Resource Allocation Algorithm Research Articles

Blockchain-based 6G task offloading and cooperative computing resource allocation study

In the upcoming era of 6G, the accelerated development of the Internet of Everything and high-speed communication is poised to provide people with an efficient and intelligent life experience. However, the exponential growth in data traffic is expected to pose substantial task processing challenges. Relying solely on the computational resources of individual devices may struggle to meet the demand for low latency. Additionally, the lack of trust between different devices poses a limitation to the development of 6G networks. In response to this issue, this study proposes a blockchain-based 6G task offloading and collaborative computational resource allocation (CERMTOB) algorithm. The proposed first designs a blockchain-based 6G cloud-network-edge collaborative task offloading model. It incorporates a blockchain network on the edge layer to improve trust between terminals and blockchain nodes. Subsequently, the optimization objective is established to minimize the total latency of offloading, computation, and blockchain consensus. The optimal offloading scheme is determined using the wolf fish collaborative search algorithm(WF-CSA) to minimize the total delay. Simulation results show that the WF-CSA algorithm significantly reduces the total delay by up to 42.58% compared to the fish swarm algorithm, wolf pack algorithm and binary particle swarm optimisation algorithm. Furthermore, the introduction of blockchain to the cloud-side-end offloading system improves the communication success rate by a maximum of 14.93% compared to the blockchain-free system.

Uplink resource shared interference mitigation scheme for in-band D2D underlay 5G networks

The benefits of Device-to-Device (D2D) communication often include increased overall system throughput, reduced latency, and increased spectrum efficiency. These advantages usually come from improved spatial reuse of time and frequency resources and the last but not least, the proximity of users engaged into D2D communication. Radio Resource allocation is a critical issue in D2D model and if not dealt with carefully, it can lead to significant interference to both pure cellular and D2D users, thereby proving detrimental to the desired outcomes. This paper presents novel Uplink Resource Shared Interference Mitigation Scheme called URSIMS. URSIMS adopts guard zones, further it implements radio resource allocation algorithm (RRAA) and distance dependent dynamic power control algorithm (DDDPA) with the goal to mitigate interference and achieve increased overall system throughput and spectral efficiency. URSIMS has been compared with other similar state of the art both for static and user mobility based scenarios; it is found that URSIMS responded adequately well and outperformed Random and Greedy schemes in terms of salient performance indicators including overall system throughput, pure cellular user throughput, spectral efficiency and D2D pair throughout. URSIMS did provide sustained higher system throughput, which significantly improved further in case of increased user mobility while compared to Random and Greedy schemes respectively.

MEC-enabled resource allocation in Internet of Vehicles

Mobile edge computing (MEC), a promising technology, is widely used in the context of the Internet of Vehicles (IoV) owing to its robust computing capabilities and proximity to mobile devices. Leveraging its advantageous features, MEC facilitates lower latency and more efficient computing services for intelligent vehicles compared to preceding technologies. The tasks inherent to IoV exhibit varying sensitivities to latency, necessitating a nuanced approach to processing. Therefore, this study aims to develop a sophisticated solution in the form of a joint task sequencing and resource allocation (JTSARA) algorithm based on deep reinforcement learning. The primary objective is to adeptly manage both computing and communication resources within the MEC environment, thereby ensuring the fulfillment of performance requirements of tasks. Initially, tasks are categorized into distinct levels according to their latency tolerance. Subsequent to this classification, a method is formulated to optimize task sequencing, improving the efficiency of computational processing. Furthermore, we tackle the intricate challenge of resource allocation by transforming it into a Markov decision process, employing deep reinforcement learning for resolution. The feasibility and effectiveness of the proposed scheme are demonstrated through comprehensive simulation experiments, affirming its potential for enhancing the performance of IoV systems.

