Multiple Resource Allocation Research Articles

Robust joint multiple resources allocation algorithm for cooperative underwater acoustic communication networks

AbstractIn this article, a joint relay selection, power control and time allocation problem is studied to maximize the energy efficiency for the cooperative underwater acoustic communication networks. The joint optimization problem is full of challenges due to the strong coupling of multiple resources and the uncertain characteristics of the underwater acoustic communication scenario. To address this issue, the worst‐case method is employed to transform an original uncertain problem into a deterministic problem. Furthermore, we propose the block coordinate descent‐based method to decouple the strongly coupling multi‐resource allocation problem into three relatively independent sub‐problems. The coupling of multiple resources is completely decoupled, thereby greatly reducing the solving difficulty. In addition, given that the sub‐problems with the fractional objective function are still non‐convex and hard to solve, the Dinkelbach‐based method is proposed to transform the fractional objective function into a subtractive form. At last, the relay selection sub‐problem is transformed into a integer programming problem, and the time allocation sub‐problem is transformed into a linear programming problem, whose optimal solutions can be obtained by some well‐established solution methods. The power allocation problem is transformed into a convex optimization problem, which can be solved by the Lagrangian dual method. Finally, in the proposed iteration structure, the three sub‐problems are alternatingly solved until convergence. Simulation results are presented to demonstrate the efficiency and robustness of the proposed algorithm.

Online Multi-Resource Allocation for Network Slicing in 5G with Distributed Algorithms

With the increasing scale of mobile cellular network applications, 5G mobile network infrastructure provides customizable services to users in the form of network slices. How to effectively allocate existing resources in real dynamic networks with time-varying network utility is a key issue that previous work did not consider. This paper first initializes the multi-resource allocation problem of network slicing in an online manner, where the utility function is set to change over time. Therefore, we propose Metis, an online network slicing resource allocation framework that combines the time-varying nature of the network utility function given bandwidth and processing power constraints with the requirement of virtual network function isolation. The goal is to maximize the cumulative network utility in the long term and specify multiple resource allocation problems by utilizing concave optimization methods. In addition, a distributed algorithm based on the online alternating direction method of multipliers with regret optimization has been developed to achieve optimal resource allocation. Our mathematical analysis proves that Metis can provably converge to the optimal solution and the result of experiments demonstrates a steady state behavior of Metis, which converges in dynamic network settings.

Research on the Influencing Factors of DT of SMEs from TOE Perspective

Purpose: This paper is to examine how the DT of SMEs is affected and driven, and provide theoretical and practical implications for the DT of SMEs. Design/methodology/approach: Starting from the TOE framework, combined with the resource-based view and contingency theory, this study identifies the antecedents that affect the DT of SMEs from the three dimensions of technology, organization, and environment. Under the framework of TOE, digital technology infrastructure, employee skills, management support and environment uncertainty will be fully discussed. With the data from 221 SMEs from China analyzed by the fs_QCA 3.0, the driving paths for high DT are divided into different conditions configurations. Findings: The DT of SMEs is not the result of a single factor driven, but rather the result of the synergistic effect of the enterprise. There are multiple resource allocation configurations that drive the DT of SMEs, indicating the complexity and synergy of the factors affecting the DT of SMEs, which can prove the DT of SMEs has the characteristic of “multiple concurrency”. Research limitations/implications: DT is a very complex phenomenon, and there are many factors that affect the DT of SMEs. Based on the TOE framework, this paper selects four factors, and many possible factors are not included in the model. Besides, for QCA, a case-based research method with a quantitative approach, structured questionnaire surveys often result in a lack of detailed understanding of the research subjects and the inability to delve into all case enterprises. Originality/value: The findings will enrich the research in the DT field, and deepen the rational understanding of the complex interaction nature of multiple factors behind the successful DT of SMEs and provide path guidance for SMEs to realize DT. The research conclusions of this study provide valuable insights for SMEs to promote DT by combining possible antecedent configurations.

