The deployment of large-scale LoRaWAN networks requires jointly optimizing conflicting metrics like Packet Delivery Ratio (PDR) and Energy Efficiency (EE) by dynamically allocating transmission parameters, including Carrier Frequency, Spreading Factor, and Transmission Power. Existing algorithms often ignore the complexity of multi-objective dynamic adaptation to oversimplify this challenge, focusing on a single metric or lacking the adaptability needed for dynamic channel environments, leading to suboptimal performance. To address this, we propose two online learning-based resource allocation frameworks that intelligently navigate the PDR-EE trade-off. Our foundational proposal, D-LoRa, is a fully distributed framework that models the problem as a Combinatorial Multi-Armed Bandit. By decomposing the joint parameter selection and employing specialized, disaggregated reward functions, D-LoRa dramatically reduces learning complexity and enables nodes to autonomously adapt to network dynamics. To further enhance performance in LoRaWAN networks, we introduce CD-LoRa, a hybrid framework that integrates a lightweight, centralized initialization phase to perform a one-time, quasi-optimal channel assignment and action space pruning, thereby accelerating subsequent distributed learning. Extensive simulations and real-world field experiments demonstrate the superiority of our frameworks, showing that D-LoRa excels in nonstationary environments while CD-LoRa achieves the fastest convergence. In physical deployments, our algorithms outperform state-of-the-art baselines, improving PDR by up to 10.8% and EE by 26.1%, demonstrating their practical effectiveness. Moreover, extensive simulations with up to 250 nodes confirm the scalability and efficiency of the proposed frameworks in large-scale LoRaWAN networks.

The deployment of LoRa networks necessitates joint performance optimization, including packet delivery rate, energy efficiency, and throughput, by dynamically configuring multiple LoRa parameters for packet transmission across varying channel environments. Due to the complexity of modeling channel features and the coupling relationship between LoRa parameters and metrics, existing works have sacrificed adaptability by focusing on specific aspects rather than the whole. Therefore, we propose D-LoRa, a distributed parameter adaptation scheme, based on reinforcement learning towards network performance. We first build a comprehensive analytical model for the LoRa network that considers complex channel features, including path loss, quasiorthogonality of spreading factor, and packet collision. Then, we formulate the joint optimization problem as a combinatorial Multi-Armed Bandit (CMAB) problem and devise metric factors to handle the trade-off among different performance metrics. Experimental results show that our scheme can increase the packet delivery rate by up to 18.5% and demonstrate superior adaptability across different performance metrics.

Accurate per-flow measurement is essential for effective network management in programmable networks. However, achieving this accuracy remains challenging due to limited switch resources and the massive scale of network flows. Existing sketch-based methods often encounter significant measurement errors, particularly when dealing with the large number of extremely small flows, known as "ant flows". To address this issue, this paper introduces Mix Sketch, a novel measurement framework designed for differentiated and precise per-flow measurement. Mix Sketch uniquely categorizes traffic into elephant, mouse, and ant flows, and employs a tailored three-level structure to measure each flow category appropriately. This approach significantly enhances measurement accuracy, especially for ant flows. Furthermore, we propose Co-Mix Sketch, a lightweight collaborative measurement scheme that leverages network topology to distribute Mix Sketch components across different node tiers, thereby optimizing resource utilization and improving accuracy without requiring complex coordination. Evaluations conducted on real-world traffic traces demonstrate that Mix Sketch substantially outperforms baseline single-node methods, while Co-Mix Sketch achieves notable accuracy improvements with minimal overhead compared to existing collaborative approaches.

With the proliferation of mobile terminals, the continuous upgrading of services, 4G LTE networks are showing signs of weakness. To enhance the capacity of wireless networks, millimeter waves are introduced to drive the evolution of networks towards multi-band 5G heterogeneous networks. The distinct propagation characteristics of mmWaves, microwaves, as well as the vastly different hardware configurations of heterogeneous base stations, make traditional access strategies no longer effective. Therefore, to narrowing the gap between theory, practice, we investigate the access strategy in multi-band 5G heterogeneous networks, taking into account the characteristics of mobile users, asynchronous switching between uplink, downlink of pico base stations, asymmetric service requirements, user communication continuity. We formulate the problem as integer nonlinear programming, prove its intractability. Thereby, we decouple it into three subproblems: user association, switch point selection, subchannel allocation, design an algorithm based on optimal matching, spectral clustering to solve it efficiently. The simulation results show that the proposed algorithm outperforms the comparison methods in terms of overall data rate, effective data rate, number of satisfied users.

Traffic measurement is essential for network management. Sliding window models can provide network management tasks with flow statistics within the most recent window at any moment. However, most existing solutions over sliding windows are not designed for traffic measurement scenarios, therefore they have higher complexity and cannot be implemented on programmable hardware switches. To address the issues, we designed Ring Sketch, which is a generic, low-complexity, and hardware-friendly traffic measurement framework over sliding windows. Ring Sketch can not only be easily implemented on programmable hardware switches but can also accurately answer typical flow statistics queries by using different sketches. Then we propose the estimation strategies for Ring Sketch and theoretically analyze its error bounds. At last, we implement Ring Sketch on OVS-DPDK and a programmable hardware switch with a Tofino chip, and all the source codes are released on GitHub. The experimental results show that Ring Sketch has a throughput over 3x higher than the state-of-the-art Sliding Sketch, and in typical measurement tasks, Ring Sketch can achieve high measurement accuracy.

Using multiple unmanned aerial vehicles (UAVs) with backscatter communication to collect data from Internet of Things (IoT) devices has emerged as a promising solution. However, many existing UAVs path planning schemes for data collection suffer from performance degradation due to their limited consideration of the full collaboration of UAVs and dynamic stochastic environments. Therefore, we propose a path planning scheme for the data collection task in multi-UAV IoT based on multi-agent reinforcement learning (MARL) to minimize the task completion time. Due to the inherent asynchronous decision making among the agents, we model the path planning problem as a macro-action decentralized partially observable Markov decision process. Furthermore, we design an action mask mechanism to enhance data efficiency, which accelerates the training speed. Simulation results show that our scheme reduces the average task completion time by 15 %.

