Distribution Of Network Resources Research Articles

A cost and demand sensitive adjustment algorithm for service function chain in data center network

The introduction of Network Function Virtualization (NFV) and Software-Defined Network (SDN) architectures has significantly reduced the Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) of network system. However, NFV orchestration management also brings about challenges. After the initial deployment of VNFs, due to the volatility of network requests, the original deployment may not be able to meet user resource demands. The key issue is how to readjust resources dynamically to accommodate more network requests without violating Quality of Service (QoS) for users. Several existing techniques can be used to achieve this goal, such as horizontal scaling, vertical scaling, and virtual network function (VNF) migration. However, these techniques inevitably incur some overhead, such as the cost of instantiating VNF and link rerouting. Additionally, resource adjustment may also result in unbalanced distribution of network resources. In this paper, an Intelligent Service Function Chain Dynamic Adjustment Algorithm (ISFCDAA) is proposed to address the above challenges. Firstly, an Integer Linear Programming (ILP) model is established with the objective of minimizing the long-term adjustment cost and reducing the imbalance of resource distribution. Then we transform the optimization process into a Markov Decision Process (MDP). Secondly, to solve the problems that the state and action space is too large and the state transition probability is uncertain in MDP, a SFC dynamic adjustment algorithm based on deep reinforcement learning is proposed. This algorithm can obtain an approximate optimal adjustment strategy for ILP model. The simulation results show that ISFCDAA can reduce the adjustment overhead and maintain a balanced distribution of network resources while ensuring the QoS. Compared with the existing algorithms, the average standard deviation of resource distribution of ISFCDAA is reduced by up to 9.90%, the average acceptance rate of ISFCDAA is improved by up to 39.57%, and the average long-term profit is improved by up to 42.92%. The incorporation of cost and demand-sensitive considerations into ISFCDAA enhances its responsiveness to fluctuating network demands, solidifying its effectiveness in dynamic resource management scenarios.

An MFD approach to route guidance with consideration of fairness

This paper introduces two fair and efficient route guidance (RG) advisory control schemes for proactive control of a large-size urban network. The two developed fairness-centered concepts are proportional fairness and anticipatory control. These concepts are developed as distinct control frameworks to address inequity issues of earlier RG schemes in a model predictive control (MPC) scheme for a heterogeneous urban network, which is divided into multiple pockets of congestion. The modelling approach uses a macroscopic fundamental diagram (MFD), which relates aggregated traffic variables, such as vehicle accumulation and trip completion rate. The proportionally fair RG control (FC) scheme is developed as a two-level RG advisory controller that focuses on maximizing the proportion of travellers utilities to effectively prevent the network from becoming congested while also considering fairness in the distribution of network resources. In addition, an anticipatory control (AC) RG scheme is devised as a two-level optimization model by incorporating road users routing behaviour as an integral part of the control scheme. Intensive sensitivity analysis is conducted under high-demand profiles and for different compliance rates and MFD parameters to analyze and evaluate the performance of these two fairness-centered routing schemes compared to a basic MPC based control scenario. The results indicate that FC improves fairness in an urban network by increasing homogeneity while maintaining efficiency. For all examined compliance levels, even as low as 30%, a more homogenized traffic condition is achieved under FC and AC. When comparing the two schemes, AC advises a smoother RG ratios compared to FC.

Research on Resource Allocation Strategies for Wireless Powered Sensor Network

Wireless powered sensor network (WPSN) solves the problem of difficult battery replacement of sensor nodes in some environments. The unreasonable distribution of network resources in the energy transmission environment will lead to a sharp decline in the energy collected by sensor nodes, reduce the network life, and cause resource waste. In this paper, by introducing hybrid relay nodes to transmit energy to passive terminals and forward terminal information, the distance between passive terminals and final receiving nodes of information is reduced, and the contradiction between passive terminals’ data transmission requirements and “double path loss” of radio frequency energy transmission in WPSN is alleviated. At the same time, in the relay-powered WPSN, the life span of the relay node is equal to the life span of the whole network. This paper studies the energy efficiency transmission scheme of the relay node to maximize the energy utilization efficiency of the relay node and improve the life span of the WPSN.