RIS-Assisted Wireless Powered MEC: Multiple Access Design and Resource Allocation

RIS-Assisted Wireless Powered MEC: Multiple Access Design and Resource Allocation

Reinforcement Learning With Data Envelopment Analysis and Conditional Value-At-Risk for the Capacity Expansion Problem

The capacity expansion problem is solved by accurately measuring the existing demand-supply mismatch and controlling the emissions output, considering multiple objectives, specific constraints, resource diversity, and resource allocation. This article proposes a reinforcement learning (RL) framework embedded with data envelopment analysis (DEA) to generate the optimal policy and guide the productivity improvement. The proposed framework uses DEA to evaluate efficiency and effectiveness for reward estimation in RL, and also assesses conditional value-at-risk to characterize the risk-averse capacity decision. Instead of focusing on short-term fluctuations in demand, RL optimizes the expected future reward with sequential capacity decisions over time. An empirical study of U.S. power generation validates the proposed framework and provides the managerial implications to policy makers. The results show that the RL agent can successfully learn the optimal policy through observing the interactions between the agent and the environment, and suggest the capacity adjustment that can improve efficiency by 8.3% and effectiveness by 0.9%. We conclude that RL complements productivity analysis, and emphasizes ex-ante planning over ex-post evaluation.

Computation Offloading and Resource Allocation for E2E Tasks in Satellite Edge Computing Networks

Satellite edge computing shows promise in delivering seamless, low-latency services in challenging contexts such as deserts, oceans, and during natural disasters. Previous studies focused on the selection of suitable offloading nodes for task processing and result transmission to the original node. However, for end-to-end tasks, exemplified by applications like satellite-based remote sensing and forestry monitoring, the disparity between source and destination nodes emphasizes the importance of routing selection in data transmission. This aspect substantially limits the effectiveness of traditional computation offloading methods. In this paper, we formulate a joint computation offloading, routing, and multiple resource allocation optimization problem for end-to-end tasks in satellite edge computing networks. Moreover, to reduce the complexity of this nondeterministic polynomial-time hard (NP-hard) problem, we decompose it into the multidimensional resource allocation in fixed offloading and routing condition and the computation offloading and routing selection problem. An algorithm based on binary particle swarm optimization for joint computation offloading, routing, and multiple resource allocation optimization problem is proposed. Simulation results are presented to demonstrate the performance of our scheme compared with other baseline schemes.

Multiple Task Resource Allocation Considering QoS in Energy Harvesting Systems

Most approaches to resource allocation in energy harvesting systems for Internet-of-Things (IoT) networks do not consider real-time periodic task allocation with Quality of Service (QoS). This paper studies the offline two-dimensional optimization problem for allocating randomly harvested energy flowing causally between slots into different real-time periodic tasks on an IoT device. We formulate an energy allocation problem, for tasks with different energy costs and requested QoS, which aims to maximize a convex reward function subject to energy causality (EC), energy saturation (ES) and task executable (TE) constraints within a certain length of time. We decouple the optimization problem into two subproblems after analysis. First, we propose a novel method to allocate energy to slots based on the Karush-Kuhn-Tucher conditions only considering the EC & ES constraints. The proposed method outperforms the state of the art “Tunnel Policy”, based on geometric programming. Next, an adaption is made to satisfy the TE constraints by allocating in the original constant power slots directly without iteratively checking the wasted energy. Finally, the energy already allocated to slots is put into tasks to complete the two-dimensional allocation. The effectiveness of the proposed methods and task framework are validated by extensive experiments.

A Survey on the Fairness of Recommender Systems

Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people’s daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is whether recommendations are fair. Unfair recommendations are not only unethical but also harm the long-term interests of the recommender system itself. As a result, fairness issues in recommender systems have recently attracted increasing attention. However, due to multiple complex resource allocation processes and various fairness definitions, the research on fairness in recommendation is scattered. To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW. First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues. Then, we review recommendation datasets and measurements in fairness studies and provide an elaborate taxonomy of fairness methods in the recommendation. Finally, we conclude this survey by outlining some promising future directions.