A cloud-edge collaborative computing framework using potential games for space-air-ground integrated IoT

As a critical component of space-air-ground integrated IoT, the aerial network provides highly reliable, low-latency and ubiquitous information services to ground users by virtue of their high mobility, easy deployment and low cost. However, the current computation and resource management model of air-ground integrated networks are insufficient to meet the latency demanding of emerging intelligent services such as autonomous systems, extended reality and haptic feedback. To tackle these challenges, we propose a computation offloading and optimization method based on potential game. First, we construct an cloud-edge collaborative computing model. Secondly, we construct Offloading Decision Objective Functions (ODOF) with the objective of minimum task processing latency and energy consumption. ODOF is proved to be a Mixed Inferior Nonlinear Programming (MINLP) problem, which is hard to solve. ODOF is converted to be a full potential game, and the Nash equilibrium solution exists. Then, a computational resource allocation algorithm based on Karush–Kuhn–Tucker (KKT) conditions is proposed to solve resource allocation problem. On this basis, a distributed game-based computational offloading algorithm is proposed to minimize the offloading cost. Extensive simulation results demonstrate that the convergence performance of the proposed algorithm is reduced by 50%, the convergence time is reduced by 13.3% and the average task processing delay is reduced by 10%.

Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system’s achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

Dynamic Adaptive Resource Allocation for Edge Computing in Big Data Analytics Using GBDT, DQN, and GA Algorithms

Edge computing in big data refers to processing and analysing data closer to its source, reducing latency and bandwidth usage. It leverages devices at the network edge to perform computations, making real-time analytics feasible. This distributed approach improves efficiency and enables faster decision-making, critical for applications like IoT, autonomous vehicles, and healthcare. The research proposes an innovative approach that harnesses three machine learning algorithms Gradient Boosting Decision Trees (GBDT), Deep Q-Network (DQN), and Genetic Algorithm (GA) to enable dynamic adaptive resource allocation within edge computing environments tailored for big data analytics. GBDT enhances classification accuracy by sequentially refining predictions through decision trees, accommodating heterogeneous data types and yielding high prediction accuracy crucial for dynamic edge environments. The GA evaluates resource allocation strategies represented as chromosomes within a population, selecting promising solutions as parents for the next generation and generating diverse offspring through crossover and mutation operations to discover optimal solutions. DQN facilitates intelligent resource allocation by iteratively refining Q-values based on experiences gathered during interactions with the environment, utilizing a neural network to determine optimal actions for a given state, thereby enhancing performance and efficiency in edge computing environments. This integrated approach ensures flexible resource allocation and fortified capabilities for big data analytics within edge computing environments. The research underscores GBDT as the most promising algorithm for resource allocation in edge computing environments, owing to its exceptional performance in resource utilization, scalability, and accuracy. This nuanced understanding of algorithmic behaviour in dynamic settings offers invaluable insights for optimizing resource allocation strategies, thereby enhancing the efficiency and effectiveness of edge computing systems in handling big data analytics tasks.

Iterative Trajectory Planning and Resource Allocation for UAV-Assisted Emergency Communication with User Dynamics

The demand for air-to-ground communication has surged in recent years, underscoring the significance of unmanned aerial vehicles (UAVs) in enhancing mobile communication, particularly in emergency scenarios due to their deployment efficiency and flexibility. In situations such as emergency cases, UAVs can function as efficient temporary aerial base stations and enhance communication quality in instances where terrestrial base stations are incapacitated. Trajectory planning and resource allocation of UAVs continue to be vital techniques, while a relatively limited number of algorithms account for the dynamics of ground users. This paper focuses on emergency communication scenarios such as earthquakes, proposing an innovative path planning and resource allocation algorithm. The algorithm leverages a multi-stage subtask iteration approach, inspired by the block coordinate descent technique, to address the challenges presented in such critical environments. In this study, we establish an air-to-ground communication model, subsequently devising a strategy for user dynamics. This is followed by the introduction of a joint scheduling process for path and resource allocation, named ISATR (iterative scheduling algorithm of trajectory and resource). This process encompasses highly interdependent decision variables, such as location, bandwidth, and power resources. For mobile ground users, we employ the cellular automata (CA) method to forecast the evacuation trajectory. This algorithm successfully maintains data communication in the emergency-stricken area and enhances the communication quality through bandwidth division and power control which varies with time. The effectiveness of our algorithm is validated by evaluating the average throughput with different parameters in various simulation conditions and by using several heuristic methods as a contrast.