A rule-based modeling approach for network application availability assessment under dynamic network restoration scheme

The efficient and accurate assessment method for application availability is essential for network operators to make the resilience-cost trade-off policy. However, the backup path of application is dynamically discovered upon both other application requests and global network resource distribution under network restoration scheme, makes the current analytical network availability models difficult for assessing application availability effectively in large-scale network scenarios. This paper proposed a rule-based modeling approach to capture application resilience behavior and measure the uncertainties during dynamic restoration process. Firstly, we propose an evolution object formalism for modeling the attributes of both logical application and network infrastructure. Secondly, the network evolution condition model is proposed to describe the failure modes of individual components. Finally, the network evolution rule is proposed, which utilizes general graph algorithm and resource match function as local rules to determine application restoration resource contention outcomes during the state transition. Through simulations, the proposed approach evaluates application availability in terms of recovery time during system state transition controlled by the rules. The effectiveness of the proposed approach is verified to an actual optical network case under two typical network restoration schemes and compare its accuracy against two analytical approaches based on RBD (Reliability Block Diagram) and Markov Chain. The sensitivity results reveal that link capacity has a more significant impact on application availability than link failures.

Model of optimal distribution of network resources with constraints on quality of service indicators

Existing algorithms and mathematical models of queuing at the nodes of a telecommunications network are considered in this paper. The necessity of coordinated solutions to problems of distribution of channel and buffer resources of the network is shown. A model for the optimal distribution of channel and buffer resources on network nodes has been developed. The optimization (minimization) criterion is the total average packet delay with constraints on the quality of service (QoS) indicators of heterogeneous flows. The optimization problem is presented as a constrained nonlinear programming problem and solved using the “fmincon” program of the optimization toolbox MATLAB package.

Cross-Layer Model of Dynamic Distribution of Radio Resources and Data Flow Service in LTE Networks

In this article, the results of the development of a mathematical model for the time-frequency resource allocation of the uplink channel and flow service in LTE (Long Term Evolution) networks are given. The proposed model is aimed at ensuring the maximum performance of the radio channel and the guaranteed quality of service for data flows of wireless network users. A comparative analysis of the proposed model with the existing methods of the time-frequency resource allocation of the LTE technology is carried out in terms of ensuring the overall performance of the uplink and allocating the required transmission rate to user stations while maintaining the quality of service. It is shown that the proposed model of dynamic distribution of radio resources and servicing of data streams increases the overall performance of the uplink compared to the Round Robin and Proportional Fair methods, by 1.42 and 1.23 times, respectively.

Efficient resource management in 6G communication networks using hybrid quantum deep learning model

The sixth generation (6G) wireless communication network is expected to provide wide-ranging coverage, short latency, cost effectiveness, lower power consumption, and a high level of security. The Quality of Service (QoS) can be improved with the help of proper resource management by utilizing Artificial Intelligence and Machine Learning procedures. The proposed Hybrid Quantum Deep Learning (HQDL) model which comprises of Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). The CNN achieves network reconfiguration, resource distribution and slice collection while RNN is utilized for error proportion, load balancing etc. The future model QoS performance is evaluated by utilizing numerous indefinite devices, slice features and congestion environments. The proposed model achieved an overall accuracy of 97.16% that replicates its pertinence.

A Systematic Analysis of Network Bandwidth Sharing in Smart Grid Environment

The integration of Information and Communication Technology (ICT) into modern infrastructure systems has given birth to the concept of “Intelligent Infrastructure,” with the smart grid as a prominent example. The smart management and efficient application of important electricity burden during agricultural production process has been realized by disposing energy efficiency management platform in rural areas. This paper investigates the applicability of grid computing within the context of intelligent infrastructure, with a particular emphasis on the smart grid. Grid computing, a distributed computing paradigm that utilizes the combined computational power of interconnected resources, has the potential to significantly improve the effectiveness, dependability, and sustainability of smart grid operations. In addition, the allocation and distribution of network resources by the grid’s systems will be examined in depth. While downloading gigabyte to terabyte-sized files from the internet, we frequently encounter various technical issues such as ISP restrictions, link expiration, etc In the meantime, the information exchanging has been proposed among the energy efficiency management platform, the distributed energy systems, the distribution automation system and the data acquisition system. Therefore, the intelligent scheduling of distributed energy can be realized in rural production during the peak period of electricity load and the affluent period.

