Adaptive Resource Allocation Algorithm Research Articles

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.

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

Adaptive Time Slot Resource Allocation in SWIPT IoT Networks

The rapid advancement of Internet of Things (IoT) technology has brought convenience to people’s lives; however further development of IoT faces serious challenges, such as limited energy and shortage of network spectrum resources. To address the above challenges, this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation (SIATS) algorithm. First, an adaptive time slot consisting of periods for sensing, information transmission, and energy harvesting is designed to ensure that the minimum energy harvesting requirement is met while the maximum uplink and downlink throughputs are obtained. Second, the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods, and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is maximized. Simulation results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4% compared with that of the fixed time slot allocation (FTS) algorithm. In the case of a large noise variance, the SIATS algorithm has good noise immunity, and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7% compared with that obtained using the FTS algorithm.

Improving video conference security in education

In article, which aims to apply communication network security to improve the transmission quality of an educational video conference, an adaptive resource allocation algorithm is used to optimize the transmission quality over the network. The application of the protocol to improve the transmission quality of a video conference can increase the transmission speed, reduce the bit error rate. The algorithm minimizes latency using ant colony optimization and clustering, which ultimately achieves the goal of reducing power consumption and improving security. In order to improve system performance, the problem of power distribution must be taken into account when improving the transmission quality of an educational video conference. By analyzing the adaptive resource allocation mechanism applicable to network security while improving the transmission quality of an educational video conference, conditions were created for optimizing resource allocation.

Adaptive resource management for cognitive power line communication system

The authors investigate the adaptive resource allocation problem for cognitive power line communication system. The dynamic optimisation model is employed to maximise the energy-aware network utility, which is defined as the difference of the weighted network throughput and the power consumption. Then, the authors decompose the stochastic optimisation into a traffic management subproblem and a joint subchannel and power allocation subproblem. Since the joint subchannel and power allocation subproblem is a mixed integer non-linear optimisation, a continuity relaxation and Lagrange dual method is proposed to obtain the optimal solution of the mixed integer non-linear optimisation subproblem. After that, the authors develop an adaptive power line communication resource allocation algorithm to cope with the power line communication network dynamics only according to the current network state information. Finally, we derive a trade-off mechanism between network utility and network delay, in which the network delay is proportional to the control parameter V and the network utility is proportional to the parameter 1 / V $1/V$ . Simulation results not only show the effectiveness but also verify the theoretical analysis of our proposed algorithm.

Application of adaptive resource allocation algorithm and communication network security in improving educational video transmission quality

With the rapid development of network multimedia technology, video communication technology has made great progress and has been applied in many fields. Educational video communication is an intuitive, timely, efficient, and remote communication tool. Video communication technology extends to educational and management. It includes interactive distance learning, distance course observation, distance conference, and training, which has caused a profound revolution in the education model and actively promoted the development of the education industry. This paper proposes the application of adaptive resource allocation algorithm and communication network security in improving the quality of educational video transmission. This article takes “Internet +” as the background and features media integration as the society enters the era of all-media. The development of education video dissemination in the era of all-media is of great significance. The content and expression of education video dissemination as a new media is mainly for youth. The audience's thinking will have a profound impact, and mainstream values will play a positive role in the physical and mental growth of young people. Therefore, we should correctly understand the application of video communication in the education industry, so that the technology can have fully applied, and a new era of education mode is opened.

Long-Term Max-Min Fairness Guarantee Mechanism for Integrated Multi-RAT and MEC Networks

Recently, mobile edge computing (MEC) has been recognized as an emerging paradigm to meet the ever-increasing computation demands. Besides, multiple radio access technology (multi-RAT) has been proposed to enhance the network throughput and service reliability. However, the two technologies have been evolving independently. In this paper, we will develop an integrated multi-RAT and MEC network framework, which enables smart devices (SDs) to offload computation tasks over multiple RAT links in parallel. Considering the resource-limited nature as well as the time-varying property of the proposed network, we then focus on investigating long-term fairness guarantee mechanisms to facilitate fair resource sharing/allocation between SDs. Specifically, we formulate a max-min stochastic optimization problem with the objective of maximizing the minimum long-term time-average offloading utility. An adaptive task splitting and resource allocation algorithm is proposed based on the Lyapunov optimization technology, which jointly optimizes the SD task splitting and uplink transmit power allocation, the RAT subcarrier allocation, and the MEC computation frequency allocation. The adaptive algorithm can accommodate to the time-varying network dynamics without requiring their distribution information. Moreover, the proposed algorithm is shown to have polynomial computation complexity and be asymptotically optimal by rigorous analysis. Simulation results verify the theoretical analysis and show that the Jain's fairness index of the proposed algorithm converges to 1 as the number of time-slots grows, which outperforms the benchmark algorithms.