Power-Efficient Dispatching of Time-Sensitive Requests in the Fog-Enabled Industrial Internet of Things

Recent advancements in service offerings and the emergence of new application areas have reveal the limitations of cloud computing as a solely solution. One of these new areas is the industrial internet of things, where industrial processes are controlled in a network-based and automated way which brings many new challenges. To solve the challenges, fog computing has been introduced as a complementary paradigm to the cloud. Resource management is one of those challenges need further investigation. The industrial internet of things by its own characteristics has led traditional methods to be inefficient within this evolving ecosystem. Therefore, fully utilizing potential of the new system necessitates effective resource management. This manuscript aims to investigate the problem of resource management in the emerging cloud-fog ecosystem of the industrial internet of things. Considering the dynamic nature of users, this study proposes the algorithms for dynamic resource allocation and provisioning based on deadlines. We formulate the problem as a multiple criteria stochastic problem and introduced an online solution. Our proposed solution takes into account the unique characteristics of Fog computing and the dynamic behavior of users. To effectively handle the scheduling of requests, the research suggests utilizing the Lyapunov optimization method and Lyapunov drift theory. The Lyapunov method helps to merge and measure all the related objectives in terms drift. The objective function is optimized, in each time slot, using a drift-plus-penalty weight parameter. By adopting this approach, the system can operate efficiently while meeting the constraints and performance requirements of IoT applications.

Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid Algorithm for Optimal Resource Allocation and Efficiency in Contemporary Networked Systems

Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid Algorithm for Optimal Resource Allocation and Efficiency in Contemporary Networked Systems

Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in Ultra-Dense Networks

Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in Ultra-Dense Networks

Blockchain-based cooperative game bilateral matching architecture for shared storage

The development of IPFS (InterPlanetary File System) and blockchain-based distributed storage projects has brought new possibilities to the field of storage. This paper proposes a blockchain-based cooperative game bilateral matching architecture as a novel approach for shared storage networks. In traditional competitive (non-cooperative) game models, the allocation of storage resources is centered around pricing, leading to a scenario where node providers often engage in price competition to obtain greater rewards, resulting in an imbalance in resource allocation for both buyers and sellers. In contrast, a distributed storage model based on cooperative game theory can better facilitate cooperation and resource sharing among node providers. This paper designs a storage resource allocation algorithm based on the stable marriage matching algorithm, demonstrating the stability of this algorithm as a matching solution. The paper also analyzes the differences between cooperative and non-cooperative game models in the market, and explores an equilibrium pricing mechanism guided by supply and demand. Furthermore, the paper introduces a trading mechanism for storage resources, including publication standards and matching schemes, ensuring efficient and trustworthy interaction between storage suppliers and demanders in a decentralized network centered around storage resources, thus enabling the circulation of the value of storage resources. A prototype of a blockchain-based shared storage trading system is implemented in this paper, utilizing bilateral matching for tradings. System evaluation and experimental testing are conducted, with results showing that the average utility value of the matching trading mechanism proposed in this paper outperforms the Double Auction-based matching model under any Poisson distribution (λ=0.1, 0.2, 0.3, …, 0.9) conditions set in the experiments. Additionally, compared to the traditional approach of directly storing complete data content on the chain, the design proposed in this paper effectively reduces on-chain storage consumption by approximately 27.06%.

A Benders decomposition algorithm for resource allocation with multi-resource operations

This paper addresses a real life scheduling problem characterised by multi-resource operations whose completion simultaneously requires more than one (renewable) resource of different types. Such problems arise in various companies not only in manufacturing but also in services. Solving such a problem needs to address two interconnected subproblems: a sequencing subproblem and a resource-allocation subproblem if more than one resource is available in some types. The resource-allocation subproblem consists of allocating to each operation the resources it requires while the sequencing subproblem consists of determining the order in which each resource performs the operations assigned to it. This paper focuses on the resource-allocation subproblem. It generalises the basic scheduling problems which consist of determining the operations' starting or completion times for a given processing sequence for every resource. We consider a cost function taking into account the makespan, the cost of resource utilisation, and the load imbalance among the resources of the same type. We first formulate the problem into a mixed-integer linear program (MILP). To efficiently solve it, even in practical-sized instances, an exact algorithm called BD (Benders decomposition with enhancing cuts) is developed where the master problem only considers integer variables. We prove that the slave problem can be transformed into finding the longest paths in a digraph and therefore can be solved with the Bellman–Ford algorithm. To enhance the efficiency of the method, equivalent solutions are limited in the master problem. The performance of the approach is evaluated by comparing it against CPLEX, a state-of-the-art commonplace MILP solver, used to directly solve the initial MILP. The computational results demonstrate that BD provides competitive solutions in all upper and lower bounds. In particular, it improves, compared with CPLEX, the upper and lower bounds by 5.07% and 4.63%, respectively, in solving practical-sized instances. The experiment also shows that considering load balancing can make more rational use of resources and avoid adverse effects caused by excessive workload of staff and imbalanced use of equipment, which is very important in real-world production.