THE METHOD OF CALCULATING THE CAPACITY OF THE CLOUD COMPONENT OF THE DISTRIBUTED MULTISERVICE NETWORK

A method for calculating the bandwidth of the cloud component of a distributed multiservice network is proposed. Which takes into account the probabilistic characteristics of the link of the first and second orders. At the preliminary stages, the probability of packet loss within the network link is calculated. An analysis of the queues of switching nodes of communication with the cloud is also carried out. Consistent application of the method will allow to estimate the probability of losses for different network users. It will also make it possible to determine the rational loading of network links with the aim of optimal distribution of network resources. The obtained results can be applied directly in the design of a distributed multiservice network. In it, user access to the link resource can be either unlimited or limited with the introduction of link resource reservation for priority classes of users. And also for the design of a distributed multiservice network in which users are provided with fixed bit rates of information transmission. The direction of further research is the extension of the method for a distributed multiservice network, in which some of the links are dependent. It is planned to develop an algorithm in which calculations for the cloud component of the network can be performed in parallel.

Distribution and Allocation of Network Resources Based on Predictive Analyses of Time-Series Telecommunications Data

With the fast development of different communication technologies, applications, and services, the adoption of advanced sensory and computing solutions, such as the various Internet of Things (IoT) and mobile computing solutions, is continuously growing. The massive adoption of mobile computing and IoT sensory devices encouraged the continuous growth of generated network traffic. Therefore, the selection of adequate solutions for efficient data processing became necessary. Despite numerous advantages arising from effective data processing, operators and enterprises working within the ICT domain have only limited amounts of available networking resources to store, process, and use valuable information extracted from large quantities of gathered data. In this paper, an optimal planning process and prediction of usage of network resources is examined. It takes into consideration the results of predictive modeling processes based on available sets of time series telecommunications data. The given forecasts enable effective selection of network architectures, as well as the distribution and allocation of network resources considering the cloud, edge, and fog networking concepts.

Multi-stage resource-aware congestion control algorithm in edge computing environment

Due to the uneven distribution of network resources and network, traffic, congestion is an inherent attribute of the Internet network. Network congestion will increase data transmission delay, reduce network throughput, increase the packet loss rate in the network, and even cause overall network performance degradation, which may cause large losses to users. This article aims to study multistage resource-aware congestion control algorithms based on edge computing environments. This article first compares and analyzes edge computing and traditional cloud computing. There are certain differences between edge computing technology and traditional cloud computing models. In the context of the Internet of Everything, the traditional cloud computing model also has some drawbacks. The low latency of edge computing can provide a good application experience and enhance the responsiveness of user applications. At the same time, the communication load of each network department is also reduced correspondingly, which solves the network congestion. Then, a congestion control algorithm based on cooperative paths was proposed, and the wireless sensor network topology and the limit of the congestion threshold of the wireless sensor network were introduced. Then it analyzes the problem of the MPTCP-based congestion control algorithm and analyzes the simulation results based on the energy-efficiency congestion control algorithm. The experimental results show that, compared with MPTCP, the throughput of the EECCA algorithm is increased by 52.6%, and it has better throughput performance. At the same time, the energy efficiency of the three algorithms in the simulation time is 1.1813 Mbits/J, 0.8311 Mbits/J, and 0.8021 Mbits/J, respectively. Compared with MPTCP, the energy efficiency of EECCA is increased by 47.3%, achieving the goal of energy saving.

Where do all my smart home data go? Context-aware data generation and forwarding for edge-based microservices over shared IoT infrastructure

With the explosion of the Internet of Things (IoT) devices, the advent of the edge computing paradigm, and the rise of intelligent applications for smart infrastructure surveillance, in-network data management is gaining a lot of research attention these days. The challenge lies in accommodating multiple application microservices with varying Quality of Service (QoS) requirements to share the sensing infrastructure for better resource utilization. In this work, we propose a novel data collection framework, CaDGen (Context-aware Data Generation) for such a shared IoT infrastructure that enables integrated data filtration and forwarding towards minimizing the resource consumption footprint for the IoT infrastructure. The proposed filtration mechanism utilizes the contextual information associated with the running application for determining the relevance of the data. Whereas the proposed forwarding policy aims to satisfy the diverse QoS requirements for the running applications by selecting the suitable next-hop forwarder based on the microservices distribution across different edge devices. A thorough performance evaluation of CaDGen through a testbed implementation as well as a simulation study for diverse setups reveals promising results concerning network resource utilization, scalability, energy conservation, and distribution of computation for optimal service provisioning. It is observed that the CaDGen can achieve nearly 35% reduction in the generated data for a moderately dynamic scenario without compromising on the data quality.

