Embedding Security Awareness for Virtual Resource Allocation in 5G Hetnets Using Reinforcement Learning

Efficient allocation of achievable virtual resources to the diverse users is a key challenging issue in a controlled and collaborative cloud environment. As well, balancing the load among the resources and mapping these virtual resources to the physical machines is even bigger challenge of present distributed computing arena. Numerous approaches were introduced by different researchers including Genetic Algorithm for dealing with these challenges, but their scope was limited to certain specific performance elements. Hence, there is a need of optimizing the existing research implementations for efficient allocation of virtualized resources in cloud computing environment. Usually, in a typical distributed computing environment like cloud computing, allocation of virtual resource and balancing of workload among them is realized by means of virtual machines live migration. This article introduces an optimization of existing Genetic algorithm (GA) that mainly intended for VM resource provisioning and load balancing. The proposed OGA_EAVRC considers Population size, Fitness function, Mutation probability, and success rate of resource for optimizing the performance through efficient resource allocation. Key objective of this work is to utilize each physical resource effectively and allocated them to end users efficiently. For studying the operational performance of OGA_EAVRC, an event based CloudSim was chosen. Simulation results states that the proposed OGA_EAVRC can efficiently allocates the workload among virtualized resource by reducing VM's migration among the physical machines.

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

In the network slicing environment, how to improve the success rate of virtual network resource allocation has become an urgent problem to be solved. To solve this problem, this paper proposes a virtual network resource allocation algorithm based on the coefficient of variation and the order relationship under network slicing. The algorithm includes four steps: sorting the importance of the attributes of the underlying nodes, sorting the importance of the attributes of the virtual nodes, calculating the comprehensive attribute values of the underlying nodes and sorting them in descending order, fetching virtual network requests one by one and performing resource allocation. To improve the success rate of resource allocation, first allocate resources for important virtual nodes. When calculating the importance of a virtual node, first calculate the attribute value of the virtual node. After the attribute value is normalized by the min-max method, the comprehensive attribute value of each virtual node is calculated according to the weight and arranged in descending order. When allocating resources to virtual nodes one by one, nodes with large attribute values are preferentially allocated. In the performance analysis part, the algorithm of this paper is compared with the traditional algorithm, and it is verified that the algorithm of this paper has achieved good results in the two dimensions of the success rate of virtual network resource allocation and the utilization of underlying network resources.KeywordsNetwork slicingResource allocationCoefficient of variationOrder relationship

In recent cloud computing research, the allocation of virtual resources has become a major concern. The Virtual Machine (VM) dynamic deployment technique is one of the key virtual resource management techniques. Virtual resource management predicts the resources on the virtual machine and manages accordingly. The study of the Virtual Machine resource dynamic deployment focuses on the fine-grained resource adjustment strategy. So, there is a need for predicting the usage of resources for the virtual machine to avoid delays, overprovisioning and under provisioning. Time series forecasting models are used because predictions of cloud resources are dependent on time. In this paper, different time series models and ensemble techniques have been fitted to predict resource usage for successive hours. Different time series analysis techniques were implemented to study the dataset and to choose the appropriate model. Various types of resources such as CPU usage, disk read times, disk write times and memory usage are predicted using the above models.

Virtual code resource allocation for energy-aware MTC access over 5G systems

As a long term solution to the gradual Internet ossification problem, virtual networks can guarantee on-demand requirements of diverse end user on a global level. Clearly it is a major challenge to achieve the automation and optimization of network management like virtual resources or QoS to ensure the personalized user satisfaction. In this article, we propose a novel cognitive management architecture to meet the future needs under a heterogeneous network environment. The architecture introduces a cognitive management cycle to the virtual network resources to ensure flexibility. We also focus on the efficient virtual network resource allocation and scheduling algorithms. Based on the complex network centrality analysis, we solve the virtual network resources allocation problem with consideration of the topology structure and statistical analysis. Finally we evaluate the efficiency and effectiveness of our algorithm on common benchmarks including the acceptance, revenue and cost.

