanation for the volatility of value creation generated by headquarter resource allocation that includes multiple layers of hierarchy. As a corollary, we extend and contribute to the theorizing on headquarters-subsidiary relations and resource allocation by illustrating different scenarios of the resource allocation process. More specifically, we develop a two-by-two matrix of the resource allocation process that corresponds to different resource allocation strategies of headquarters (winner-picking and cross-subsidization) and subsidiary behavior (collaboration or competition) in multinational corporations. We argue that, depending on which scenario within the matrix is brought to the fore, our understanding of how the resource allocation process plays out between headquarters and subsidiaries will differ and therefore influence value creation within the multinational corporation.

In this paper, we focus on the dynamic traffic assignment (DTA) problem on large-scale expressway networks especially under the condition of traffic events (such as severe weather, large traffic accidents etc.). We formulate the expressway network DTA problem as a nonconvex optimization problem. Considering the difficulty of solving the problem, we put forward an approximate solution algorithm based on the network flow theory with high computational efficiency and strong applicability. Specifically, a machine learning approach and a series of customization strategies of network flow model (including multi-origin and multidestination split, flow discretization and parallel edges addition methods) are proposed to transform the DTA problem into a resoluble network flow problem. After that, in order to improve the operational efficiency of an expressway network after traffic events occurring, the proposed network flow based two-layer DTA model (NTDM) is separated into two parts-traffic flow limitation model based on maximum flow algorithm and traffic assignment model based on minimum cost flow algorithm. Experiments on real expressway network data in some provinces of China show that our method can make a significant 26.03% reduction in saturation rate in a particular scenario. In efficiency test, the NTDM is at least 600 times faster than the traditional analytic method in a network with thousands of nodes.

Applications to the theory of flows in networks

Internet technology is advancing with each passing day, the user's demand is also increasing. Of course the users will more concern to quality of service. The vendors must find out a win win method of resource allocation to meet users and itself. Therefore, the resource allocation of cloud computing has become one of hottest topics. In literatures, some researchers have proposed resource allocation methods which include allocation of virtual machines and service classification, etc. However, these methods are based on subjective observations that lead to the overall cloud architecture becomes imbalance. In order to prevent such situation happened, we use the Data Envelopment Analysis (DEA) to solve the imbalance problem. In this paper, our analysis is start from the user's requests, and use the DEA to evaluate the whole cloud parameters. Then we can find out the resource allocation policy which is the most suitable between the users and vendors.

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.

Wireless virtual reality (VR) communication applications have emerged as a transformative technology, offering innovative solutions in various areas of everyday life. However, the successful deployment of these applications faces challenges in ensuring high quality of experience (QoE), especially in environments with limited network resources. This research paper presents a novel approach to address the challenge of enhancing QoE by incorporating deep reinforcement learning (DRL) techniques in the resource allocation process. The proposed model takes into account the quality of service (QoS) parameters of the 5G new radio (NR) network to optimize its operation, ensuring a seamless and immersive VR experience. Specifically, the resource allocation strategy adopts a policy that maximizes the transmission-related QoE value based on the evolving characteristics of the communication channel and user interactions. To evaluate the effectiveness of the proposed approach, extensive simulations and comparative analyses against traditional resource allocation methods are performed. The results demonstrate significant improvements in the transmission-related QoE values and highlight the superiority of the DRL-based resource allocation approach in the dynamic and unpredictable wireless environments.

Global constraints are used in constraint programming to help users specify patterns that occur frequently in the real world. In addition, global constraints facilitate the use of efficient constraint propagation algorithms for problem solving. Many of the most common global constraints used in constraint programming use filtering algorithms based on network flow theory. We show how we can formulate global constraints such as GCC, Among, and their combinations, in terms of a tractable set-intersection problem called Two Families Of Sets (TFOS). We demonstrate that the TFOS problem allows us to represent tasks that are often difficult to model in terms of a classical constraint satisfaction paradigm. In the final part of the paper we specify some tractable and intractable extensions of the TFOS problem. The contribution of this paper is the characterisation of a general framework that helps us to study the tractability of global constraints that rely on filtering algorithms based on network flow theory.KeywordsSchedule ProblemConstraint ProgrammingNetwork FlowConstraint Satisfaction ProblemGlobal ConstraintThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A characteristic of data envelopment analysis (DEA) is to allow individual decision-making units (DMUs) to select the factor weights that are the most advantageous for them in calculating their efficiency scores. This flexibility in selecting the weights, on the other hand, deters the comparison among DMUs on a common base. In order to rank all the DMUs on the same scale, this paper proposes a compromise solution approach for generating common weights under the DEA framework. The efficiency scores calculated from the standard DEA model are regarded as the ideal solution for the DMUs to achieve. A common set of weights which produces the vector of efficiency scores for the DMUs closest to the ideal solution is sought. Based on the generalized measure of distance, a family of efficiency scores called ‘compromise solutions’ can be derived. The compromise solutions have the properties of unique solution and Pareto optimality not enjoyed by the solutions derived from the existing methods of common weights. An example of forest management illustrates that the compromise solution approach is able to generate a common set of weights, which not only differentiates efficient DMUs but also detects abnormal efficiency scores on a common base.

The effects of habitat heterogeneity on a diffusing population are investigated here. We formulate a reaction-diffusion system of partial differential equations to analyze the effect of resource allocation in an ecosystem with resource having its own dynamics in space and time. We show a priori estimates to prove the existence of state solutions given a control. We formulate an optimal control problem of our ecosystem model such that the abundance of a single species is maximized while minimizing the cost of inflow resource allocation. In addition, we show the existence and uniqueness of the optimal control as well as the optimal control characterization. We also establish the existence of an optimal intermediate diffusion rate. Moreover, we illustrate several numerical simulations with Dirichlet and Neumann boundary conditions with the space domain in 1D and 2D.