Uneven Resources network promotes cooperation in the prisoner's dilemma game

Scholars explored cooperative behavior on different network; however they did not consider the resource distribution and consumption on them. In our work, we propose a kind of uneven resource distribution network model, in which players can consume finite resources to survive. In our model, firstly, there are four players, and then we imitate real organism behavior, such as eating, migration, game, leaning, and reproduction. Meanwhile, during this process, finite resource also decreases with players’ consumption. After numerical simulation, we find that defectors can occupy the most resource-rich areas at first, but they cannot survive in hostile area and decrease rapidly. While cooperators always keep growing. In addition, when resources decrease substantially, cooperative behavior can be promoted. This also sprouts new insights on survival and development of cooperative behavior.

HS-MOEA/D: An Oriented Algorithm for Delay and Reliability VNF-SC Deployment

Network function virtualization (NFV) technology can realize on-demand distribution of network resources and improve network flexibility. It has become one of the key technologies for next-generation communications. Virtual network function service chain (VNF-SC) deployment is an important problem faced by network function virtualization technology. In this paper, the problem, VNF deployment for VNF-SC, is investigated. First, a two-objective mathematical model, which maximizes balancing and reliability of SFC, is established. In this model, VNFs are divided into two classes, i.e., part of required VNFs in each VNF-SC is dependent, others are independent. Second, harmony search-based MOEA/D (HS-MOEA/D) is proposed to solve the model effectively. In HS-MOEA/D, Chebyshev decomposition mechanism is used to transform multiobjective optimization problem into a series of single-objective optimization subproblems. A new evolutionary strategy is deeply studied in order to propose a new harmony search (HS) algorithm. Finally, to show high performance of the proposed algorithm, a large number of experiments are conducted. The simulation results show that the proposed algorithm enhances the reliability of SFC and reduces the end-to-end delay.

Interfirm network structure and firm resources: Towards a unifying concept

PURPOSE: The objective of this paper is to propose a concept of network resource distribution that systematically unifies the resource-based and network-based perspectives on interfirm networks and enables integrated analysis of how firm resources and network structure interact to affect firm performance. METHODOLOGY: This conceptual paper first reviews the extant literature on interfirm networks and then develops the unifying concept of network resource distribution. FINDINGS: The literature review indicates that strategy scholars have long sought to integrate the resource-based view and the social network explanations of firm performance but, thus far, only a partial integration has been achieved. In particular, studies on the resource-level heterogeneity of interfirm networks have largely been limited to the analysis of firm dyads. How firm resources and network structure beyond the immediate network partners interact to affect firm performance has not yet been adequately addressed. The proposed unified concept of network resource distribution systematizes prior research and illuminates how network structure and firm resources interact to affect firm performance beyond the immediate network partners. IMPLICATIONS FOR THEORY AND PRACTICE: For theory, this paper highlights gaps in the extant literature on interfirm networks and proposes a unifying concept that can be utilized to address these gaps and to develop further theory in the area. For practice, this paper encourages managers not to limit their analyses of strategic alliances to immediate partnerships; it is also crucial to consider the partners and their resources, and reflect on how they are related to one another outside of the immediate partnership portfolio. ORIGINALITY AND VALUE: Network resource distribution is a novel concept that ties together and systematizes various strands of research on interfirm networks, thus providing a foundation for future research in the area. The concept is also amenable to detailed operationalization, facilitating subsequent quantitative testing of theoretical arguments combining firm resources and the structure of a network.

Метод Форда-Фалкерсона и генетический алгоритм для оптимального распределения сетевых ресурсов при финансовых ограничениях

The article is devoted to finding the optimal network resources distribution in the network represented by anundirected complete connected planar uplifted graph. It is assumed that the weight of the edge of the graph is determined by the minimum bandwidth(traffic) of the router at its nodes. The functional is the sum of the bandwidth (maximal flow) of the routers on all nodes under the restrictions on their total cost. The complexity of the problem lies in the fact that there are two types of uncertainty in it: the first – the type of router in each network node is not initially determined, the second – the path between the nodes on which the maximum traffic is realized is not defined. The first uncertainty is solved by using the Genetic Algorithm and the second uncertainty is solved by using the Ford Fulkerson algorithm. An example of calculation for a fragment of a network of a real Internet Service Provider (ISP) is presentedin this article.