Traffic measurement is critical for network manage-ment. The programmable networking paradigm paves the way for implementing fine-grained and accurate traffic measurement. However, in programmable networking, the programmable re-sources on hardware switches are highly limited. Most existing solutions have not considered the deployment of multiple traffic measurement tasks under resource constraints in programmable networks. To address the issue, we construct the network model and problem formulation, and we refer to this problem as the traffic measurement task deployment problem and prove it is NP-hard. To solve it, we proposed an approximate algorithm called Ant Colony Optimization with Dynamic Pruning (ACO-DP). We conducted simulations on the Fat-Tree topologies to evaluate the performance of ACO-DP. The evaluation results show that ACO-DP can achieve much higher overall measurement utility and faster convergence compared to other benchmark algorithms, and the solutions returned by ACO-DP are very close to the optimal solutions.

Wireless sensor networks (WSNs) enable intelligent collaborative perceptions in the Internet of Things. However, devices in WSNs are battery-powered with limited energy resources. During transmission, routing policies significantly affect the energy efficiency in terms of both energy consumption and energy balance among nodes, and further impact the network lifetime. Previous works mostly used heuristic fixed strategies to make routing decisions based on incomplete information in a distributed manner for lower control costs and faster calculation when facing numerous devices in WSNs, which easily lead to performance limitations and routing loops. To this end, we model the network lifetime maximization problem as a decentralized partially observable Markov decision process and propose a new scheme MeFi based on Mean Field Reinforcement Learning to perform real-time energy-efficient routing policies for WSNs. The utilization of Mean Field Theory effectively simplifies the intractable interactions among numerous agents and guides the policy training. Additionally, a prioritized-sampling loop-free algorithm is developed to eliminate routing loops and avoid routing policies with significant energy consumption. Experimental results show that our scheme outperforms several algorithms by up to 50%, significantly enhancing energy efficiency and extending WSN lifetime under different circumstances.

Geological environment remote sensing (GERS) interpretation contributes to lithological mapping, disaster prediction, soil erosion monitoring, and so on. However, the rich diversity, complex distribution, interclass similarities, and uncertainties in data quality of geological elements pose challenges to GERS interpretation. In addition, current automatic feature extraction of GERS elements, which rely on deep learning (DL) and remote sensing (RS) information process technologies, often require sufficient labeled data. Due to the enormous labor cost and specialized expertise needed, labeled GERS samples are limited to training the data-driven models. To tackle the above challenges, we introduce the semi-supervised dual-model progressive self-training (DM-ProST) framework. This framework employs two DL networks with different initializations as evaluator models to correct each other. A sample filtering strategy is then implemented to evaluate the quality of unlabeled samples, selecting high-quality and reliable ones to expand the training set. In addition, a fully connected conditional random field (CRF) module is incorporated to optimize DL network prediction maps, thereby enhancing the boundary performance of segmentation results. The framework utilizes a multitask loss function that combines consistency loss with cross-entropy, enabling the models to learn discriminative GERS features. This process accurately generates pseudo-labels and achieves precise lithology mapping of GERS with a small amount of annotation samples. Finally, we conducted an experimental evaluation on the Landsat 8 dataset in Xinjiang, China, and massive experiments proved the effectiveness of DM-ProST.

Mobile Edge Computing (MEC) is a promising computing paradigm that provides cloud computing services in proximity to end users. Due to the bursty and spatially imbalanced arrival of computation tasks, the workload on different edge servers may vary wildly. To improve the Quality of Experience (QoE), peer offloading has been proposed as an effective cooperation method that offloads tasks from busy edge servers to idle ones. Although the average latency has been extensively considered in the design of peer offloading strategies, the worst-case latency, a common Quality of Service (QoS) requirement that is usually demanded by latency-sensitive applications, yet receives much less attention. In this paper, we study the task scheduling among collaborative edge servers and propose an online algorithm that aims to maximize the system utility under the worst-case latency requirement and long-term energy consumption constraints. Both theoretical analysis and simulation results demonstrate that our algorithm performs well under various situations.

With the development of Mobile Edge Computing (MEC) and Internet of Things (IoT) technology, various real-time monitoring and control applications are deployed to benefit people’s daily life. The performance of these applications relies heavily on the timeliness of collected environmental information, which can be effectively quantified by the recently introduced metric named age of information (AoI). Although extensive researches have been conducted to optimize AoI under various circumstances, these works commonly require a priori information about the system dynamics that is usually unknown in realistic situations. To design a more practical scheduling algorithm, in this paper, we formulate the AoI minimization problem as a Constrained Markov Decision Process (CMDP) which can be solved by Reinforcement Learning (RL) algorithms without prior knowledge. To improve the running efficiency, we (1) introduce post-decision states (PDSs) to exploit the partial knowledge of the system’s dynamics, (2) perform a batch update in every learning step, (3) decompose the system-level value function into multiple device-level value functions, and (4) propose a heuristic algorithm to find the greedy action. Numerical results demonstrate that our algorithm is highly efficient and outperforms the benchmarks under various scenarios.

The integration of Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT), which is usually referred to as Wireless Powered Mobile Edge Computing (WP-MEC), has been recognized as a promising technique to enhance the lifetime and computation capacity of wireless devices (WDs). Compared to the conventional battery-powered MEC networks, WP-MEC brings new challenges to the computation scheduling problem because we have to jointly optimize the resource allocation in WPT and computation offloading. In this paper, we consider the energy minimization problem for WP-MEC networks with multiple WDs and multiple access points. We design an online algorithm by transforming the original problem into a series of deterministic optimization problems based on the Lyapunov optimization theory. To reduce the time complexity of our algorithm, the optimization problem is relaxed and decomposed into several independent subproblems. After solving each subproblem, we adjust the computed values of variables to obtain a feasible solution. Extensive simulations are conducted to validate the performance of the proposed algorithm.

Geological remote sensing interpretation can extract elements of interest from multiple types of images, which is vital in geological survey and mapping, especially in inaccessible regions. However, due to numerous classes, high interclass similarities, complex distributions, and sample imbalances of geological elements, the interpretation results of machine-learning (ML)-based methods are understandably worse than manual visual interpretation. Additionally, scholars in remote sensing have mainly carried out their works to interpret a single geological element category, such as mineral, lithological, soil and structure. The interpretation of multiple geological elements is missing, which is more in line with the open world. To improve the interpretation results of ML-based methods and reduce the labor cost in geological survey and mapping, we propose a deep-learning (DL)-feature-based adaptive multi-source data fusion network (AMSDFNet) for the efficient interpretation of multiple geological remote sensing elements. The AMSDFNet has two branches for learning valuable spatial and spectral information from two kinds of data sources, wherein the atrous spatial pyramid pooling operation and an attention block are applied to adaptively extract and fuse multi-scale informative features. A hard example mining algorithm was also added to select important training examples to address sample imbalance. A large-scale region in western China with sufficient geological elements was set as the research area. The proposed model improved the two critical metrics by more than 2% in the experiment section. As far as we know, this research work is the first time DL features and multi-source remote sensing images have been utilized to simultaneously interpret geological elements of lithology, soil, surface water, and glaciers. The extensive experimental results demonstrated the superiority of DL features and our model in geological remote sensing interpretation.

