A new quantum key distribution resource allocation and routing optimization scheme

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

Integrated Route Planning and Resource Allocation for Connected Vehicles

Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities.As the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly,it becomes critical to efficiently orchestrate the communication and computation resources on edge clouds.Existing methods usually perform resource allocation in a fairly effective but still reactive manner,which is subject to the capacity of nearby edge clouds.To deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity,we proactively balance the edge computing demands across edge clouds by appropriate route planning.In this paper,route planning and resource allocation are jointly optimized to enhance intelligent driving.We propose a multi-scale decentralized optimization method to deal with the curse of dimensionality.In large-scale optimization,backpressure algorithm is used to conduct route planning and load balancing across edge clouds.In small-scale optimization,game-theoretic multi-agent learning is exploited to perform regional resource allocation.The experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.

Energy Efficient Constrained Shortest Path First-Based Joint Resource Allocation and Route Selection for Multi-Hop CRNs

Cognitive radio networks(CRNs) are expected to improve spectrum utilization efficiently by allowing secondary users(SUs) to opportunistically access the licensed spectrum of primary users(PUs).In CRNs,source and destination SUs may achieve information interaction in an ad hoc manner.In the case that no direct transmission link between the SU transmission pairs is available,multi-hop relay SUs can be applied to forward information for the source and destination SUs,resulting in multi-hop CRNs.In this paper,we consider a multi-hop CRN consisting of multiple PUs,SU transmission pairs and relay SUs.Stressing the importance of transmission hops and the tradeoff between data rate and power consumption,we propose an energy efficient constrained shortest path first(CSPF)-based joint resource allocation and route selection algorithm,which consists of two sub-algorithms,i.e.,CSPF-based route selection sub-algorithm and energy efficient resource allocation sub-algorithm.More specifically,we first apply CSPF-based route selection sub-algorithm to obtain the shortest candidate routes(SCRs) between the SU pair under the transmission constraints.Then,an energy efficient resource allocation problem of the SCRs is formulated and solved by applying iterative algorithm and Lagrange dual method.Simu-lation results demonstrate the effectiveness of the proposed algorithm.

Multi-Path Routing and Resource Allocation in Active Network

An algorithm of traffic distribution called active multi-path routing (AMR)in active network is proposed. AMR adopts multi-path routing and applies nonlinear optimizeapproximate method to distribute network traffic among multiple paths. It is combined to bandwidthresource allocation and the congestion restraint mechanism to avoid congestion happening and worsen.So network performance can be improved greatly. The frame of AMR includes adaptive trafficallocation model, the conception of supply bandwidth and its' allocation model, the principle ofcongestion restraint and its' model, and the implement of AMR based on multi-agents system in activenetwork. Through simulations, AMR has distinct effects on network performance. The results show AMRisa valid traffic regulation algorithm.

STUDY ON STRATEGY OF DYNAMIC JOINT ROUTING AND RESOURCE ALLOCATION IN LAYERED OPTICAL TRANSPORT NETWORKS

A layered network model for optical transport networks is proposed in this paper,which involves Internet Protocol(IP) ,Synchronous Digital Hierarchy(SDH) and Wavelength Division Mul-tiplexing(WDM) layers. The strategy of Dynamic Joint Routing and Resource Allocation(DJRRA) and its algorithm description are also presented for the proposed layered network model. DJRRA op-timizes the bandwidth usage of interface links between different layers and the logic links inside all layers. The simulation results show that DJRRA can reduce the blocking probability and increase network throughput effectively,which is in contrast to the classical separate sequential routing and resource allocation solutions.

Resource Partitioning and Routing Optimization in Relay Enhanced Cellular Network

A joint routing and resource partitioning scheme were proposed to improve cell capacity and user throughput of cellular network enhanced with two-hop fixed relay nodes (FRNs). Radio resources are partitioned under a reuse partitioning based framework, which guarantees effective and efficient inter-cell interference management. At the same time, each mobile terminal was assigned a channel-dependent route by the routing controller, which tries to maximize the cell capacity under the constraint imposed by reuse partitioning. Intensive computer simulations demonstrate the performance superiority of the FRN enhanced cellular network employing this scheme in comparison with conventional network, as well as the validity of the channel-dependent routing mechanism.