Manajemen Bandwidth Berdasarkan Prediksi Perilaku Pengguna Pada Jaringan TCP/IP Dengan Jaringan Syaraf Tiruan

User behavior prediction on TCP / IP networks is very possible because each data packet is passed through a router that can be analyzed and then classified into several behavior types from commonly performed activities by internet users. User-behavior predictions developed using artificial neural networks (ANN), which can show accurate results in predicting the bandwidth used by users in the next few days. Predicting results helps network administrators to provide a more accurate picture of user behavior patterns in the future, where the Personnel Service Application System (SAPK) which becomes the user behavior internet priority it's only utilized network resources at 26.8% of the total internet network resources available. The ANN that was developed also showed that 40.3% of network resources were used more by streaming video activities and 37% were used for streaming audio activities. With the prediction result, the network administrator rearranges the internet network resources distribution by applying a new prediction results pattern. This study conclusively shows that user behavior with streaming video activities is the largest bandwidth user and needs to get special attention on the internet network at the Regional X National Civil Service Agency Office.

Analysis of Security of QoS in WiMAX and Mobile Networks

In this paper we discuss several elements of importance for securing QoS in multimedia networks. Firstly, we present the first factor, which refers to understanding the characteristics of multimedia traffic in order to define and implement the QoS requirements. Secondly, factor refers to translation between QoS parameters that implies the distribution of system and network resources, and thirdly the factor establishes the appropriate QoS architecture that can provide the required QoS guarantees for multimedia applications. We have been analyzing security-critical applications such as remote operation, which may require a guaranteed level of availability (hard QoS). There are basically two ways to secure a guaranteed QoS. The first is simply to provide a lot of resources, enough to meet the expected peak (peak) requirements with a significant security margin. This approach generously oversupplies the (over provisioning) network. We gave a detailed security analysis as features of WiMAX. More precisely, our analysis is based on the claim that its key feature of the WiMAX network is that the security layer is built into the protocol leg instead of being added later, i.e. the security layer is complex between PHY and MAC layers.

Joint energy efficient power and subchannel allocation for uplink MC‐NOMA networks

SummaryDue to the exponential rise in the number of subscribers in existing wireless networks, energy‐efficient distribution and utilization of network resources have become the preliminary objective of researchers nowadays. Nonorthogonal multiple access (NOMA) has attained widespread significance in this regard and has become a strong candidate for enhancing energy efficiency (EE) performance of existing networks. Multicarrier NOMA (MC‐NOMA) technique is an extension of NOMA that breaks the available resource block into various subchannels and allocates them efficiently to NOMA users (NUs). MC‐NOMA networks have the ability to further enhance system performance by efficiently exploiting channel diversity for addressing the problems of spectral inefficiency and power limitations. Therefore, in this paper, we have considered MC‐NOMA technology for an uplink scenario to investigate its performance for enhancing system EE by performing energy‐efficient power and subchannel allocation. To this end, we have formulated a joint user clustering, subchannel allocation, and power allocation problem for EE maximization (JSPEE) of uplink MC‐NOMA scenario. Due to the nonconvex, combinatorial nature of the problem, we propose a two‐step solution: that is, for each subchannel, first, subchannel allocation and user clustering are attained through low‐complexity suboptimal algorithm, which is followed by energy‐efficient power allocation of NUs. For subchannel allocation and user clustering, we exploit the channel diversity in the form of difference in channel gain values of various users. For power allocation, the nonconvex nature of the formulated problem is tackled by employing Dinkelbach and successive convex approximation (SCA) techniques to attain an energy‐efficient solution. Our simulation results portray that the JSPEE algorithm clearly outperforms the current NOMA as well as OMA works and enhances the system EE by efficient user clustering and exploiting channel diversity.

Income distribution in family networks by gender and proximity

AbstractWhereas the significance of family networks for support and well‐being has been shown in previous research, few studies have analysed the income distribution within family networks. The aim of this study is to examine income distribution within family networks and how they have changed over time for women and men in different parts of the income distribution and if the incomes are more similar in the geographically proximate family network. The analysis is based on register data and by use of ordinary least squares (OLS) and quantile regressions. The results indicate that men in the lowest income group tend to have become more similar to their family network over time. Gender differences have decreased, possibly as an effect of women's higher labour market participation rate leading to decreased income disparity. This paper contributes by highlighting how the uneven distribution of economic resources in family networks adds to individual's own resources.