With an ambitious increase in the number of Internet of Things (IoT) terminals, IoT networks face a huge challenge which is providing diverse and complex network services with different requirements on a common infrastructure. To solve this challenge, Software Defined Network (SDN) and Network Function Virtualization (NFV) are adopted to build next-generation IoT networks which are softwarized and virtualized. This way, network functions are virtualized as Virtualized Network Functions (VNFs) and a network service consists of a set of VNFs. One of the main challenges for realizing this paradigm is the optimal resource allocation for VNFs. Most existing works assumed that services are represented as Service Function Chains (SFCs) which are chains. However, network services in IoT networks are more complex and diverse, therefore, more appropriate representations are Virtualized Network Function Forwarding Graphs (VNF-FGs) which are Directed Acyclic Graphs (DAGs). Previous works failed to exploit this special graph structure, which makes them sub-optimal or non-applicable for IoT networks. In this paper, we investigate the VNF-FG placing problem in dynamic IoT networks where DAG-represented services arrive and depart. To fully exploit the graph structures of services and handle the complexity of dynamic IoT networks, we combine a novel neural network structure Graph Neural Network (GNN) with Deep Reinforcement Learning (DRL) and propose an efficient algorithm for VNF-FG placing, which is called Kolin. Extensive simulation results suggest that Kolin outperforms the state-of-the-art solutions in terms of system cost, acceptance ratio, and computation complexity.

In recent years, with the rapid proliferation of smart devices, Mobile Edge Computing (MEC) has been regarded as a promising technique that provides computing services in proximity to end-users. To improve the performance of MEC systems, Collaborative Edge Computing (CEC) is proposed to balance the load among cooperative edge servers. In practice, however, edge servers belong to different MEC service providers (SPs) and they have no incentive to help others. To encourage the cooperation between self-interested SPs, in this paper, we propose a profit-sharing incentive mechanism based on the Shapley value. In addition to the desirable properties such as efficiency and fairness, we also proved that our mechanism induces optimal offloading strategies and provides every SP an incentive to join the coalition. To protect the private information of SPs, we defined an aggregate profit function for each SP and showed that revealing this function is sufficient to calculate the profit allocation. Simulation results demonstrate that the system performance and SPs' revenue are substantially improved under cooperation.

Mobile-edge computing (MEC) is a new paradigm that provides cloud computing services at the edge of networks. To achieve better performance with limited computing resources, peer offloading between cooperative edge servers (e.g., MEC-enabled base stations) has been proposed as an effective technique to handle bursty and spatially imbalanced arrival of computation tasks. While various performance metrics of peer offloading policies have been considered in the literature, the worst case response time, a common quality of service (QoS) requirement in real-time applications, yet receives much less attention. To fill the gap, we formulate the peer offloading problem based on a stochastic arrival model and propose two online algorithms for cases with and without prior knowledge of task arrival rate. Our goal is to maximize the utility function of time-average throughput under constraints of energy consumption and worst case response time. Both theoretical analysis and numerical results show that our algorithms are able to produce close to optimal performance.

As one of the key concepts in the 5G network, MEC can support the latency-sensitive and compute-intensive services by widely deploying computing and storage capacity to the base stations at the network edge. Because these services are sensitive to latency, the joint optimization problem of task offloading and resource allocation needs to be solved in a short time. In this paper, we propose a Fast AI-assistant Solution for Task Offloading and Resource Allocation in MEC (FAST-RAM), which can directly solve the joint optimization problem leveraging a deep neural network. FAST-RAM can produce the offloading policy and resource allocation scheme in milliseconds. Meantime, our solution has near-optimal performance and sufficient feasibility under different network environments.

Network Function Virtualization (NFV) aims to provide a way to build agile and service-aware networks by building a new paradigm of provisioning network services where physical network functions are deployed as Virtual Network Functions (VNFs). However, how to optimally allocate resources in the uncertain NFV environment where flow rates fluctuate has not been fully resolved. In this paper, we study the cost minimizing problem of VNF placing and routing optimization while two goals are considered: optimizing service migration caused by flow fluctuation and stabilizing queue backlogs in the network. We first formulate the problem as a stochastic optimization programming problem. Then we propose an online algorithm, named Migration Aware VNF plaCing and Routing Online algorithm (MACRO), based on Lyapunov optimization technique. MACRO can make good decisions without knowing any future information. The theoretical analysis suggests that MACRO achieves an optimality gap of O([1/(V)]) and the queue backlogs are bounded by O(V), where V is a tunable parameter that controls the tradeoff between cost and backlogs. The experiment results suggest that MACRO achieves queue stability and outperforms benchmark algorithm by 6% in terms of cost.

Network Function Virtualization (NFV) provides a new paradigm for provisioning network service where network functions are deployed as Virtual Network Functions (VNFs). Due to the advantages of NFV, many Network Function Virtualization Providers (NFVPs) offer their NFV services by deploying VNFs with purchased cloud resources in cloud environment to save the provisioning expense. However, existing VNF provisioning solutions ignore the influences of the dynamics of cloud environment, which may lead to over-provisioning and high deployment expense. In this paper, we study the problem of how should the NFVPs purchase cloud resources to provide NFV services for customers in order to minimize the expense of NFVPs, considering the dynamics of the system. We first abstract the system model of this problem and formulate it as a stochastic optimization programming problem. Then, we present our VIrtual Network functiOn proviSioning (VINOS) approach that can efficiently solve the stochastic optimization programming with a rolling horizon procedure. In particular, it first leverages Long Short Term Memory (LSTM) networks to predict future exogenous information and then optimally solves a deterministic problem over short horizon. We conduct extensive numerical experiments to evaluate the proposed approach. The experiment results suggest that our approach achieves total cost of 1.2 times offline optimum, and outperforms the benchmark algorithm by 8%, averagely.