Study of Water Resource Allocation and Optimization Considering Reclaimed Water in a Typical Chinese City

Reclaimed water is considered to be an important alternative to freshwater to solve the imbalance between the supply and demand of regional water resources; it is also recognized as an effective tool for alleviating ecological problems caused by insufficient water flow. Yiwu City is a typical area experiencing a water shortage in southeastern China because the regional water resources are limited. In this study, the multiple water resource allocations in Yiwu City are optimized, the complex coupling model of multiple water resource allocation is established, and both the economic and ecological effects of multiple water resource allocation in Yiwu City are simulated and analyzed. The simulation results of optimizing the multiple water resource allocations show an efficient way of reclaimed water utilization in this typical Chinese city. In order to ensure the future economic and social development of Yiwu City, it is necessary to introduce reclaimed water into different fields, such as residential water, industrial water, agricultural water, and environmental water. Reclaimed water has also proven to have a high capability for pollutant control and reduction, which is also important to the ecology and environmental protection.

A two-stage stochastic optimization for disaster rescue resource distribution considering multiple disasters

In disaster rescue, multiple resource allocation strategies before and after disasters are a key research issue. Response planning for sudden-onset disasters must take into account the inherent uncertainties associated with the catastrophe and potential ensuing disasters. This article investigates the problem of resource allocation optimization in the context of natural disasters accompanied by the occurrence of multiple secondary hazards. A two-stage stochastic optimization model is proposed to simulate the scenario of random occurrence and severity of multiple natural hazards to optimize the resource allocation of rescue teams, warehousing items and medical resources when multiple natural hazards occur in combination. A particle swarm optimization (PSO) algorithm is designed and compared with a commercial solver (CPLEX) and lower bound at various scales. The results show that PSO performs optimally for large-scale cases. A case study, undertaken in Beijing, demonstrates that this method can improve the estimated casualty rescue success rate to 90.6%.

Joint multiple resource allocation for offloading cost minimization in IRS-assisted MEC networks with NOMA

Effective resource allocation can exploit the advantage of intelligent reflective surface (IRS) assisted mobile edge computing (MEC) fully. However, it is challenging to balance the limited energy of MTs and the strict delay requirement of their tasks. In this paper, in order to tackle the challenge, we jointly optimize the offloading delay and energy consumption of mobile terminals (MTs) to realize the delay-energy tradeoff in an IRS-assisted MEC network, in which non-orthogonal multiple access (NOMA) and multiantenna are applied to improve spectral efficiency. To achieve the optimal delay-energy tradeoff, an offloading cost minimization model is proposed, in which the edge computing resource allocation, signal detecting vector, uplink transmission power, and IRS phase shift coefficient are needed to be jointly optimized. The optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension variables. To solve the intractable problem, we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable types. The computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource allocation. Further, the wireless transmission subproblem is solved iteratively through decoupling the residual variables. In each iteration, the closed-form solution of residual variables is obtained through different successive convex approximation (SCA) methods. We verify the proposed algorithm can converge to an optimum with polynomial complexity. Simulation results indicate the proposed method achieves average saved costs of 65.64%, 11.24%, and 9.49% over three benchmark methods respectively.

An Adaptive Memetic Algorithm for the Joint Allocation of Heterogeneous Stochastic Resources.

This article investigates the joint allocation problem of stochastic resources (JASRs), which is widely found in complex systems. A general mathematical model for joint allocation of multiple heterogeneous stochastic resources is built, capturing the interdependencies between resources, quantity constraints, capability constraints, and strategy constraints of resources. An adaptive memetic algorithm (MA) is proposed for JASR, and the multipermutation encoding method is developed to denote assignment schemes of different resources. Multiple permutation-based operators are employed in the mutation and local search process under the genetic evolution framework and learning framework, respectively. Besides, a hybrid initialization method and an adaptive replacement strategy are put forward. Moreover, a restart strategy is developed to rebuild the population to maintain the genetic diversity. Twenty-five random test instances are produced to validate the effectiveness of the proposed MA. The results of computational experiments and the Wilcoxon rank-sum test demonstrate that these JASR instances can be well handled by the proposed adaptive MA, and the proposed MA is able to provide remarkably better decision schemes for the majority of the test instances than the prevailing solution methods.

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.

