Dynamic Resource Allocation Scheme Research Articles

The Resilience of Electrical Support in UAV Swarms in Special Missions

Unmanned aerial vehicle (UAV) swarms serve as a dynamic platform for diverse missions, including communication relays, search and rescue operations, and environmental monitoring. The success of these operations crucially depends on the resilience of their electrical support systems, especially in terms of battery management. This paper examines the reliability of electrical support for UAV swarms engaged in missions that require prioritization into high and low categories. The paper proposes a dynamic resource allocation strategy that permits the flexible reassignment of drones across different-priority tasks, ensuring continuous operation while optimizing resource use. By leveraging the Markov chain theory, an analytical model for the evaluation of the resilience of the battery management system under different operational scenarios was developed. The paper quantitatively assesses the impact of different operational strategies and battery management approaches on the overall system resilience and mission efficacy. This approach aims to ensure uninterrupted service delivery for critical tasks while optimizing the overall utilization of available electrical resources. Through modeling and analytical evaluations, the paper quantifies the impact of various parameters and operating strategies on overall system resilience and mission availability, considering the utilization strategies of batteries and their reliability and maintenance metrics. The developed models and strategies can inform the development of robust battery management protocols, resource allocation algorithms, and mission planning frameworks, ultimately enhancing the operational availability and effectiveness of UAV swarms in critical special missions.

Developing A Novel Load Balancing Approach for Mitigating Resource Contention in Multi-Cloud Environments

In the rapidly evolving landscape of cloud computing, the management of resources and load balancing across multiple cloud environments presents significant challenges. This paper proposes a novel approach to address resource contention issues in multi-cloud environments through the development of an innovative load-balancing mechanism. By leveraging advanced algorithms and dynamic resource allocation strategies, our approach aims to effectively distribute workloads across diverse cloud infrastructures, thereby optimizing resource utilization and mitigating contention. Through extensive experimentation and evaluation, we demonstrate the effectiveness and efficiency of our proposed solution in enhancing performance and resilience in multi-cloud deployments. Our findings underscore the importance of tailored load-balancing mechanisms in enabling scalable and reliable operations in complex cloud environments.

Desktop as a Service using Local Computers

Abstract: Desktop-as-a-Service (DaaS) has gained prominence as a cloud-based solution for delivering desktop environments to users. However, traditional DaaS models often rely on remote data centers, posing concerns regarding data security, latency, and dependence on external infrastructure. In this paper, we propose a paradigm shift by introducing a novel approach to DaaS that leverages the untapped potential of local computing resources.Our system harnesses the idle computing power available within the user's local environment, including desktops, laptops, and servers, to create a distributed DaaS platform. By integrating virtualization technologies and dynamic resource allocation strategies, we enable the provisioning of desktop environments ondemand, effectively reducing the reliance on centralized data centers. Key components of our system include a centralized management platform for orchestrating resource allocation and workload distribution, virtualization techniques for isolating and managing desktop instances, and robust security measures to safeguard sensitive data. Through experimental validation, we demonstrate the feasibility and performance of our approach, showcasing improvements in data privacy, latency reduction, and resource utilization compared to traditional DaaS models. Our research presents a significant step towards decentralized and resilient computing ecosystems, offering users greater control over their desktop environments while maintaining the scalability and accessibility inherent to DaaS paradigms

A Method for 5G–ICN Seamless Mobility Support Based on Router Buffered Data

The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN’s identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay.

Joint resource allocation and security redundancy for autonomous driving based on deep reinforcement learning algorithm

AbstractAutonomous vehicles navigating urban roads require technology that combines low latency with high computing power. The limited resources of the vehicle itself compel it to offload task requirements to edge server (ES) for processing assistance. However, as the number of vehicles continues to increase, how edge servers reasonably allocate limited resources to autonomous vehicles becomes critical to the success of urban intelligent transportation services. This paper establishes an urban road scenario with multiple autonomous vehicles and an edge computing server and considers two main driving behaviour transition resource requests, namely car‐following behaviour requests and lane‐changing behaviour requests. Simultaneously, acknowledging that vehicles may encounter unforeseen traffic hazards when switching driving behaviours, a safety redundancy setting strategy is employed to allocate additional resources to the vehicle to ensure safety and model the vehicle resource allocation problem in the autonomous driving system. Double‐deep Q‐network (DDQN) is then used to solve this model and maximize the total system utility by comprehensively considering resource costs, system revenue, and autonomous vehicle safety. Finally, results from the simulation experiment indicate that the proposed dynamic resource allocation scheme, based on deep reinforcement learning for autonomous vehicles under edge computing, not only greatly improves the system's benefits and reduces processing delays compared to traditional greedy algorithms and value iteration, but also effectively ensures security.