More and more applications learn from the data collected by the edge devices. Conventional learning methods, such as gathering all the raw data to train an ultimate model in a centralized way, or training a target model in a distributed manner under the parameter server framework, suffer a high communication cost. In this paper, we design Select Neighbors and Parameters (SNAP), a communication efficient distributed machine learning framework, to mitigate the communication cost. A distinct feature of SNAP is that the edge servers act as peers to each other. Specifically, in SNAP, every edge server hosts a copy of the global model, trains it with the local data, and periodically updates the local parameters based on the weighted sum of the parameters from its neighbors (i.e., peers) only (i.e., without pulling the parameters from all other edge servers). Different from most of the previous works on consensus optimization in which the weight matrix to update parameter values is predefined, we propose a scheme to optimize the weight matrix based on the network topology, and hence the convergence rate can be improved. Another key idea in SNAP is that only the parameters which have been changed significantly since the last iteration will be sent to the neighbors. Both theoretical analysis and simulations show that SNAP can achieve the same accuracy performance as the centralized training method. Compared to the state-of-the-art communication-aware distributed learning scheme TernGrad, SNAP incurs a significantly lower (99.6% lower) communication cost.

Software-Defined Networking (SDN) provides programmable, flexible and fine-grained traffic control capability, which paves the way for realizing dynamic and high-performance traffic measurement and traffic engineering. In the SDN paradigm, the traffic forwarding and measurement strategies are realized through flow tables stored in the Tenantry Content Addressable Memories (TCAM) of SDN switches. However, the number of TCAM entries in SDN switches is limited. In this paper, we aim to jointly optimize the Traffic Matrix Measurement (TMM) and Traffic Engineering (TE) process under the TCAM capacity and flow aggregation constraints in software-defined networks. We first formulate the joint optimization problem as a Mixed Integer Linear Programming (MILP) model. Then to get an initial traffic matrix for the joint optimization problem, we propose a simple flow rule generation strategy named Maximum Load Rule First (MLRF) to efficiently generate feasible flow rules, which are used to provide direct measurements for the traffic matrix measurement problem. At last, to solve the joint optimization efficiently, we propose two efficient heuristic algorithms named Traffic Matrix Measurement First (TMMF) and Traffic Engineering First (TEF), respectively. TMMF and TEF can generate feasible flow rules for realizing TMM and TE strategies. Our evaluations on real network topologies and traffic traces verify that by jointly optimizing the TMM and TE strategies, both TMMF and TEF can significantly improve TMM accuracy and TE objective (i.e., load balancing) with limited TCAM resource.

Edge computing is a promising technology that offers data analysis and computing for Internet of Things (IoT) services at the network edge. It has the potential to significantly reduce the latency and improve the reliability of IoT services by allowing computation workloads and local data generated by IoT devices to be offloaded to edge nodes. This paper aims to develop algorithms for efficient provision of both job assignment and resource allocation for edge computing networks. The main objective is to minimize the long-term average of the response time delay subject to constraints on computation resources and power consumption. We apply a drift-plus-penalty based Lyapunov optimization approach to convert the original problem into an upper bound optimization problem. We then relax the latter to a convex optimization problem. Finally, a distributed algorithm based on branch-and-bound approach is provided and the gap between the distributed algorithm solution and the optimal solution of the original problem is theoretically analyzed. Numerical results based on extensive experiments have demonstrated that our distributed algorithm can achieve the required performance of edge computing that supports IoT systems, under static traffic conditions as well as under dynamic environments with time-varying traffic.

Video streaming service has been consuming a massive amount of Internet traffic during recent years. Even though Dynamic Adaptive Streaming over HTTP (DASH) has become the mainstream technology for improving users' Quality of Experience (QoE), the competing of multiple independent DASH streams could degrade the QoE and make unfair resource allocation. With the support of Software Defined Networking (SDN), it is possible to jointly optimizing resource allocation and bitrate adaptation in this competing scenario. In this paper, we propose FAIR-AREA, a fast Artificial Intelligence based joint optimization of rate adaptation and resource allocation of DASH service. With FAIR-AREA, we can solve this complex optimization problem only in milliseconds and achieve near optimal performance at the same time.

Mobile edge computing (MEC) is an emerging paradigm which has drawn much attention from the academy and industry. Leveraging 5G technique, MEC provisions the ability to support the latency-sensitive and compute-intensive services by deploying computing and storage capacity at the network edge. As one of the critical problems in MEC, resource allocation problem needs to be solved within a very short time to satisfy the low latency requirement of services. In this paper, we propose an Accelerator for Resource allocation in MEC (ARM), which can directly solve the resource allocation problem based on deep neural network. With the aid of our scale-free representation scheme and feasible guarantee algorithm, ARM can solve the problem in milliseconds. Meanwhile, our algorithm achieves near 2-factor approximation to the optimal solution.

Network Function Virtualisation (NFV) aims to increase the deployment flexibility and integration of new network services with increased agility within operator's networks. Due to the promises of NFV, it is also considered as one of the building blocks for 5G and edge computing. However, the Intrinsic dynamic features of NFV and even rigorous requirements proposed by 5G and edge computing expose severe challenges to resource management in NFV. In this paper, We study the problem of Virtualised Network Function (VNF) chain placement in dynamic environment, and formulate it as Integer Linear Programming problem with taking the dynamic characteristics of resource allocation into consideration. Then we propose an efficient dynamic algorithm with provable competitive performance based on primal-dual approach combined with an efficient subroutine. The theoretic analysis shows our algorithm is (1 - 1/e)-competitive to offline optimal solution. Finally, We evaluate the proposed approach through extensive numerical simulations. Experiment results show that the proposed algorithm achieves near-optimal competitive ratio and has much better performances than several algorithms in many aspects.

Due to the hard constraint of measurement resources in switches, accurately and timely measuring a huge number of fine-grained flows is very challenging. To handle this challenge, we design FlowMap, which is a Bloom filter and hash-based approach to keep track of fine-grained flows with small bandwidth as well as computation and memory overheads. In each switch, FlowMap stores flow IDentifiers (IDs) in the FlowID table and encodes flow counters in the counting table with small memory space and constant operation time. To get the per-flow counters, FlowMap leverages the computing power of the remote controller to decode the encoded flow statistics collected from switches periodically. In addition, to achieve scalable flow counter decoding, FlowMap divides the cells of the counting table into several groups, and then uses a two-level flow mapping scheme to map flows to different groups, each of which can be decoded independently and concurrently at the remote controller. The simulation results show that FlowMap can provide per-flow counters with high accuracy for all the flows in short time scales with low overheads, and comparing with the existing approach, FlowMap is more scalable and robust.