Impact of joint resource partitioning and routing on performance of relay enhanced cellular networks

Radio resource assignment schemes and routing strategies in relay enhanced cellular networks are proposed in this paper. Under the reuse partitioning-based frequency planning framework, the intra-cell resource partitioning between the base station and relay nodes was addressed firstly by introducing a metric of effective reuse factor. Then, coverage-oriented and capacity-oriented rantings, as well as two link bandwidth assignment schemes＂ equal-bandwidth per link＂ and ＂equal-bandwidth per mobile station＂ were developed. These key issues and their impacts on the system performance were analyzed comprehensively and supported by simulations. Results show that the cell capacity and edge user throughput of the proposed network are superior to the traditional non-relay network when an appropriate effective reuse factor is adopted.

Traffic resource allocation for complex networks

In this paper, an optimal resource allocation strategy is proposed to enhance traffic dynamics in complex networks. The network resources are the total node packet-delivering capacity and the total link bandwidth. An analytical method is developed to estimate the overall network capacity by using the concept of efficient betweenness （ratio of algorithmic betweenness and local processing capacity）. Three network structures （scale-free, small-world, and random networks） and two typical routing protocols （shortest path protocol and efficient routing protocol） are adopted to demonstrate the performance of the proposed strategy. Our results show that the network capacity is reversely proportional to the average path length for a particular routing protocol and the shortest path protocol can achieve the largest network capacity when the proposed resource allocation strategy is adopted.

Energy-Aware Service Selection Method Based on Sharing Routes in Wireless Sensor Networks

This paper presents a novel energy-aware algorithm for service composition based on sharing routes in Wireless Sensor Networks (WSNs). The method integrates the resource of the overlapping WSNs to a virtual resource pool in the execution cycles of the workflow. This approach chooses the suitable service instances according to the current execution environment and user requirements with minimum energy consumption. Finally, the performance of sharing routes service composition selection in WSNs has been evaluated.

Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern era.IoT generally refers to a network of gadgets linked via wireless network and communicates via internet.Resource management,especially energy management,is a critical issue when designing IoT devices.Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment.In this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT environment.The proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance phase.The proposed EECBRMmodel was validated through a series of experiments and the results were verified under several aspects.EECBRM model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network lifetime.When simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant manner.Thus,the efficiency of the proposed model is established.

Security Concerns with IoT Routing: A Review of Attacks, Countermeasures, and Future Prospects

Today’s Internet of Things (IoT) application domains are widely distributed, which exposes them to several security risks and assaults, especially when data is being transferred between endpoints with constrained resources and the backbone network. Numerous researchers have put a lot of effort into addressing routing protocol security vulnerabilities, particularly regarding IoT RPL-based networks. Despite multiple studies on the security of IoT routing protocols, routing attacks remain a major focus of ongoing research in IoT contexts. This paper examines the different types of routing attacks, how they affect Internet of Things networks, and how to mitigate them. Then, it provides an overview of recently published work on routing threats, primarily focusing on countermeasures, highlighting noteworthy security contributions, and drawing conclusions. Consequently, it achieves the study’s main objectives by summarizing intriguing current research trends in IoT routing security, pointing out knowledge gaps in this field, and suggesting directions and recommendations for future research on IoT routing security.

Workload-aware request routing in cloud data center using software-defined networking

Large latency of applications will bring revenue loss to cloud infrastructure providers in the cloud data center. The existing controllers of software-defined networking architecture can fetch and process traffic information in the network. Therefore, the controllers can only optimize the network latency of applications. However, the serving latency of applications is also an important factor in delivered user-experience for arrival requests. Unintelligent request routing will cause large serving latency if arrival requests are allocated to overloaded virtual machines. To deal with the request routing problem, this paper proposes the workload-aware software-defined networking controller architecture. Then, request routing algorithms are proposed to minimize the total round trip time for every type of request by considering the congestion in the network and the workload in virtual machines（VMs）. This paper finally provides the evaluation of the proposed algorithms in a simulated prototype. The simulation results show that the proposed methodology is efficient compared with the existing approaches.