Adaptive Price Estimation in Cognitive Radio Enabled Smart Grid Networks

This paper focuses on the allocation of resources to cognitive users in a two-way cognitive radio facilitated smart grid (SG) network. The resources are essential in controlling the performance of demand response management in the SG, ensuring profit to the power supplier and simultaneously cost-saving to the consumers. However, cognitive users need more power for their data transmission, which compels the utility company to increase its electricity price. Hence, we propose an adaptive resource allocation algorithm based on normalized least mean squares (NLMS) to estimate the electricity price that benefits both the supplier and consumers with optimal allocation of power demands under the constraints of transmission power, system throughput, and the probability of detection. The simulation results validate the performance of our proposed scheme by comparing it with the application of metaheuristic algorithms for maximizing aggregate profit. The impact of the channel parameters on system performance is also studied.

An adaptive multi-sensor resource allocation algorithm based on efficiency function

In this paper, combined with the Kalman filtering algorithm, the linear weighted sum of the normalized prediction information increment DI and the norm of target filter error covariance matrix ║ cP ║ is used to represent the pairing function of the target-sensor combination and the assignment and automatic updating of the pairing function between sensor and target are realized. Based on the analytic hierarchy process (AHP), an adaptive sensor resource allocation algorithm is proposed. The simulation results show that the algorithm can effectively integrate various evaluation factors and is compatible with different types of targets. The algorithm has simple structure and good versatility. The limited sensor resources can be dynamically adjusted according to the priority of targets, making the sensor allocation strategy more reasonable and effective.

Resource scheduling strategy for uncertain factors in mobile cloud computing

At present, resource configuration of mobile cloud computing has received extensive attention from the outside world. Most of the similar resource scheduling configuration fails to comprehensively consider the dynamics of mobile terminals and the difference in user requested resources. Therefore, considering uncertainty in paging scheduling under mobile cloud resource environment from the perspective of consumers has become the key to solving the problem of resource allocation in the mobile cloud computing environment. This paper proposes an adaptive matching resource allocation algorithm based on uncertain factors under mobile cloud computing environment. Uncertain factors of the mobile terminal are derived via QoS attribute, and then user information and load characteristics of the user requested resources are analyzed through CLIQUE similarity matching. Afterwards, based on the mapping between similarity and resources, resource paging allocation can be carried out based on adaptive matching resource allocation algorithm. From the perspective of consumers, dynamics of mobile terminals and uncertainty of paging scheduling in the mobile cloud resource environment under different user requested resources can be considered to allow minimized delay and optimized paging strategies.

Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

With the popularity of smart mobile equipment, the amount of data requested by users is growing rapidly. The traditional centralized processing method represented by the cloud computing model can no longer satisfy the effective processing of large amounts of data. Therefore, the mobile edge computing (MEC) is used as a new computing model to process the big growing data, which can better meet the service requirements. Similar to the task scheduling problem in cloud computing, an important issue in the MEC environment is task offloading and resource allocation. In this paper, we propose an adaptive task offloading and resource allocation algorithm in the MEC environment. The proposed algorithm uses the deep reinforcement learning (DRL) method to determine whether the task needs to be offloaded and allocates computing resources for the task. We simulate the generation of tasks in the form of Poisson distribution, and all tasks are submitted to be processed in the form of task flow. Besides, we consider the mobility of mobile user equipment (UE) between base stations (BSs), which is closer to the actual application environment. The DRL method is used to select the suitable computing node for each task according to the optimization objective, and the optimal strategy for solving the objective problem is learned in the algorithm training process. Compared with other comparison algorithms in different MEC environments, our proposed algorithm has the best performance in reducing the task average response time and the total system energy consumption, improving the system utility, which meets the profits of users and service providers.

Deep learning and hierarchical graph-assisted crosstalk-aware fragmentation avoidance strategy in space division multiplexing elastic optical networks.