Dynamic Adaptive Streaming over HTTP (DASH) has been broadly applied within most of mainstream video delivery services. At the same time, the lack of Quality of Experience (QoE) and the competition among several DASH streams have attracted significant attention. Aiming to supply better QoE for DASH streams in terms of video quality as well as starvation-free playing back, and achieve fairness among clients, we try to formulate the joint optimization of resource allocation and rate adaptation (JRA2) problem in this paper based on the information of streams' playout buffers and available network bandwidth. We first analyze the buffer behavior of a DASH stream using a modified version of M/D/1 queue and formulate the JRA2problem based on the analysis. To solve this problem, we propose an algorithm based on Generalize Benders Decomposition (GBD) to get optimal solution (JRA2-G) and devise one heuristic algorithm (JRA2-A) for acceleration. Based on the sufficient demonstrating results, the proposed algorithms can supply high-quality and smooth playing back for clients.

Computing frameworks have been widely deployed to support global-scale services. A job typically has multiple sequential stages, where each stage is further divided into multiple parallel tasks. We call the set of all the tasks in a stage of a job a cotask. In this paper, we aim to minimize the average Cotask Completion Time (CCT) in cotask scheduling. To the best of our knowledge, there is no prior work on cotask scheduling for cloud computing. We propose the Cotask Scheduling Scheme (CSS), and take MapReduce as a representative of computing frameworks. CSS schedules cotasks following the Minimum Completion Time First (MCTF) policy, and we prove this problem is NP-hard. We formulate the model using the Integer Linear Programming (ILP), and solve it through an efficient heuristics based on ILP relaxation. Through real trace based simulations, we show that CSS is able to reduce the average CCT by up to 62.20% and 69.93% with traces from our testbed and from a large production cluster respectively.

Hybrid Software-Defined Networking (HSDN) is a transitional networking form of SDN where SDN elements are partially deployed in traditional networks. Previous researches show that redirecting every flow of source-destination pair through at least one SDN switch can obtain flow manageability, e.g., access control and traffic measurement. Intuitively, the selection of SDN switch as the waypoint for every flow has a significant effect on the Traffic Engineering (TE) performance, such as maximum link utilization and routing efficiency. And it is worth noting that SDN switch can split traffic to the outgoing links to exactly profit the TE performance. In this paper, from the perspective of TE performance, we propose a flow routing and splitting (FRS) algorithm whereby we jointly determine an appropriate SDN switch for every flow as the waypoint, as well as optimizing the traffic splitting fractions for every SDN switch among its outgoing links to minimize the maximum link utilization. We conduct simulations with different SDN deployment rate. The results indicate that, when 20% of the SDN switches are deployed, the proposed FRS algorithm can obtain a lower maximum link utilization compared with other state-of-art works. Not only that, FRS algorithm can also generate a little longer paths for every flow on the average, which has a limited influence on the routing efficiency.

In-network content caching is an inherent capability of Content Centric Networking (CCN) architecture. Undoubtedly, efficient caching strategies can help CCN networks to achieve high performance content dissemination. In general, there are two types of caching strategies: collaborative and non-collaborative caching strategies. Compared with non-collaborative caching strategies, collaborative caching strategies have much better caching performance. However, collaborative caching strategies incur extra overhead (e.g., computation and communication). To make a trade-off between the performance and overhead, we propose a distributed lightweight collaborative in-network caching strategy in this paper, called Popularity Publishing based caching strategy (PopPub for short). PopPub uses a lightweight protocol to publish the content popularity statistics counted by edge routers to other routers, and caches different contents at different routers along the content delivery paths based on the popularities of the contents. To evaluate the performance of PopPub, we conduct both theoretical analysis and extensive simulations on different topologies. The evaluation results confirm that PopPub yields the best performance compared with several state-of-art caching strategies, and the extra overhead incurred by PopPub is low.

Content-Centric Networking (CCN) is a novel networking paradigm that treats the named contents, not the hosts, as the first-class citizens of the network. In the forwarding plane, CCN employs a stateful forwarding scheme, which maintains per-packet state information in Pending Interest Table (PIT). By employing stateful forwarding, CCN enables native support for content requests aggregation and multicast. However, the stateful forwarding scheme requires large-sized PITs with extremely high access speed to store per-packet state information, leading to scalability issue. To overcome the issue, this paper proposes a Hybrid forwarding scheme based on content POPularity (HyPOP) for CCN. HyPOP classifies the contents into popular and unpopular contents, and uses the stateful and Bloom Filter based stateless forwarding schemes to forward the popular and unpopular contents, respectively. The mathematical analysis results demonstrate that if PITs only store state information for popular contents, the small-sized PITs are sufficient for achieving satisfactory forwarding performance. Furthermore, the extensive simulation results also verify that HyPOP can reduce the size of PIT significantly and achieve promising forwarding performance.

In current data centers, an application (e.g., MapReduce, Dryad, search platform, etc.) usually generates a group of parallel flows to complete a job. These flows compose a coflow and only completing them all is meaningful to the application. Accordingly, minimizing the average Coflow Completion Time (CCT) becomes a critical objective of flow scheduling. However, achieving this goal in today's Data Center Networks (DCNs) is quite challenging, not only because the schedule problem is theoretically NP-hard, but also because it is tough to perform practical flow scheduling in large-scale DCNs. In this paper, we find that minimizing the average CCT of a set of coflows is equivalent to the well-known problem of minimizing the sum of completion times in a concurrent open shop. As there are abundant existing solutions for concurrent open shop, we open up a variety of techniques for coflow scheduling. Inspired by the best known result, we derive a 2-approximation algorithm for coflow scheduling, and further develop a decentralized coflow scheduling system, D-CAS, which avoids the system problems associated with current centralized proposals while addressing the performance challenges of decentralized suggestions. Trace-driven simulations indicate that D-CAS achieves a performance close to Varys, the state-of-the-art centralized method, and outperforms Baraat, the only existing decentralized method, significantly.

We revisit the problem of identifying link metrics from end- to-end path measurements in practical IP networks where shortest path routing is the norm. Previous solutions rely on explicit routing techniques (e.g., source routing or MPLS) to construct independent measurement paths for efficient link metric identification. However, most IP networks still adopt shortest path routing paradigm, while the explicit routing is not supported by most of the routers. Thus, this paper studies the link metric identification problem under shortest path routing constraints. To uniquely identify the link metrics, we need to place sufficient number of monitors into the network such that there exist $m$ (the number of links) linear independent shortest paths between the monitors. In this paper, we first formulate the problem as a mixed integer linear programming problem, and then to make the problem tractable in large networks, we propose a Monitor Placement and Measurement Path Selection (MP-MPS) algorithm that adheres to shortest path routing constraints. Extensive simulations on random and real networks show that the MP- MPS gets near-optimal solutions in small networks, and MP- MPS significantly outperforms a baseline solution in large networks.