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network.

In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24-29% compared with the existing state-of-the-art techniques.

Fairness‐aware eMBB/URLLC spectrum resource multiplexing to ensure reliability in vehicular networks

AbstractSpectrum resource multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra‐Reliable and Low Latency Communications (URLLC) via puncturing in vehicular networks has become an important research direction. The main issue in this direction is how to minimize the adverse impact of preemptive puncturing on eMBB traffic. However, the available eMBB/URLLC coexisting schemes lack considerations on the quality of service of a single eMBB user and fairness between users. In view of this, we formulate the eMBB/URLLC multiplexing problem to maximize eMBB reliability and fairness under the premise of meeting the requirements of URLLC as a nonlinear integer programming (NIP) in this article. To balance the performance and computational complexity, we decompose this NIP into two subproblems that be solved efficiently. For the eMBB spectrum resource allocation subproblem of maximizing the reliability and fairness under resource constraints, we prove that this problem is NP‐hard, and design a Deep Q‐Network (DQN)‐based spectrum resource allocation algorithm and a two‐stage heuristic spectrum resource allocation algorithm, respectively. For the URLLC preemption subproblem, we further propose bandwidth‐sensitive URLLC spectrum resource preemption algorithm and time‐sensitive URLLC spectrum resource preemption algorithm, respectively. Simulation results show that the proposed scheme can guarantee better fairness and reliability for eMBB users compared with the comparison schemes. Besides, for the eMBB resource allocation problem, the proposed scheme can achieve near‐optimal performance with low complexity.

Compound Matrix-Based Project Database (CMPD)

The impact of projects is vital, from business operations to research to the national economy. Therefore, management science and operation research have extensively studied project scheduling and resource allocation for over six decades. Project databases were proposed to test algorithms, including simulated or real, single or multiprojects, and single-mode or multi-mode projects. However, the dozens of project databases are extremely heterogeneous regarding the file structure and the features of the modeled projects. Furthermore, the efficiency and performance of project scheduling and resource allocation algorithms are susceptible to the characteristics of projects. Therefore, the proposed Compound Matrix-Based Project Database (CMPD) collects and consolidates the most frequently used project databases. The proposed Unified Matrix-Based Project-Planning Model (UMP) sparse matrix-based model enables the addition of new features to existing project structures, such as completion priorities, structural flexibility, and quality parameters, to broaden the scope of considered projects and to take account of flexible approaches, such as agile, extreme, and hybrid projects.

Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

Due to the rapid development of low earth orbit satellite constellations, e.g., Starlink, OneWeb, etc., integrated satellite-terrestrial networks have been viewed as a promising paradigm to globally provide satellite internet services for users. However, when the contents from ground data centers are provided for users by satellite networks, there will be high capital expenditures in terms of communication delay and bandwidth usage. To this end, in this paper, a cooperative-caching and resource-allocation problem is investigated in integrated satellite–terrestrial networks. Popular contents, which are cached on satellites and ground data centers, can be accessed via inter-satellite and satellite–terrestrial networks in a cooperative way. The optimization problem is formulated to jointly minimize the deployment costs of storage resource usage and network bandwidth consumption. A cooperative caching and resource allocation (CCRA) algorithm based on a neighborhood search is proposed to address the problem. The simulation results demonstrate that the proposed CCRA algorithm outperforms Greedy and BFS in reducing the deployment costs.