A holistic mathematical cyber security model in fog resource management in computing environments

In the evolving landscape of computing environments, fog computing has emerged as a pivotal technology for handling the increasing demands of Internet of Things (IoT) applications. Fog computing, with its decentralized approach, brings computational power closer to IoT devices, reducing latency and bandwidth constraints. However, this distributed architecture poses significant cybersecurity challenges, necessitating a holistic mathematical model to ensure the security and integrity of fog resources. This review presents a comprehensive mathematical cyber security model that is tailored to fog resource management in computing environments. The models generally encompass a wide array of security aspects, including data confidentiality, integrity, availability, and authentication. Leveraging advanced mathematical techniques, the models offer a robust framework for assessing and enhancing security in fog computing systems. The key components include threat modeling, risk assessment, and dynamic resource allocation strategies. By systematically analyzing potential threats and vulnerabilities specific to fog computing, these models assist in devising effective countermeasures and security policies. Moreover, there is a need to incorporate an adaptive resource allocation algorithm to dynamically allocate computing resources based on security priorities and real-time threat assessments. This review demonstrates the effectiveness of the mathematical cyber security model in bolstering the resilience of fog computing environments. The review highlights the need for safeguarding the next generation of fog computing systems against cyber threats, fostering the continued growth and adoption of transformative technology.

Joint AP Selection and Task Offloading Based on Deep Reinforcement Learning for Urban-Micro Cell-Free UAV Network

The flexible mobility feature of unmanned aerial vehicles (UAVs) leads to frequent handovers and serious inter-cell interference problems in UAV-assisted cellular networks. Establishing a cell-free UAV (CF-UAV) network without cell boundaries effectively alleviates frequent handovers and interference problems and has been an important topic of 6G research. However, in existing CF-UAV networks, a large amount of backhaul data increases the computational pressure on the central processing unit (CPU), which also increases system delay. Meanwhile, the mobility of UAVs also leads to time-varying channel conditions. Therefore, designing dynamic resource allocation schemes with the help of edge computing can effectively alleviate this problem. Thus, aiming at partial network breakdown in an urban-micro (UMi) environment, an urban-micro CF-UAV (UMCF-UAV) network architecture is proposed in this paper. A delay minimization problem and a dynamic task offloading (DTO) strategy that jointly optimizes access point (AP) selection and task offloading is proposed to reduce system delay in this paper. Considering the coupling of various resources and the non-convex feature of the proposed problem, a dynamic resource cooperative allocation (DRCA) algorithm based on deep reinforcement learning (DRL) to flexibly deploy AP selection and task offloading of UAVs between the edge and locally is proposed to solve the problem. Simulation results show fast convergence behavior of the proposed algorithm compared with classical reinforcement learning. Decreased system delay is obtained by the proposed algorithm compared with other baseline resource allocation schemes, with the maximize improvement being 53%.

Improving the Performance of Delay-sensitive Multimedia Computation in Automotive Networks with DP-ERACOM using CloudSim Simulator

Abstract: We propose a Dynamic Priority-based Efficient Resource Allocation and Computing (DP-ERACOM) scheme for processing delay-sensitive multimedia computation in automotive networks. The proposed scheme breaks down each multimedia task into four smaller tasks and dynamically distributes resources based on the priority of the multimedia tasks. The three main processing and computing units in the proposed scheme are the load manager, compute cluster unit, and transmission unit. We evaluate the performance of the proposed scheme using CloudSim simulator. The results show that the proposed scheme can significantly improve the performance of delay-sensitive multimedia computation in automotive networks.

DRL-Driven Dynamic Resource Allocation for Task-Oriented Semantic Communication

Semantic communication has been regarded as a promising technology to serve upcoming intelligent applications. However, few studies have addressed the problem of resource allocation in semantic communication networks. Most resource allocation mechanisms act fairly to all original data, ignoring the meaning behind the transmitted bits. In this paper, a dynamic resource allocation scheme for the task-oriented semantic communication network (TOSCN) based on deep reinforcement learning (DRL) is proposed, which allows data with richer semantic information to preferentially occupy limited communication resources. This paper aims to design a deep deterministic policy gradient (DDPG) agent at the micro base station to maximize the long-term transmission efficiency of tasks. Firstly, the relationship between semantic information and task performance is investigated. Subsequently, a novel wireless resource allocation model for TOSCN is proposed by taking the image classification task as an example. Then, a joint optimization problem of the semantic compression ratio, transmit power, and bandwidth of each user is formulated. The agent is trained in an interactive learning environment to obtain a decent trade-off between the amount of data delivered to the receiver and the accuracy of intelligent tasks. Simulation results demonstrate that the proposed scheme achieves significant advantages in relieving communication pressure and improving task performance in resource-constrained wireless networks.

JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network

Providing robust communication services to mobile users (MUs) is a challenging task due to the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) are used to improve connectivity by allocating resources to MUs more efficiently in a dynamic environment. However, energy consumption and lifetime issues in UAVs severely limit the resources and communication services. In this paper, we propose a dynamic cooperative resource allocation scheme for MEC–UAV-enabled wireless networks called joint optimization of trajectory, altitude, delay, and power (JO-TADP) using anarchic federated learning (AFL) and other learning algorithms to enhance data rate, use rate, and resource allocation efficiency. Initially, the MEC–UAVs are optimally positioned based on the MU density using the beluga whale optimization (BLWO) algorithm. Optimal clustering is performed in terms of splitting and merging using the triple-mode density peak clustering (TM-DPC) algorithm based on user mobility. Moreover, the trajectory, altitude, and hovering time of MEC–UAVs are predicted and optimized using the self-simulated inner attention long short-term memory (SSIA-LSTM) algorithm. Finally, the MUs and MEC–UAVs play auction games based on the classified requests, using an AFL-based cross-scale attention feature pyramid network (CSAFPN) and enhanced deep Q-learning (EDQN) algorithms for dynamic resource allocation. To validate the proposed approach, our system model has been simulated in Network Simulator 3.26 (NS-3.26). The results demonstrate that the proposed work outperforms the existing works in terms of connectivity, energy efficiency, resource allocation, and data rate.

Lyapunov-Based Optimization of Edge Resources for Energy-Efficient Adaptive Federated Learning

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient adaptive federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient-based algorithms to perform continuous learning and adaptation. Hinging on Lyapunov stochastic optimization tools, we dynamically optimize radio parameters (e.g., set of transmitting devices, transmit powers, bits, and rates) and computation resources (e.g., CPU cycles at devices and at server) in order to strike the best trade-off between power, latency, and performance of the federated learning task. The framework admits both a model-based implementation, where the learning performance metrics are available in closed-form, and a data-driven approach, which works with online estimates of the learning performance of interest. The method is then customized to the case of federated least mean squares (LMS) estimation, and federated training of deep convolutional neural networks. Numerical results illustrate the effectiveness of our strategy to perform energy-efficient, low-latency, adaptive federated learning at the wireless network edge.

Dynamic Resource Allocation Using an Adaptive Multi-Objective Teaching-Learning Based Optimization Algorithm in Cloud

Resource allocation is a non-polynomial complete problem in the cloud data center that selects the proper resources to execute many fine computational granularity tasks. Customer requirements and capacity of applications change frequently. To bridge the gap between frequently changing customer requirement and available infrastructure for the services, we propose a dynamic resource allocation strategy using an adaptive multi-objective teaching-learning based optimization (AMO-TLBO) algorithm in Cloud computing. To improve the exploration and exploitation capacities, AMO-TLBO introduces the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. Moreover, a grid-based approach to adaptively assess the non-dominated solutions maintained in an external archive is used. The objectives of AMO-TLBO include minimizing makespan, cost and maximizing utilization using well-balanced load across virtual machines. The evaluation results show that the proposed algorithm outperforms TLBO, MOPSO and NSGA-II algorithms in terms of different performance metrics.

Dynamic Offloading Strategy for Delay-Sensitive Task in Mobile-Edge Computing Networks

Mobile-edge computing (MEC) technology offers computing resources for mobile devices to conduct computationally heavy activities by putting servers at the wireless mobile network’s edge. This mitigates the scarcity of computing resources in mobile devices and enhances the intelligence of the Internet of Things (IoT), which is a crucial technology for achieving industrial digitalization. Considering the time-varying channel as well as the time-varying available computing resources of MEC servers, this article formulates a hybrid optimization problem that combines task offload and resource allocation. The goal is to minimize MEC servers’ overall power consumption. Since the channel state information (CSI) stored in the MEC system is not real time, we propose a reinforcement learning (RL) algorithm for predicting current CSI from historical CSI and obtain the optimal strategy for task offloading. On the other hand, convex optimization methods are used to accomplish the dynamic resource allocation strategy. In addition, an approach based on deep RL (DRL) is put forward to overcome the dimensionality curse in RL algorithms. The simulation experiments illustrate that the proposed algorithms outperform the nonpredictive schemes by a large margin, and their performance is close to that of the optimum scheme, which utilizes simultaneous CSI.