In the context of 5G network slicing, in order to solve the problem of low service reliability, a high-reliability virtual network resource allocation algorithm based on service priority is proposed in this paper. The algorithm includes four steps: priority ranking of virtual network request and virtual nodes, reliability ranking of underlying nodes, resource allocation for virtual nodes, and resource allocation for virtual links. The priority ranking of virtual network request and virtual nodes mainly include two processes of ordering the virtual network based on service priority and ordering the virtual node based on centrality. In the step of allocating resources to virtual nodes, the main work is to allocate resources to virtual nodes based on the resources and reliability of the underlying nodes. In the simulation experiment, the algorithm in this paper is compared with the traditional algorithm, which verifies that the algorithm in this paper has achieved good results in terms of key business reliability indicators.

In order to solve the problem of cross-domain resource allocation in IoT in Smart Grid, this paper proposes a virtual network resource allocation mechanism based on particle swarm algorithm. Its goal is to minimize mapping overhead under planning request. In this paper, VN request is divided into multiple virtual subnets according to matching set of virtual resource matching phase, virtual library resource type price information and border node information. We also propose a cross-domain virtual network mapping algorithm based on particle swarm optimization. It can be used to improve the efficiency of cross-domain virtual network mapping. Finally, the execution time, mapping cost and performance of the algorithm in different environments are tested by simulation experiment, which verifies its efficiency and stability performance in virtual network partitioning.

An online auction mechanism for cloud computing resource allocation and pricing based on user evaluation and cost

With the rapid increase of smart grid's intelligence degree, types and number of communication services are also increasing. It will be a challenge to design a resource allocation method meeting various QoS requirements and effectively using substrate resources. In this paper, a cost-and-QoS-based resource allocation mechanism is proposed to solve wireless resource slicing problem in access network. It virtualizes substrate wireless resources and builds a combined and non-convex optimization model, then uses simulated annealing optimization algorithm (SAOA) to complete allocation of virtual wireless resources. Simulation results show that compared with QoS-based wireless resource allocation algorithm (QoS-WRA) and wireless resource dynamic allocation algorithm (WRDA), SAOA has stronger advantages in saving energy consumption and ensuring transmission rate.

Edge Computing (EC) has been proposed as a promising approach to fulfil the requirements of high bandwidth and ultra-latency of mobile applications. However, existing researches on resource allocation only consider the computing resource of mobile devices and EC servers, while ignored the constraint of the networking resources once spreading applications among multiple EC servers via wireless and wired networks. Fibre-Wireless access networks (FiWi) combine the huge bandwidth of optical fibre networks and flexible access of wireless networks to address the above issue and bridge the coexistence of multiple EC servers. They propose a Virtualisation-based Architecture converging EC over FiWi (VAECFW) to centralise control and allocate networking and computing resources for serving requested services. In addition, they study the problem of resource allocation of EC over FiWi and propose two algorithms Revenue-based Virtual Network Embedding (R-VNE) and Balanced Central Processing Unit Resource Allocation with Virtual Network Embedding (BCRA-VNE). Simulation experiments show that the services acceptance ratio is increased about 35% under their proposed architecture, and the average service requests bandwidth utilisation of R-VNE is increasing from 50 to 66%, and the BCRA-VNE is from 37 to 48%. The two algorithms not only achieve higher revenue but also get better profit rate.

Cloud computing(CC) delivers multiple services to users by processing complex tasks through internet connections to serve as a dynamic, dependable, and flexible computing solution. With the increased Internet speed, the user demands to perform many tasks through computing provided by cloud virtual machines (VMs) created in heterogeneous servers. The complexity of cloud computing(CC) increases through the diversity of servers and the increased demand for resources to perform more tasks, which makes the cloud data center(CDC) unbalanced in load. Therefore, future cloud systems will require more effective resource management(RM) methods to balance load and improve Quality of Service (QoS). This paper proposes KMPPVM-DRM, a dynamic clustering and scheduling approach for VM resource allocation(RA) based on the KMeans++ algorithm. VMs are clustered into three groups (high, medium, low) according to their real-time normalized CPU and RAM weights, using weighting parameters α = 0.6 and β = 0.4. Tasks are categorized into three levels based on length and mapped to appropriate VM clusters. Within each cluster, a Utilization-Level Comparator (ULC) dynamically selects the optimal VM for task execution. The experimental results were conducted using CloudSim, with 5 and 10 VMs and task counts ranging from 1000 to 10000. The proposed model was compared with PSO, ACO, and DBSCAN algorithms using execution time, average start time, and average finish time. Results show that with 10 VMs, the proposed model achieved a harmonic mean execution time of 886.72 ms, compared to 4102.99 ms (PSO), 4672.25 ms (ACO), and 3071.51 ms (DBSCAN). It also attained the lowest harmonic mean start and finish times of 166.41 ms and 167.19 ms, respectively. Relative reduction in execution time against DBSCAN ranged from 69.90% to 71.52%, with start and finish time improvements between 52.65% and 81.51%.To ensure statistical reliability, a paired t-test confirmed that the performance improvements were statistically significant (p < 0.05) across all task sizes. The findings confirm that KMPPVM-DRM enhances resource allocation(RA) efficiency and scheduling effectiveness, maintaining balanced loads even with limited resources (e.g., only 5 VMs) and outperforming PSO, ACO, and DBSCAN in all tested scenarios.