In Software Defined Networks (SDN), applications on the controller could enforce fine-grained control on flows by policies employing more packet fields. These policies are converted to flow entries and stored in switch Flow Table. To store these entries, Flow Table requires large storage space because an entry consisted of more packet fields needs more storage space and the number of entries also increases significantly due to fine-granularity definition of flows. However, Flow Table has limited storage space owing to the constraints of Ternary Content Addressable Memory (TCAM). As a result, the switch Flow Table in SDN faces scalability issue. We address this issue by means of adaptive Flow Table management, namely we manage how long the entries occupy the storage space by setting adaptive timeouts to them. Through this means, the storage space could be reused efficiently and more flows could be supported with the same Flow Table (without updating hardware devices). Our proposed method TimeoutX, for the first time, combines traffic characteristics, flow types and Flow Table utilization ratio to decide the timeout of each entry and it outperforms current timeout setting strategies in both metrics of table miss number and blocked packet number, which indicates TimeoutX could make the best of Flow Table and support more flows.

In current data centers, an application (e.g. MapReduce) usually generates a collection of parallel flows sharing a common goal. These flows compose a coflow and only completing them all is meaningful. Accordingly, minimizing the average coflow completion time (CCT) becomes a critical objective for flow scheduling. In this topic, the state-of-the-art centralized method, Varys, achieves a good average CCT; but it has the scalability problem. Alternatively, the only existing decentralized method, Baraat, suffers from the head-of-line blocking problem. To solve these problems, we propose D-CAS, a preemptive, decentralized, coflow-aware scheduling system in this paper. D-CAS pursues coflow-level remaining-time-first (MRTF) principle by leveraging a simple negotiation mechanism between each coflow's data senders and receivers. As the MRTF principle is inherently preemptive and proven to be a near-optimal guideline to minimize average CCT, D-CAS avoids the head-of-line blocking problem and gets good performances. Through extensive simulations, we find that D-CAS achieves a performance close to Varys (gap <; 15%) and outperforms Baraat significantly (about 1.4-4×).

In the data flow models of today's data center applications such as MapReduce, Spark and Dryad, multiple flows can comprise a coflow group semantically. Only completing all flows in a coflow is meaningful to an application. To optimize application performance, routing and scheduling must be jointly considered at the level of a coflow rather than individual flows. However, prior solutions have significant limitation: they only consider scheduling, which is insufficient. To this end, we present Rapier, a coflow-aware network optimization framework that seamlessly integrates routing and scheduling for better application performance. Using a small-scale testbed implementation and large-scale simulations, we demonstrate that Rapier significantly reduces the average coflow completion time (CCT) by up to 79.30% compared to the state-of-the-art scheduling-only solution, and it is readily implementable with existing commodity switches.

In this paper, we study the multiobjective optimization problem for green network routing. Although traditional commonly used multiobjective optimization methods can yield a Pareto efficient solution, they need to construct an aggregate objective function (AOF) or model one objective as a constraint in the optimization problem formulation. As a result, it is difficult to achieve a fair tradeoff among all objectives. Accordingly, we induce a Nash bargaining framework, which treats the two objectives as two virtual players in a game theoretic model, who negotiate how traffic should be routed to optimize both objectives. During the negotiation, each of them announces its performance threat value to reduce its cost, so the model is regarded as a threat value game. Our analysis shows that no agreement can be achieved if each player sets its threat value selfishly. To avoid such a negotiation break-down, we modify the threat value game to have a repeated process and design a mechanism to not only guarantee an agreement, but also generate a fair solution. Finally, to evaluate the efficiency of our proposed framework, we implement it into two multiobjective optimization cases for network green routing. The first case is load balancing and energy efficiency optimization for intradomain routing, and the second one is the energy efficiency optimization of two domains for interdomain routing.

In Software Defined Networks (SDN), more packet fields are included to design fine-grained policies. These policies are stored as entries in switch Flow Table. However, fine-grained policies cause the scalability issue as a single flow entry needs larger storage space and a significant number of flow entries need to be stored, but the Flow Table is limited due to the constraints of Ternary Content Addressable Memory (TCAM). To address this issue, we propose Adaptive Hard Timeout Method (AHTM) to improve the Flow Table utilization by optimizing the timeouts of flow entries, thus the Flow Table is reused efficiently. AHTM models the Flow Table as a queueing system and derives closed-form formulas for analysis and optimization. We also implement AHTM as a light-weighted SDN application and it offers interfaces to other applications. The simulation results show that AHTM can achieve the balance between blocking probability and extra workload to SDN controller.

In this paper, we study how to manage the topology reconfiguration in OSA-based datacenter networks (DCNs). Though an OSA-based DCN can change its topology to adapt to the traffic matrix and improve the network scalability, it requires too much time (10ms) to reconfigure the topology, which may not only incur a great amount of traffic loss in high throughput low latency DCNs, but also bring much performance degradation to the delay sensitive flows. Therefore, a progressive topology reconfiguration scheme is required to reduce the traffic loss and guarantee the performance of delay sensitive flows. To this end, we first formulate the problem as a mathematical model, and then analyze its feasibility and complexity. Based on these analyses, topology management algorithm (TMA) is proposed to calculate the topology reconfiguration scheme that can maintain the topology connectivity during reconfiguration. By simulation, we find that TMA can reduce the traffic loss during topology reconfiguration by up to 50% in most of the cases and reconfigure topology without traffic loss in some cases.

We focus on the dynamic Routing and Spectrum Allocation (RSA) problem in EONs with mixed line rates. To solve the dynamic RSA problem efficiently, we decompose the problem into routing and spectrum allocation sub-problems. For the routing sub-problem, we propose an efficient multi-constrained routing algorithm, Sorted Feasible Paths Searching (SFPS), to find the shortest feasible paths for the dynamic traffic demands. For the spectrum allocation sub-problem, we propose a spectrum allocation strategy named Adaptive Segmentation (AS) to allocate spectrum for the non-commensurate traffic demands of EONs with mixed line rates. Simulation results prove that the proposed dynamic RSA algorithm is time-efficient and perform better than existing dynamic RSA algorithms in terms of bandwidth blocking probability and spectrum fragmentation ratio.