Polynomial-Time Adaptive Routing Algorithm Based on Spectrum Scan in Dynamic Flexible Optical Networks

Optical Orthogonal Frequency Division Multiplexing (OOFDM) has been proposed as a highly spectrum-efficient modulation technique, which can provide flexible spectrum assignment with fine granularity. In OOFDM-based flexible optical networks, Routing and Spectrum Assignment (RSA) has become a key problem. However, widely used dynamic RSA schemes, such as Fixed Routing (FR) and K-shortest Paths (KSP) routing schemes, are not able to realize route computation based on the link state information, thus leading to poor blocking performance and inefficient resource utilization. To solve this problem, Adaptive Routing (AR) schemes, e.g., the Entire Path Searching (EPS) scheme, have been proposed recently. These schemes have low blocking probability; however, since their computational complexities are factorial, they are not suitable for use in real networks. In this paper, we propose a novel Spectrum-Scan Routing (SSR) scheme in dynamic flexible optical networks. To the best of our knowledge, SSR is the first polynomial-time AR scheme that can realize adaptive shortest-route computation. Simulation results show that our proposed SSR scheme has lower blocking probability and higher resource utilization compared with FR and EPS. Moreover, the worst-case computational complexity of SSR increases linearly with the network scale of the torus topologies, making it applicable to real networks.

基于强化学习的大规模多模Mesh网络联合路由选择及资源调度算法

为了平衡新型电力系统中大规模多模Mesh网络的传输可靠性和效率,该文在对优化问题进行描述和分析的基础上提出一种基于强化学习的大规模多模Mesh网络联合路由选择及资源调度算法,分为两个阶段。在第1阶段中,根据网络拓扑结构信息和业务需求,利用一种多条最短路径路由算法,输出所有最短路径。在第2阶段中,提出一种基于多臂老虎机(MAB)的资源调度算法,该算法基于得到的最短路径集合构建MAB的摇臂,然后根据业务需求计算回报,最终给出最优的路由选择及资源调度方式用于业务传输。仿真结果表明,所提算法能够满足不同的业务传输需求,实现端到端路径的平均时延和平均传输成功率的高效平衡。

考虑区域资源利用均衡的电动公交充电站选址及充电路径问题

在新基建助推区域平衡发展和低碳双重背景下,研究电动公交充电站选址及充电路径问题,以经济效益最大化和区域资源利用均衡为目标,考虑公交司机可能充电时机以及充电需求、运营时间、利用率等约束条件建立两阶段多目标决策模型,设计多重嵌套式遗传算法求解模型,并应用于算例检验模型可行性。结果表明:仅考虑经济效益目标会产生充电“拥挤”、路途过长等弊端,而本文模型能有效缩小区域间公交充电效率差异,提高全局资源利用均衡性;通过降低充电站容量可提高充电站利用率与经济效益,但区域资源利用均衡性会减弱,且充电站容量对公交充电路途所耗费时间的作用较为敏感;适当增加区域资源利用均衡目标的权重更有利于提高总体优化效果。研究结果可为公交充电站选址和路径规划提供决策支持。

域间路由安全增强及区块链技术的应用研究

边界网关协议(BGP)是目前互联网中事实上的域间路由标准,其安全性建立在自治系统(AS)身份及路径真实完整基础上,但BGP自身缺乏相应的安全保障机制,其安全问题备受关注。区块链作为构建新一代信息基础设施的创新技术,支撑在开放互联网环境中构建分布式多方信任体系,为当前传统BGP安全增强中遇到的问题和挑战提供了技术应对。根据技术发展和安全迭代,将BGP安全增强划分为3个递进阶段:以安全边界网关协议(S-BGP)为代表的理论创新阶段,以资源公钥基础设施(RPKI)为安全基础的应用实践阶段,以及基于区块链技术的创新发展阶段。分析BGP路由传播方式和路由策略的脆弱性,以及前缀劫持、路径伪造和路由泄露3类典型的安全威胁方式;梳理传统BGP安全增强技术路线和研究脉络,重点从主动防御和异常检测两个方面讨论针对错误路由宣告攻击的应对机制;在简要介绍区块链技术基本原理的基础上,利用区块链的去中心化、防篡改、可溯源、分布式部署等特性,重点探讨基于区块链的BGP安全增强技术的实现思想、路径和方法;主要结合区块链技术的应用,讨论BGP安全增强中面临的挑战,并对未来研究方向进行展望。