Dynamic Resource Allocation Strategy of Multi-Objective Fuzzy Optimization Based on Markov Decision Process

Dynamic Resource Allocation Strategy of Multi-Objective Fuzzy Optimization Based on Markov Decision Process

Performance Analysis of Directional Ultra-Dense Networks with Dynamic Spectrum Partition Strategy

Intercell interference coordination (ICIC) plays a significant role in strengthening ultra-dense network (UDN) downlink coverage. From a statistical average perspective, a user is primarily interfered by its adjacent base station (BS), especially the second nearest BS. By modeling BSs equipped with directional antennas as a Poisson point process (PPP), this paper proposes a dynamic spectrum resource allocation strategy mainly about users’ service BS and its nearest interference BS, where the subchannel assigned by the typical (served) user is interlaced from the channel simultaneously occupied by users within the effective radiation range of its second nearest BS. To fully explore this scheme for directional networks, we develop analytical expressions in terms of success probability and ergodic rate for the typical user based on the techniques of stochastic geometry, taking into account the fading of directional BS radiation angle. Then, we derive the meta distribution of the signal-to-interference ratio (SIR) for capturing individual link performance changes of users. Simulations verify the correctness of numerical results, and it is revealed that this strategy is in favor of users alleviating interference from their second nearest BSs and the performance advantages of the proposed ICIC strategy are better than those of the traditional directional UDNs.

Sequential dynamic resource allocation in multi-beam satellite systems: A learning-based optimization method

Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources. One of the primary challenges accompanying with Multi-Beam Satellites (MBS) is an efficient Dynamic Resource Allocation (DRA) strategy. This paper presents a learning-based Hybrid-Action Deep Q-Network (HADQN) algorithm to address the sequential decision-making optimization problem in DRA. By using a parameterized hybrid action space, HADQN makes it possible to schedule the beam pattern and allocate transmitter power more flexibly. To pursue multiple long-term QoS requirements, HADQN adopts a multi-objective optimization method to decrease system transmission delay, loss ratio of data packets and power consumption load simultaneously. Experimental results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.

Resource Allocation and Slicing Puncture in Cellular Networks With eMBB and URLLC Terminals Coexistence

Ultrareliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) are two types of services with delay Quality-of-Service (QoS) demands. Considering the random and sporadic URLLC packets arrival feature, slicing puncture is believed to be the suitable method to support coexistence communication scenario of eMBB and URLLC terminals. However, slicing puncture in a short time is not trivial, and needs accurate wireless resource scheduling. At the same time, it may damage the QoS of eMBB service. All of these make the QoS guaranteed scheduling problem more challenging. In this article, we investigate the bandwidth, power allocations, slice puncturing problems to find the way satisfying services’ individual QoS demands. For the scheduling of eMBB service, we formulate a joint optimization problem for bandwidth and power allocations with long-term constraints of queues backlog. To solve this problem, we utilize the Lyapunov drift-plus-penalty (DPP) method to establish the relationship between the long-term constraints and the short-term optimization problem, which means that the long-term constraints are gradually satisfied by the proposed strategy at each step. We further divide the short-term optimization problem into two subproblems and adopt a block coordinate descent (BCD) algorithm to reduce the computation complexity. Then, we put forward a one-to-one matching method to solve the integer programming in resource block (RB) allocation and slicing puncture problems. Numerical results demonstrate that the proposed dynamic resource allocation and puncturing strategy (DRAPS) can solve the scheduling problem of eMBB and URLLC services in the presence of multiple randomness of channel-state information (CSI) and URLLC packet arrival.

Learning large-scale fuzzy cognitive maps under limited resources

Research on the problem of learning large-scale fuzzy cognitive maps (FCMs) with a limited computational budget is outstanding. To learn large-scale FCMs from time series, in most work, this problem is decomposed into learning local connections of each concept, respectively, and then one optimizer is employed to optimize each such sub-problem. Each sub-problem may have different requirements for the computational resource, but the existing methods ignore this issue and allocate the same amounts of computational resources for each sub-problem. In this paper, we propose two strategies to address this problem. We first develop a dynamic resource allocation strategy to maximize the performance of the decomposition-based optimizer under a limited computational budget. Second, we propose a half-thresholding memetic algorithm to improve the performance of the traditional evolutionary algorithm. We term our proposal as a half-thresholding memetic algorithm with a dynamic resource allocation strategy (HTMA-DRA). Finally, the experiments on large-scale synthetic data and DREAM datasets compared with the existing state-of-the-art methods demonstrate the effectiveness of the proposed HTMA-DRA.

A dynamic resource allocation strategy for collaborative constrained multi-objective optimization algorithm

A dynamic resource allocation strategy for collaborative constrained multi-objective optimization algorithm

PDRAS: Priority-Based Dynamic Resource Allocation Scheme in 5G Network Slicing

PDRAS: Priority-Based Dynamic Resource Allocation Scheme in 5G Network Slicing