IP Fast Reroute (IP FRR) is the IETF standard for providing fast reaction to failures in IP and MPLS/LDP networks. In the past decade, several IP FRR proposals have been proposed, and among them Loop-Free Alternates (LFA) is the simplest, but it cannot achieve 100% single failure coverage. In contrast, Maximally Redundant Trees (MRT) can provide 100% single failure coverage and seems a promising scheme. However, MRT has some drawbacks as it can lead to long backup detours and heavy network congestion. In this paper we combine MRT with LFA to merge their advantages and improve the quality of protection. Observations of performance evaluation suggest that with almost same time and resource consumption as MRT, our mechanism can greatly enhance the quality of protection in terms of backup path length and maximum link utilization.

Energy saving in the Internet has become important since the massive new applications are increasing tremendously, along with their energy consumption. In this paper we propose an energy efficient algorithm based on multi-topology routing. Since traffic flow dynamically changed in actual IP network scenarios, the proposed algorithm has to adapt the network energy consumption to daily traffic scenarios. The proposed algorithm is a three-phases algorithm: first, one day is divided into multiple time periods according to the daily traffic flow; second, in each time period, a corresponding energy efficient sub-topology is obtained by utilizing neighboring region search to configure link weights and switch off low loaded links; last, to improve network performance (such as packet loss rate) during energy efficient sub-topology changes in different time periods, an IP fast rerouting strategy is proposed. Simulation results show that the proposed algorithm has a better performance in energy efficiency than current energy efficient algorithm, and improve the network performance during time period changes.

In-network caching as one of the primary components for ICN (information-centric network) has attracted more and more attentions. In this paper, we present a profit-based caching for ICN. The profit value for the content arriving on a router is determined by request popularity, distance to content source, content size and content duration, etc. The content duration is considered as an important factor in methodology to avoid the error caused by storing the overdue contents, which is the main difference from existing caching scheme. A 0-1 ILP (integer linear programming) is used to formulate whether to cache the coming content or not and eviction objects simultaneously. Also, a near-optimal heuristic algorithm is proposed to find profit-efficient cache decision, which can be quickly deployed. The analytical and simulation results show our profit-based caching scheme can attain a better network profit compare to Least Frequently used (LFU), and totally avoids caching withdrawing contents.

In this paper, we study the tradeoff between two important traffic engineering objectives: load balance and energy efficiency. Although traditional commonly used multi-objective optimization methods can yield a Pareto efficient solution, they need to construct an aggregate objective function (AOF) or model one of the two objectives as a constraint in the optimization problem formulation. As a result, it is difficult to achieve a fair tradeoff between these two objectives. Accordingly, we induce a Nash bargaining framework which treats the two objectives as two virtual players in a game theoretic model, who negotiate how traffic should be routed in order to optimize both objectives. During the negotiation, each of them announces its performance threat value to reduce its cost, so the model is regarded as a threat value game. Our analysis shows that no agreement can be achieved if each player sets its threat value selfishly. To avoid such a negotiation break-down, we modify the threat value game to have a repeated process and design a mechanism to not only guarantee an agreement, but also generate a fair solution. In addition, the insights from this work are also useful for achieving a fair tradeoff in other multi-objective optimization problems.

The concept of monitoring trail (m-trail) provides a striking mechanism for fast and unambiguous link failure localization in all-optical networks. To achieve fast m-trail design in large-size networks, two efficient heuristics RCA+RCS and MTA are proposed against the optimal ILP (Integer Linear Program) model. However, RCA+RCS suffers from the disjoint trail problem which increases the required number of m-trails, and MTA always finds a deterministic solution which may not be good enough due to the limited solution space. In this paper, we propose a new heuristic RNH-MTA (Monitoring Trail Allocation with the Random Next Hop policy) to solve those issues. Similar to MTA, RNH-MTA ensures a valid optical structure of each m-trail and sequentially adds necessary m-trails to the solution, and thus is free of the disjoint trail problem. By replacing the deterministic searching in MTA using the Random Next Hop policy, RNH-MTA sets up a probabilistic model in extending each m-trail. This not only enlarges the solution space and increases the solution diversity, but also enables a controllable tradeoff between the solution quality and the running time of the algorithm. Our numerical results show the advantages of RNH-MTA over both RCA+RCS and MTA.

LISP (Locator/Identifier Separation Protocol) is proposed to address the routing scalability problem of current Internet, and a mapping system is required to support the LISP EID-to-RLOC (Endpoint Identifier to Routing Locator) mapping services. In this paper we suggest ERMA (EID-to-RLOC Mapping Assignment) of local network could be tuned to specify the ingress points of inbound traffic, which is helpful for improving the network resource utilization in stub domains. One Mixed Integer Linear Programming model is proposed for ERMA-only optimization in the network with given link weights; another model is formulated for the joint optimization of ERMA and link weights. To make the joint optimization problem tractable, one local search algorithm, Optimized Stepsize Algorithm, is proposed. Our numerical results show that the maximum link utilization decreased by tuning ERMA in both cases.

We study the m-trail (monitoring trail) allocation problem in all-optical WDM mesh networks for achieving fast and unambiguous link failure localization. The existing ILP is not feasible for solving the problem in large-size networks. A heuristic RCA+RCS can find feasible solutions in a shorter running time, but it is a randomized algorithm. More importantly, RCA+RCS suffers from the disjoint trail problem which dramatically increases the number of required monitors in large-size networks. In this paper, we propose a new heuristic MTA (Monitoring Trail Allocation) to solve the problem. MTA avoids those issues in RCA+RCS, and achieves an efficient tradeoff between monitor cost and bandwidth cost. Compared with RCA+RCS, MTA greatly shortens the running time and achieves a much higher solution quality. We also show that MTA provides a flexible framework to enable multiple possible variations for future study.

Intra-domain traffic engineering can significantly enhance the performance of large IP backbone networks. An important component of current methods for traffic engineering with link state routing protocols like OSPF is accurate knowledge of traffic matrix. However, the traffic matrix is unknown and varies with time. So it is important to obtain a traffic engineering method that is "robust" to variations in traffic matrix. In this paper, we use multi-topology routing (MTR) for providing robust traffic engineering in IP networks. We first formulate the problem of robust traffic engineering using MTR as a mixed integer programming problem (MIP). To make the problem solvable, we then decompose the problem into logical topologies design problem and traffic assignment problem, which are solved by a heuristic algorithm and a tractable linear programming (LP) model, respectively. Using simulation results on Rocketfuel topologies, we study and discuss effectiveness of the proposed robust traffic engineering approach. Simulation results show that robust traffic engineering based on MTR is promising.