Mobility aware blockage management of a multi-user multi-cell hybrid Li-Fi Wi-Fi system with freeform diversity receiver

In order to accommodate high data traffic, the co-deployment of heterogeneous networks like light fidelity (Li-Fi) and wireless fidelity (Wi-Fi) can offer supplementary small-cell layers of support and bring about a paradigm shift in achievable system throughput and quality of service. However, dense deployment of Li-Fi access points(APs) in an indoor arena often leads to co-channel-interference (CCI) that can be fruitfully diminished by adopting a customized optical front-end called freeform diversity receiver (FDR). With regard to multi-user association, this work judiciously imparts the motivation behind the adoption of FDR in a hybrid Li-Fi Wi-Fi network (HLWNet). Based on different mobility scenarios and blockage conditions the performance of the proposed HLWNet has been evaluated. A Li-Fi channel model with FDR and a rule-based resource allocation algorithm (RBRA) has been proposed for the purpose. Nevertheless, the network data quality of the multi-user system has been estimated in terms of packet loss, latency, and fairness index. Unlike the existing optimal resource allocation (ORA), the RBRA demonstrates superior network performance. Additionally, the execution time in the RBRA is reduced by a factor of 89 compared to the optimal resource allocation algorithm. Simulation results show a satisfactory fairness index of more than 0.85 and latency within 2.1 ms for a 10-user association inside an indoor environment of 25m2 floor area. In the absence of LOS blockage, the system throughput exhibits minimal variation, staying consistent for both methods with less than a 2% difference. However, significant improvement in average user throughput and effective system throughput has been observed compared to the existing studies. Despite line-of-sight (LOS) blockage, the proposed system with RBRA consistently maintains throughput within the range of 2.01 Gbps to 2.55 Gbps. The average user throughput, varying from 182 Mbps to 480 Mbps, is contingent upon the number of associated users, which ranges from 4 to 14.

Reinforcement learning-based resource allocation for dynamic aggregated WiFi/VLC HetNet

High transmission rates and low power consumption make visible light communication (VLC) a highly promising supplementary technology for the next generation of mobile communication. Taking into account the limited coverage area, VLC can be combined with WiFi as a heterogeneous network (HetNet), thanks to their non-overlapping spectrum. In order to fully utilize the advantages of the WiFi and VLC technologies, a new aggregated WiFi/VLC HetNet consisting of a single WiFi AP and multiple VLC APs, is designed, where the user equipment (UE), for the first time, can access multiple VLC APs and one WiFi AP simultaneously, and be allowed to acquire multiple resource blocks (RB) in each AP at the same time. To optimize the performance of the above designed HetNet, a multi-objective optimization problem (MOOP) is formulated, which aims to maximize system throughput while reducing the handover rate. Since the above MOOP is non-convex and nonlinear, the traditional resource allocation (RA) algorithm has a complex calculation process and poor timeliness performance to deal with this problem. To solve the above MOOP, a reinforcement learning (RL)-based RA algorithm is proposed, and considering the RB handover overhead in the aggregated WiFi/VLC HetNet, a reward function related to the system throughput and the UE handover rate is carefully designed. System throughput, system handover rate, user satisfaction, as well as user fairness performance are analyzed under three typical indoor illumination layouts. Finally, compared with the greedy algorithm and the hypergraph-based carrier aggregation algorithm, numerical results show that the proposed RL-based RA algorithm could improve the system throughput over the former two algorithms by 30.26% and 19.71%, while reducing the system handover rate by 0.15% and 0.02%, respectively.

Preference based multi-issue negotiation algorithm (PMINA) for fog resource allocation

Preference based multi-issue negotiation algorithm (PMINA) for fog resource allocation

Resource allocation for integrated satellite‐terrestrial networks based on RSMA

AbstractTo achieve high data rate transmission of virtual reality (VR) applications globally, a resource allocation algorithm based on Rate‐Splitting Multiple Access (RSMA) in an integrated satellite‐terrestrial network (ISTN) is proposed. In this network, a user selection strategy is proposed. The maximum transmission power budgets of the base station and satellite, as well as the service quality constraints for all users, are considered. Then, a joint resource allocation problem is formulated. To tackle the non‐convex problem, auxiliary variables are introduced and the first‐order Taylor expansion method is used to convert it into a convex one. An iterative algorithm is proposed based on the successive convex approximation (SCA) to allocate rate and power resources. Simulation results demonstrate that the proposed algorithm significantly enhances the transmission rate and effectively satisfies the VR transmission rate requirements, when compared to benchmark algorithms.