Cloud computing is a revolutionary paradigm that encompasses high connectivity, elasticity, measurable, and easily managed services. These attributes help boost technology and part of its efficiency is based on the allocation of virtual resources and the allocation of services. Due to the high demand for data processing in web applications in recent years, there has been a greater supply of computational power by service providers and as a result of this, it is possible to meet a varied number of demands and customers. Because of this, the modeling of cloud architecture must comprise from the survey of tasks, specification of the modules, and functions of each component of the architecture. We present MoHRiPA, a prototype that aims to manage computing resources and fulfill requests in a private cloud. Thus, the aim of the project was to develop a distributed and service-oriented cloud architecture that has hybrid functions with independent modules that perform specific functions such as resource allocation, service assignment, monitoring, and mapping of the infrastructure. The prototype presents a differential in relation to works in the literature that do not adequately address the mechanisms of performance evaluation, which are fundamental to establish the strengths and weaknesses of architecture. The method used for the research comprises the modeling of the architecture, the specification of the components, as well as an emphasis on modules that have hybrid properties for supporting the use of different virtualization and monitoring tools. The architecture solution presented, allows to evaluate under the aspect of performance what several works in the literature little explore: the evaluation of the general performance of the solution with formal techniques, such as the complete factorial planning that offered more accurate analyzes about the functionalities and peculiarities of the modules of architecture. In conclusion, the work modeled a cloud architecture in order to develop a prototype with components that perform the functions of cloud computing, the assignment of different service models, and the evaluation of these components as a whole. Concerning the results, experiments were modeled evaluating the allocation of virtual machines and evaluating the resource mapping algorithms that presented satisfactory results.

When 5G slice network is applied to bear intelligent substation monitoring, one can obtain qualified service quality and reduce the slice rental cost, though reasonable and customized virtual network resources allocation among time-sensitive, mobile broadband, and narrow-band sensing services. As a result, the mapping model of electrical service demand onto communication and computing resources of network slices was established, and the mechanism and transformation between them are revealed. Constrained by the transmission bandwidth, time delay and computing resource demand of the electrical multiple services, the economical efficiency optimization problem of electric 5G multi-service network slice is formulated. A joint allocation algorithm of communication and computing resources is proposed to address the issue of efficient and economic resources utilization.

This paper deals with Network Slicing-based 5G Networks to support the varying demands of customers and for efficient resource utilization. A network slice can be defined as a set of network and virtual network function (VNF) resources deployed across multiple administrative domains. Here, multi-domain refers to multiple infrastructure providers spread across different geographic regions. Slice demands and QoS requirements may vary dynamically, which can be satisfied by scaling the allotted VNF resources. The VNF scaling problem can be posed as a time series forecasting problem that predicts future VNF resources based on the slice traffic demand. 5G deployments with multiple domains pose a serious challenge in terms of data privacy since one domain may need access to the data of another domain for efficient resource allocation using the conventional forecasting approaches that requires data aggregation. In this paper, we use the federated learning approach in which the training data remains within the respective domains but learns a shared model by aggregating locally-computed updates. We evaluate the applicability of federated settings in VNF scaling using two state-of-the-art deep learning models, Long Short-Term Memory (LSTM) and Gated recurrent units (GRU). We present a comparison of the performance of the proposed federated system against the centralized system. Additionally, synthetic data in each domain has been generated using Generative Adversarial Networks (GAN) to improve the forecasting results.