Sparse Code Multiple Access Assisted Resource Allocation for 5G V2X Communications

In 5G vehicle-to-everything (V2X) systems, the scarcity of spectrum resources and the inefficiency of resource allocation make vehicle-to-vehicle (V2V) communications that require stringent latency and high reliability still a challenge. Existing relevant literatures either focus on vehicle-to-infrastructure (V2I) communications, or consider scenarios where the vehicle roles (transmitter or receiver) are fixed in V2V communications, or study the resource allocation within a single cell. What’s more, considering the high-speed movement of vehicles across the coverage regions of multiple cells, the above resource allocation problem becomes even more challenging. Toward this end, we consider in this paper a multi-cell 5G V2X system where V2V links and V2I links coexist and the vehicle roles are not fixed, and propose a sparse code multiple access-based centralized resource allocation scheme, so as to address the above challenges. In view of the fact that our formulated maximizing packet reception ratio problem is a combinatorial optimization problem and is NP-hard, we design a three-stage heuristic yet joint alternating optimization approach to obtain a suboptimal solution. Extensive numerical results demonstrate the superior performances of the proposed scheme in multiple perspectives.

An Adaptive Multiobjective Genetic Algorithm with Multi-Strategy Fusion for Resource Allocation in Elastic Multi-Core Fiber Networks

Core switching on different links in optical networks enables network operators to allocate network resources more flexibly, so as to reduce the network request blocking ratio under limited resources. Facing a differentiated network environment and diversified user demands, network operators need to optimize multiple objectives that are independent and diversionary of each other, and to provide multiple resource allocation schemes whose objective values do not dominate each other. For the static routing, spectrum, and core assignment (RSCA) problem in elastic optical networks with multi-core fiber (MCF-EONs), there is no literature that simultaneously considers core switching and multiobjective optimization algorithms. This paper improves the existing models and algorithms to adapt to the RSCA problem. In this paper, the RSCA problem is formulated as an integer linear programming model to minimize both network request blocking and crosstalk ratios simultaneously by considering core switching and inter-core crosstalk. To solve the model efficiently, we, therefore, design a joint routing and core coding scheme supporting core switching and propose a multiobjective evolutionary algorithm based on decomposition with adaptation and multi-strategy fusion (MOEA/D-AMSF), which integrates the new mechanisms of hybrid initial population generation, adaptive crossover, and double-layer and multi-point mutation in different iteration stages. These new mechanisms accelerate algorithm convergence and enhance solution diversity. Simulation results show that the proposed algorithm can obtain more dominated and diverse solutions compared with the existing multiobjective algorithm without considering core switching.

Underpinnings of User-Channel Allocation in Non-orthogonal Multiple Access for 5G

Non-orthogonal multiple access (NOMA) is a part of 5th generation (5G) communication systems. This article presents the underpinnings and underlying structures of the problem of NOMA user-channel allocation. Unlike the heuristics for NOMA user-channel allocation, the presented results are guaranteed to converge to a solution. In addition, the solutions are stable. More specifically, the deployment of results to cellular vehicular communication systems is shown as a use case of 5G technology in smart transport. Generally, the results apply to any NOMA system. Unlike the orthogonal frequency division multiple access resource allocation problem, the core matching is not the solution to NOMA resource allocation. The conditions under which the fix-point NOMA resource allocation is guaranteed to be stable from the viewpoint of both the base station and the NOMA users are described. In addition, relationships of NOMA user-channel resource allocation to game models and subgame perfect Nash equilibria are elucidated.

