Optimal resource allocation in survey designs

Optimal resource allocation across related channels

The QoS based Resource Allocation Model (Q-RAM) proposed by R. Rajkumar et al. (1998) presented an analytical approach for satisfying multiple quality of service dimensions in a resource constrained environment. Using this model, available system resources can be apportioned across multiple applications such that the net utility that accrues to the end users of those applications is maximized. We present several practical solutions to allocation problems that were beyond the limited scope of Q-RAM. We show that the Q-RAM problem of finding the optimal resource allocation to satisfy multiple QoS dimensions is NP hard. We then present a polynomial solution for this resource allocation problem which yields a solution within a provably fixed and short distance from the optimal allocation. Secondly, Q-RAM dealt mainly with the problem of apportioning a single resource to satisfy multiple QoS dimensions. We study the converse problem of apportioning multiple resources to satisfy a single QoS dimension. In practice, this problem becomes complicated, since a single QoS dimension perceived by the user can be satisfied using different combinations of available resources. We show that this problem can be formulated as a mixed integer programming problem that can be solved efficiently to yield an optimal resource allocation. We also present the run times of these optimizations to illustrate how these solutions can be applied in practice. A good understanding of these solutions will yield insights into the general problem of apportioning multiple resources to satisfy simultaneously multiple QoS dimensions of multiple concurrent applications.

Multimedia traffic is predicted to account for 82% of the total data traffic by the year 2020. With the increasing popularity of video streaming applications like YouTube, Netflix, Amazon Prime Video, popular video content is often required to be delivered to a large audience simultaneously. Multicast transmission can be used to cater to such applications efficiently. The common content can be transmitted to the users on the same resources resulting in considerable resource conservation. This paper proposes various schemes for efficient grouping and resource allocation for multicast transmission in LTE. The optimal grouping and resource allocation problems are shown to be NP-hard and so, we propose heuristic algorithms for both these problems. We also formulate a Simulated Annealing based algorithm to approximate the optimal resource allocation for our problem. The LP-relaxation based resource allocation proposed by us results in allocations very close to the estimated optimal.

Optimal container resource allocation in cloud architecture: A new hybrid model

recipitation shortage and the consequent loss of several water resources, as well as the population growth, are the most important problems in arid and semi-arid regions like Iran. The providence of basic tools for optimal water resources management is considered as one of the main solutions to this problem. Since the agricultural sector is the main user of water resources, the present study presented a model based on an artificial neural network method for optimal allocation of water resources in the agricultural sector during the statistical period of 2007-2016. The objective function was determined for each product in the agricultural sector as well as product performance, each product revenues, and cultivated area of the demand function. Maximization of the objective function (to maximize economic profits) and optimal allocation of water resources were; then, conducted by using the neural network. The results of the application of the artificial neural network method to the problem of optimal water allocation showed that, in this section, higher revenues could be obtained through economic policies as well as changing the pattern of cultivation. Furthermore, the results revealed that about 44 percent of the optimal allocation revenues of water resources ($115 billion) were improved between the agricultural sectors, compared to the current situation, by applying a coefficient of 0.9 compared to two coefficients of 0.75.

In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.

As one of the most important communication scenarios in the coming fifth generation (5G) cellular networks, ultrareliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems. However, one of the biggest challenges is that how to integrate URLLC and control performance together to maximize the overall system performance. In this paper, we investigate the resource allocation for URLLC uplink in real-time wireless control systems. Specifically, we first discuss the relationship between communication and control performance. Based on that, we convert the hybrid co-design problem into a regular wireless resource allocation problem. Then, we propose an iteration algorithm to obtain the optimal wireless resource allocation. Simulation results indicate the performance of our method.

In this article, we address the problem of optimal joint power and resource allocation when Non-Orthogonal Multiple Access (NOMA) and Orthogonal Frequency Multiple Access (OFDMA) are combined for a hybrid downlink multiple access. First, an optimal solution of joint power and resource allocation minimizing the transmit power consumption, is obtained by rewriting the original optimization problem into an equivalent convex one and then by solving it by means of the well-known interior-point method. Second, we analyze the properties of the optimum, which are twofold; we show that at the optimum, the order of the users is preserved from one channel to another. Then, we derive a closed-form expression for the optimal power allocation in the particular case where all users have positive non-zero transmit powers in all channels.

Teaching resources in higher education are an important part of the talent cultivation system of higher education institutions. Dynamic management and optimal allocation of teaching resources are the focus of the construction of each school, which reflects the teaching, research, and management level of the school to a certain extent. In this paper, after combining the current situation and management problems of teaching resources in six schools in City A and putting forward constructive opinions, we further use data envelopment analysis to study the efficiency of teaching resource allocation in School H in City A and put forward the method of resource optimization.The input redundancy rate of School X1 in School H during the three years of 2021-2023 is 30.25%, 44.15%, and 55.26%, respectively. The data shows that the school has an excess of teacher inputs and there is a shortage of resources for teachers. For the current dynamic management and resource allocation problems of teaching resources in A city school, this paper argues that the management of school teaching resources is a long-term and complex organic whole, and needs to be reformed in terms of structure, resource integration and management, and to construct a high-quality and efficient management model. In terms of resource optimization and allocation, schools can optimize the talent resource allocation mechanism and other aspects to promote the full allocation of teaching resources in higher education.