An important traffic engineering problem for OSPF networks is the determination of optimal link weights. In this paper, we assume that the traffic matrix, which specifies traffic load between every source-destination pair in the network, is unknown and varies with time, but that always lies inside an explicitly defined region. Our goal is to compute an optimal link weights that minimizes maximum link utilization for all traffic matrices inside the bounding region. We first present a mixed-integer programming formulation to compute the optimal link weights. We then present a heuristic algorithm to find the optimal weights. Our simulations show that the proposed algorithm not only performs better than existing weight setting schemes in terms of minimizing congestion ratio, but also can achieve solution which is close to the optimal solutions.

We address the issue of traffic-oblivious routing (i.e., robust routing) in WDM networks dealing with both link and node failures under load-balancing architectures. Two distinct schemes are proposed. One is static with the goal of minimizing total network cost given a set of multiple failures. The other considers dynamic network environment (i.e., connection requests arrive one after another), and is designed against the failures aiming at the maximum network throughput. We manifest by simulation that both of these two schemes perform better than the previously proposed protection schemes as well as the unprotected ones.

As multicast applications become popular, provisioning survivable multicast connections in WDM networks is an important issue. In this paper, we study the problem of multicast protection in sparse-splitting WDM networks, and propose an efficient protection algorithm called sparse-splitting constrained multicast protection (SSMP) algorithm. Differing from previous works, the backup paths derived by SSMP can share wavelengths with primary tree in sparse-splitting WDM networks. To achieve wavelength sharing between primary tree and backup paths, a layered graph model is developed. Simulation results show that SSMP can achieve better performance in terms of average network cost and blocking probability than existing algorithms.

In this work, we investigate the problem of protecting dynamic multicast sessions in mesh WDM networks against single link failures. We propose two efficient multicast session protecting algorithms, called optimal path pair based removing residual links (OPPRRL) and source leaf path based avoiding residual links (SLPARL), which try to reduce the usage of network resource by removing or avoiding residual links in the topology consists of "light-tree " and its backup paths. We compare the proposed algorithms with existing algorithms through simulation. Simulation results indicate that the two proposed algorithms have better performance than other existing algorithms in terms of wavelength links required and network blocking probability.

In this paper we present a novel efficient energy-aware approach for clustering nodes in wireless sensor networks. We use different cluster head (CH) declaration delays for each node to characterize the qualification to be a CH. The approach guarantees the fairly uniform cluster distribution while incurring low overheads. Additionally, we do not make any assumptions about the distribution or node capabilities, e.g., location-awareness. The simulation results show that our clustering approach outperforms LEACH both in cluster characteristics and in the efficiency of prolonging the network lifetime.

The advances in wavelength-division multiplexing (WDM) technology are expected to facilitate bandwidth-intensive multicast applications. A single fiber failure in such a network, however, can disrupt the information dissemination to several destination nodes in a "Iight-tree"-based multicast session. Thus it is imperative to protect the multicast sessions. In this paper, we propose a novel protection scheme, called multicast protection through spanning paths (MPSP), for resource efficient multicast protection with spare capacity sharing. Here, a spanning path is a path from a leaf node to any other leaf node of a multicast tree. The key idea of MPSP is first to identify a backup path for each spanning path and then to appropriately select parts of these backup paths to protect the primary multicast tree, so that the total bandwidth allocated to the primary multicast tree and its protection paths (or trees, etc.) is minimized. While previous studies only consider self-sharing and intra-request sharing, to the best of our knowledge, this is the first time to take inter-request sharing of spare capacity into consideration when protecting dynamic multicast sessions. We use simulations to demonstrate the performance of the MPSP scheme. It is shown that significant performance improvements are achieved in terms of average cost per multicast session and blocking probability. Compared with existing schemes, the average cost is reduced by about 22% and the blocking probability can be reduced by about 27% in average.

The advances in wavelength-division multiplexing (WDM) technology are expected to facilitate bandwidth-intensive multicast applications. A single fiber cut on such a network, however, can disrupt the transmission of information to several destination nodes on a "light-tree"- based multicast session. Thus it is imperative to protect multicast sessions. In this article we propose a novel protection scheme called multicast protection through spanning paths (MPSP) for efficient multicast protection. Here, a spanning path is a path from a leaf node to any other leaf node of a multicast tree. The key idea of MPSP is to derive a backup path for each spanning path and then appropriately select part of these backup paths to protect the primary multicast tree such that the total bandwidth allocated to the primary multicast session and its protection paths (or trees, etc.) for all the multicast sessions is minimized. Simulation results are used to demonstrate the good performance of the proposed protection scheme in reducing the spare capacity for protection. Compared with existing schemes, the decrease in average cost can be quite high (e.g., decrease about 20%) and the blocking probability can be reduced approximately 15%.

A new heuristic algorithm, called virtual topology mapping for design protection based on layered graph (LG VTMDP), used in design protection for WDM optical networks is proposed in this paper. The algorithm considers the problem of routing and wavelength assignment simultaneously as well as design protection. Load balancing and capacity constraints of physical links are also considered. The LG VTMDP algorithm is shown to perform better than the combination of the disjoint alternate path (DAP) algorithm and existing wavelength assignment algorithms. Then an efficient wavelength converter placement (WCP) algorithm is presented. By only placing a small number of wavelength converters at some "key" nodes, this sparse conversion scheme can obtain sufficiently high performance. Finally, the performance of our algorithms is studied using network examples.

This paper studies multihop optical networks in which nodes employ wavelength routing switches that enable the establishment of wavelength division multiplexed (WDM) channels, called lightpaths, between node pairs. In fact, most optical networks are multifiber networks. The problem of dynamical routing and assignment of wavelength (RAW) in such networks is studied in this paper. Two resource assignment strategies, PACK and SPREAD, are proposed. By virtue of a layered-graph, routing and assignment of wavelength subproblems can be considered simultaneously. These two strategies can be used to solve the RAW problem in networks with even links as well as that in networks with uneven links. Simulation shows that layered-graph-based RAW algorithms perform better than the existing ones. It also shows that SPREAD with distributive use of network resources can achieve better performance than PACK with collective use of resources in multifiber networks. The layered-graph-based algorithms can effectively deal with the failure of the fiber/link and node. By making use of the special structure of the layered-graph, we propose a shortest path algorithm, whose complexity is lower than that of the standard shortest path algorithm.

Two algorithms are proposed for the dynamic routing and assignment of wavelength problem in multi-fiber wavelength division multiplexing all-optical networks. By virtue of the layered graph, the routing and assignment of wavelength subproblems can be considered simultaneously. Simulation shows that layered-graph-based RAW algorithms perform better than the existing ones. Making use of the special structure of the layered graph, we propose a shortest path algorithm, whose complexity is lower than that of the standard shortest path algorithms.