Proportional Fair Scheduling for Downlink mmWave Multi-User MISO-NOMA Systems

In this paper, we study non-orthogonal multiple access (NOMA) user scheduling and resource allocation problem for a generic downlink single-cell multiple input and single output (MISO) millimeter wave (mmWave) system. The larger number of packed antennas and the highly directional property of mmWave communications enable directional beamforming to achieve spatial diversity. Toward this end, we consider two different hybrid precoding schemes which are based on orthogonal matching pursuit (OMP). Users are assigned into different clusters and the base station (BS) transmits superposed signals that share the same precoding vector. Moreover, both fixed number of users per cluster and dynamic number of users per cluster are investigated. We aim to jointly optimize the user clustering, service scheduling, and power allocation strategy, in maximizing the proportional fairness (PF) among the users and exploring the multiuser diversity and multiplexing gain. Since the formulated joint user clustering, scheduling and power allocation problem is a mixed integer non-convex optimization problem, we propose a two-fold methodology. First, we apply a hybrid precoding and user clustering scheme, where the hybrid precoder is constructed by singular vector division (SVD) or minimum mean square error (MMSE). Then, with the obtained result, we approximate the proportional fairness power allocation problem by a sequence of Geometric Programming (GP) problems which are solved iteratively. The proposed scheme strikes a balance between the spectral efficiency and service fairness. Results show that the proposed MISO-NOMA scheme which is based on MMSE hybrid precoder and the proposed user scheduling and power allocation strategy under proportional fairness metric can outperform various conventional MISO schemes. Furthermore, our proposed dynamic number of users per cluster scheme outperforms the fixed scheme and can be considered as an upper bound in several aspects, including spectral efficiency and fairness.

Intelligent Resource Allocation in Industrial IoT using Reinforcement Learning with Hybrid Meta-Heuristic Algorithm

The exponential growth of networking technology has resulted in a massively expanded computer ecosystem. With implications in the Industrial Internet of Things (IIoT) and virtual reality, mobile networks operate and provide a multi-aspect strategy for multiple resource allocation paradigms and service-oriented possibilities in the computing sectors. The Mobile Edge Computing (MEC) model combines a virtual source with edge communication among execution. Thus, this study is to develop and implement a revolutionary resource allocation technique in the IIoT by combining optimal Reinforcement Learning (RL) with a hybrid meta-heuristic algorithm. The three basic levels in the proposed paradigm are "Data input layer," "Data management layer," and "Data analytics layer". The data management layer is responsible for the data collected from edge devices and external devices. The proposed model's goal at the data management layer is to provide an intelligent work allocation mechanism. The Harris Hawks-Spider Monkey Optimization (HH-SMO) method combines Harris Hawks Optimization (HHO) and Spider Monkey Optimization (SMO) to find the best job allocation. From the statistical analysis, the mean of HH-SMO-RL is 15.88%, 14.91%, 14.79%, and 5.99% superior to SMO-RL, HHO-RL, JA-RL, and DHOA-RL respectively, which has shown the resource allocation in IIoT using optimized RL respectively.

Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory.

Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered.

Towards Heat-Recirculation-Aware Virtual Machine Placement in Data Centers

As customers take virtual machines (VMs) as their demands, high-efficient placement of VMs is required to reduce the energy consumption in data centers. Existing Virtual Machine placement (VMP) strategies can minimize energy consumption of data centers by optimizing resource allocation in terms of multiple physical resources (e.g., memory, bandwidth, CPU, etc.). However, these strategies ignore the role of heat recirculation in the data center, which can cause a huge energy waste in cooling. To address this problem, we propose a heat-recirculation-aware VMP strategy for reducing the energy consumption of data centers. This novel VMP strategy takes into account heat recirculation coupled with multiple physical resource allocation to reduce the energy consumption of data centers. We design a simulated annealing based algorithm called SABA to lower the energy consumption of data centers where multiple VMs are deployed. SABA remarkably cuts down the energy consumption of physical resources through two salient features. First, it obtains an approximation of the optimum with much fewer iterations than simulated annealing algorithm (SA). Second, it reduces the number of activated servers required for VM tasks. We quantitatively evaluate the performance of SABA in terms of algorithm efficiency, the number of activated servers and the energy-saving. We compare the performance of SABA with state-of-art XINT-GA, PPVMP, TSTD and SA algorithms. Moreover, we evaluate the efficiency of SABA by leveraging a real-world 50 hours trace from practical IBM cloud data centers. Experimental results indicate that our heat-recirculation-aware VM placement strategy provides a powerful solution for improving the energy efficiency of data centers (SABA improves energy efficiency of cooling by up to 13.2% over TSTD, 13.8% over XINT-GA and 45% over PPVMP algorithm).