Space division multiplexing elastic optical networks (SDM-EONs) with multi-core fiber (MCF) are the promising candidate for future optical networks due to their high transmission capacity and high flexibility. However, the inherent defects of inter-core crosstalk and spectrum fragmentation may have some negative impact on the performance of SDM-EONs. A deep learning and hierarchical graph-assisted crosstalk-aware fragmentation avoidance (DLHGA) strategy is proposed in this paper. Firstly, we introduce a deep learning (DL) model to predict future requests, so as to achieve reasonable scheduling of resource in advance. Then, considering the inter-core crosstalk of MCF, we abstract the core, spectrum and time resource as a three-dimensional (3D) model with the hierarchical graphs. Therefore, the resource allocation process is simplified to be mitigating the crosstalk and fragmentation from the perspective of inter-core and intra-core, respectively. Finally, based on DL traffic prediction and different characteristics of hierarchical graph, we present an adaptive resource allocation algorithm considering relieving core adjacency and downgrading modulation format to achieve efficient resource scheduling. We evaluate our DLHGA strategy in different topologies, and the results show that our strategy can efficiently improve network performance in terms to inter-core crosstalk and bandwidth blocking probability compared to earlier approaches.

Adaptive resources allocation algorithm based on modified PSO for cognitive radio system

Radio spectrum has become a rare resource due to the rapid development of wireless communication technique. Cognitive radio is one of important techniques to deal with this radio spectrum problem. But the resource allocation in cognitive radio also has its own issues, such as the flexibility of the allocation algorithm, the performance of resource allocation, and so on. In order to increase the flexibility of the allocation algorithm for cognitive radio, more and more researches are focusing on the evolutionary algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO). Evolutionary algorithm can greatly improve the flexibility of the allocation algorithm for cognitive radio system in different communication scenarios, but the performances are relatively lower than the original mathematical methods. So in this paper, we proposed an adaptive resource allocation algorithm based on modified PSO for cognitive radio system to solve these problems. Modified particle swarm optimization (Modified PSO) has both genetic algorithm (GA) and particle swarm optimization (PSO)'s updating processes which makes this modified PSO overcame PSO's own disadvantages and keep advantages. Simulation results showed our proposed algorithm has enough flexibility to meet cognitive radio systems' requirements, and also has a better performance than original PSO.

Adaptive resource allocation based on the billing granularity in edge-cloud architecture

With the rapid development and popularization of the Internet of Things technology, the number of network edge devices has increased rapidly, and the Internet of Things perception layer has generated massive data. Cloud-Edge collaboration will be used more and more to solve this problem. When the business reaches its peak, cloud computing capabilities cannot meet business needs. The cloud service provider can apply for resources to meet the requirements of the computing resources. After the peak period of the business, if there are idle computing resources, the resources can be released to the cloud service provider, which can reduce the service cost and save the computing resources. The traditional flexible resource management is mainly used in a single cloud environment, and the prediction of the resource demand is insufficient. The factor of the cloud resource billing granularity is neglected, resulting in high cloud lease cost. Therefore, an adaptive resource allocation algorithm and a data migration algorithm are proposed. The prediction algorithm provides the basis for the adaptive resource allocation of the edge cloud cluster. The adaptive resource allocation algorithm determines the resource allocation scheme of the edge cloud cluster with the lowest service cost. The data migration algorithm guarantees the reliability of data and achieves cluster load balancing. A large number of experimental results show that our newly proposed algorithm can greatly improve system performance in terms of better cost control, higher data integrity and load balancing.

ARMA-Prediction-Based Online Adaptive Dynamic Resource Allocation in Wireless Virtualized Network

Wireless network virtualization (WNV) provides a novel paradigm shift in the fifth-generation (5G) system, which enables to utilize network resources more efficiently. In this paper, by jointly considering cache space and time-frequency resource allocation in wireless virtualized networks, we first formulate an optimization programming to investigate the minimization problem of network overheads while satisfying the quality of service (QoS) requirements of each virtual network on overflow probability. Then, with diverse demands of virtual networks for different kinds of resources taken into consideration, an online adaptive virtual resource allocation algorithm with multiple time-scales based on auto regressive moving average (ARMA) prediction method is proposed to solve the formulation, which could eliminate the irrationalities existed in traditional approaches caused by the uncertainty of traffic and information feedback delay. More specifically, in the proposed resource scheduling mechanism with multiple time-scales, on the one hand, a reservation strategy of cache space is developed according to the ARMA prediction information under long time-scales. On the other hand, virtual networks are sorted by the overflow probabilities derived by the large-deviation principle and dynamic time-frequency resource scheduling under short time-scales. Simulation results reveal that our proposal can provide tangible gains in reducing the bit loss rate and improving the utilization of physical resources.