气候变化对南水北调西线工程的影响及建议

南水北调工程是国家水网的重要组成部分,其西线工程建设是缓解我国西北、华北地区水资源短缺的重要举措。气候变化下西线工程引水区和受水区水资源情势直接关系到工程的规划建设与运行方式。基于历史实测资料和气候模式情景,采用数理统计与水文模拟相结合的途径分析了气候变化对南水北调西线工程引水区和受水区水资源的影响。结果表明:1)1961—2020年西线调水区和受水区气候变化以暖湿化为主要特征,调水区年径流量整体稳定略增,受水区年径流量显著性减少。2)未来中期(2035年)和远期(2050年),调水区和受水区气温继续显著升高,降水多呈非显著增加;未来中期、远期调水区年径流量较基准期(1961—2000年)分别增加1.42%和2.08%,受水区年径流量分别减少1.02%和0.28%。3)未来气候变化下,调水区年径流量相对稳定,能够满足调水需求,受水区年径流量略减,对跨流域调水需求有所增加。为确保南水北调西线工程的长期稳定运行,建议加强气候变化及其影响的不确定性研究,制定科学合理的应对措施,以应对未来可能的水资源短缺的挑战。

分支定价算法求解携带医疗资源的家庭医护人员调度问题

针对携带医疗资源的家庭医护人员调度优化问题,综合考虑医疗资源、弹性服务时间窗、医患技能等级匹配等特征,以最小化运营成本和弹性时间窗惩罚成本为目标构建数学模型。并根据Dantzig-Wolfe分解原理将模型重构为基于访问路径的主问题和若干带资源约束的最短路子问题模型。根据问题特征设计了分支定价算法来求解该问题。小规模算例结果表明医疗资源特性对医护人员路径调度有重要影响,而弹性时间窗可以有效降低总成本;多种算例的运算结果证明了分支定价算法求解的高效性。

基于水网视角的水资源与关联要素适配性评价

为研究水资源与关联要素的空间错配问题,基于水网视角,根据适配理论和水资源多维属性理论,提出了包含资源层、社会层、经济层和生态层的水资源与关联要素适配性评价方法,并应用于南水北调中线一期工程影响区。结果表明:该方法可较为合理、系统地揭示水资源与关联要素的适配特征;2016—2020年南水北调中线一期工程影响区水资源与关联要素空间分布相对合理,整体基尼系数为0.388,资源层和社会层水资源与关联要素空间适配性较好,基尼系数分别为0.283和0.284,而经济层和生态层水资源与关联要素错配问题严重,基尼系数为0.501和0.486;南水北调中线一期工程通水后,水资源与关联要素的空间适配性在资源层、社会层、经济层有不同程度改善,在生态层变化不大。

全维可定义网络5.0新型网络架构

【目的】面对万物智能互联时代的新兴产业向网络技术提出的确定性、安全性、管控能力等方面的需求,尽力而为、形式单一的IP网络难以同时提供网络质量、安全性、管理能力和效率的性能保障。新的历史阶段急需一个新的网络体系架构,在充分考虑网络广泛互联的基础上,更深度地关注网络空间安全和产业应用对网络的精细要求。【方法】本文提出了全维可定义网络5.0新型网络架构,采用“以网络为中心、能力内生、全维定义”的全新网络架构设计理念,提出了一套覆盖数据面、控制面、管理面和内生安全体系的新型核心协议体系,利用泛在IP、人工智能等技术进行高效的寻址、路由规划、可信管理等。【结果】通过这种架构,网络5.0将网络中的存储、计算资源和多等级转发能力进行高效整合,从而更好地满足应用需求,并实现网络的内生安全、确定性传输、算网融合、差异化服务等功能,显著降低了网络应用的复杂性,提高了网络效率和应用质量。【结论】全维可定义网络5.0促进了泛在场景应用的发展,使得网络从一个单纯的连接工具转变为一个功能丰富、服务多元、安全可靠的平台,并已成功实践和验证在多个应用领域中,成为新型网络架构主流发展路径之一。