Cooperative dynamic free-space optical (FSO) networks exploit the fact that atmospheric losses are distance dependent to enhance the performance of FSO networks. This enhancement is achieved by sharing the resources of shorter links among different nodes in the network. In this paper, two joint transceiver placement and resource allocation schemes are proposed to optimally place FSO redundant transceivers based on optimal resource allocation in cooperative dynamic FSO networks. Specifically, one scheme increases reliability and capacity, while the other increases reliability and fairness of cooperative dynamic FSO networks during severe weather conditions. The schemes are formulated as multi-objective and bi-level integer linear programming problems and solved using an exhaustive search to obtain optimal solutions. The numerical results reveal that higher reliabilities can be achieved with enhanced capacities and fairness using the first and second schemes, respectively. Furthermore, these improvements are achieved by using many fewer numbers of FSO redundant transceivers than those of random placement.

Advanced metering infrastructure targets the real-time collection of massive multisource data with smart meters (SMs), forming a key component of active distribution networks (ADNs). To achieve comprehensive situation awareness for ADNs, the use of high-speed and reliable two-way communications, such as 5G communications, is the basis. However, it is challenging to optimally allocate wireless resources for massive data, especially when emergency events occur randomly. This study discusses an energy-efficient and optimal wireless resource allocation method employing 5G uplink-based machine-type communications for achieving ADN situation awareness. We propose a 5G-based framework that can reasonably reserve resource blocks for emergency data and model the resource allocation scheme as a problem of maximizing energy efficiency (EE). Then, by using the sample average approximation theory, we transform the stochastic constraint and summarize the scheme as a mixed-integer linear programming problem. Finally, we construct an iterative algorithm based on the Lagrange dual algorithm for searching the global optimal allocation scheme. The results of simulations and experiments illustrate that even if emergency events occur randomly, our algorithm can optimally support the cooperative transmission of all sampled data while providing the best EE for large-scale SM communications.

An evolutionary algorithm-based optimal allocation method of wind resources under the background of carbon neutralization is proposed in order to better achieve the goal of energy conservation and emission reduction under the background of carbon neutralization, aiming at the current unreasonable allocation of wind resources. The evaluation model of balanced wind resource allocation is designed, and the evaluation index of optimal wind resource allocation is constructed using the evolutionary algorithm. The optimal allocation path of wind energy resources is chosen to achieve the goal of reasonable wind energy resource allocation. Finally, simulation experiments show that using an evolutionary algorithm to solve the problem of poor energy allocation and achieve the research goal, the optimal allocation method of wind energy resources under the background of carbon neutralization can effectively solve the problem of poor energy allocation.

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

Water competing conflict among water competing sectors from different levels should be taken under consideration during the optimization allocation of water resources. Furthermore, uncertainties are inevitable in the optimization allocation of water resources. In order to deal with the above problems, this study developed a fuzzy max–min decision bi-level fuzzy programming model. The developed model was then applied to a case study in Wuwei, Gansu Province, China. In this study, the net benefit and yield were regarded as the upper-level and lower-level objectives, respectively. Optimal water resource plans were obtained under different possibility levels of fuzzy parameters, which could deal with water competing conflict between the upper level and the lower level effectively. The obtained results are expected to make great contribution in helping local decision-makers to make decisions on dealing with the water competing conflict between the upper and lower level and the optimal use of water resources under uncertainty.

The increasing frequency of high-impact, low-probability (HILP) events, such as natural disasters and cyberattacks, poses significant risks to the energy sector, highlighting the urgent need for resilient distribution grids. This paper proposes a novel framework to enhance power grid resilience following HILP events by strategically deploying mobile battery energy storage systems (MBESSs) and dynamically forming microgrids. This approach addresses the shortcomings of strategies that often focus on individual resilience aspects. The framework leverages both pre-event optimization and post-event dynamic resource allocation to achieve robust and efficient grid recovery. A novel quantitative metric is developed to evaluate grid resilience, enabling informed decision-making for MBESS deployment and scheduling. Before HILP events occur, the framework optimizes MBESS charge/discharge schedules to improve operational performance and enhance grid resilience. Following an event, the framework rapidly forms microgrids and dynamically relocates MBESSs to prioritize the restoration of loads, accelerating recovery efforts. By combining MBESS capabilities with microgrid formation and optimized resource allocation, this work provides a comprehensive and adaptable framework for bolstering grid resilience in the face of unforeseen disruptions, ultimately contributing to a more resilient and reliable power grid.