Adaptive Virtual Resource Allocation in 5G Network Slicing Using Constrained Markov Decision Process

Network virtualization technology is generally envisaged as a promising technology to consequently satisfy various types of service requirements. On the other hand, non-orthogonal multiple access (NOMA) technology has the potential to significantly increase the spectral efficiency of the system. However, previous works that jointly address these two issues have not considered the dynamic resource allocation issue in this context. In this paper, we propose a slice-based virtual resources scheduling scheme with NOMA technology to enhance the quality-of-service (QoS) of the system. We formulate the power granularity allocation and subcarrier allocation strategies into a constrained Markov decision process problem, aiming at the maximization of the total user rate. In order to further avoid the curse of dimensionality and the expectation calculation in the optimal value function, we develop an adaptive resource allocation algorithm based on approximate dynamic programming to solve the problem. Extensive simulation works have been conducted under various system settings, and the results demonstrate that the proposed algorithm can significantly reduce the outage probability and increase the user data rate.

Intelligent resource allocation scheme for the cognitive radio network in the presence of primary user emulation attack

An energy-efficient cognitive radio network (CRN) design is one of the primary requirements for the low-battery-driven wireless terminals in the presence of primary user emulation attack. Furthermore, sensing accuracy is also essential for allocating vacant bands to the secondary users (SUs) for data transmission. Hence, this study focuses on designing an energy-efficient double threshold-based CRN. The adversarial effects arising in the presence of the attacker are analysed and are treated as constraints while formulating the energy efficiency (EE) maximisation problem. To develop the formulation, a unique SU selection algorithm to identify most eligible SUs for data transmission is proposed. The EE is then maximised by minimising the total power consumption through novel adaptive resource allocation algorithm. Hence, the authors proposed approaches are based on the suitable selection of SUs and adaptive power allocation under the constraints of maximum throughput, controlled transmission power providing sufficient protection to the primary user, minimum power consumption and minimum false alarm probability. The extensive simulation results demonstrate that the proposed scheme substantially increases EE with lower complexity over the conventional scheme.

Adaptive Resource Allocation Algorithm Based on Minimize Average Bit-Error-Rate for OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier modulation method for wireless systems. However, the OFDM communication system is sensitive to inter-carrier interference (ICI), which arises because of Doppler spread and carrier frequency offset. This study addresses the problem of resource management in the presence of ICI and multipath fading channels. This study developed a minimized average bit error rate-based method that adaptively allocates the subcarrier, bit, and power in OFDM systems to obtain minimum total transmission power under certain quality of service requirements (i.e., the minimum data rate and the bit error rate). The simulation results demonstrated that the proposed suboptimal algorithms can substantially improve the performance of the OFDM systems compared with the uniform power allocation algorithm or conventional water-filling algorithm. Furthermore, applying the proposed resource allocation algorithm was demonstrated to distribute the sum capacity more fairly and flexibly among users than the sum capacity maximization method did.

Adaptive Resource Allocation Algorithm in Wireless Access Network

Wireless network state varies with the surrounding environment, however, the existing resource allocation algorithm cannot adapt to the varying network state, which results to the underutilization of frequency and power resource. Therefore, in this paper, we propose an adaptive resource allocation algorithm which can efficiently adapt to the varying network state by building an optimal mathematical model and then changing the weighted value of the objective function. Furthermore, the optimal allocation of subcarrier and power is derived by using the Lagrange dual decomposition and the subgradient method. Simulation results show that the proposed algorithm can adaptively allocate the resource to the users according to the varying user density which represents the network state.

Adaptive Multi-Homing Resource Allocation for Time-Varying Heterogeneous Wireless Networks Without Timescale Separation

In this paper, we design an adaptive multi-homing resource allocation algorithm for time-varying heterogeneous wireless networks (HetNet), where the algorithm iteration timescale is the same to the network state acquisition timescale. First, the network utility maximization is characterized by a stochastic optimization model. Second, the multi-homing resource allocation (MHRA) algorithm is developed to accommodate the dynamic wireless network states, i.e., time-varying wireless channels between the access points (AP) and mobile terminals and as well the queuing dynamics at the APs. Then, we investigate the tracking error between the MHRA algorithm output and the target optimal resource allocation solution. Based on these results, an adaptive-compensation multi-homing resource allocation (AMRA) algorithm is proposed to offset the tracking error so as to enhance the network utility. Specifically, we give a sufficient condition that the AMRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Finally, we derive a tradeoff between network utility and media transmission delay, where the increase of average delay is approximately linear in $V$ and the increase of network utility is at the speed of $1/V$ with the control parameter $V$ . Simulation results validate the theoretical analysis of our proposed scheme.