The objective of congestion control is to maximize network bandwidth utilization and minimize the average flow completion time in data center networks. Key performance indicators required to achieve this objective are high throughput and low packet latency. Existing approaches have proposed various methods to control packet delivery based on various network events or parameters such as packet loss, bottleneck bandwidth, RTT, and queue length. However, these methods often result in suboptimal packet transmission states that compromise throughput or latency. We provide a method to overcome this drawback. To this end, we introduce a congestion control method called PRTT (Predicted RTT) that leverages predicted RTT for congestion control. By using accurately predicted RTT values, PRTT dynamically adjusts the packet delivery rate to control the number of in-flight packets. This enables the transmission to approach states where the buffer holds only a few packets while fully utilizing the link bandwidth. Experimental results show that PRTT achieves higher throughput, lower latency, and shorter flow completion times than other state-of-the-art methods, particularly under bursty traffic. These results demonstrate that PRTT offers a promising solution for congestion control.

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.

The paper addresses the challenge of scheduling multiple output priority queues of a switch in a real-world setting characterized by bursty traffic and diverse traffic priorities. Existing queue scheduling methods primarily employ two types of strategies: priority-based scheduling and weight-based scheduling. However, there is a lack of scheduling methods that can simultaneously handle bursty traffic in a timely manner and maintain priority-based scheduling. This paper addresses this issue by formulating the problem as a restless multi-armed bandit problem, and a queue scheduling method is proposed to balance priority service provisioning and bursty traffic processing. The proposed queue scheduling method operates efficiently in a timely manner based on instant queue length and utilizes the Whittle index method to achieve an asymptotically optimal solution. This design facilitates packet forwarding by considering the instant states of queues, offering improvements over existing methods. Experimental results demonstrate that the proposed method achieves a more efficient balance between priority service provisioning and bursty traffic processing compared to other state-of-the-art methods. Additionally, the proposed method results in better balanced network performance metrics, such as queue length, packet delay, and packet loss, thus efficiently supporting various applications that generate bursty traffic randomly.

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 %.

It is crucial to automatically detect anomalous patterns in system logs to protect computer systems from cyber attacks and malfunctions. However, as log data is becoming increasingly complex and labeled logs are difficult to obtain, it poses serious challenges to existing methods. To this end, this paper introduces the pre-training and fine-tuning paradigm to the log analysis domain and proposes a novel log anomaly detection framework. We propose the masked log reconstruction approach to pre-train a Transformer-based foundation model and fine-tune it for the event prediction task to obtain the anomaly detector. Our training methods exploit the sequential information within unlabeled logs with self-supervised learning. Experimental results on two public datasets demonstrate the performance superiority of our framework compared with existing state-of-the-art methods. More importantly, it is suitable for real-world scenarios where labeled logs are difficult to acquire.

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.

Federated Learning (FL) is a distributed machine learning method that ensures the privacy and security of participants’ data by avoiding direct data upload to a central node for training. However, the traditional FL typically applies a star structure with cloud servers as the central aggregator for the model parameters from different terminals, leading to problems such as central failure, malicious tampering and malicious participants, resulting in training errors or system crashes. To address these issues, a permissioned blockchain is used to build a secure and reliable data-sharing platform among participating terminals, replacing the central aggregator in the traditional FL called blockchain-based federated learning. However, the block generation method of the blockchain system may introduce significant latency in the federated learning where distributed model parameters upload randomly, resulting in low efficiency of the federated learning. To overcome this, we propose a block generation strategy that groups terminals and generates a block for each group, which minimizes the latency of a single round of federated learning, and an optimal block generation algorithm that considers data distribution, terminal resources, and network resources is provided. The analysis shows that the proposed algorithm can effectively obtain the optimal solution of block generation to minimize the authentication time, and we conduct extensive experiments that demonstrate the time efficiency of the proposed algorithm.

The fast anomaly detection and localization is essential for network management, however, it is also very challenging for the current networks due to the lack of flexible control and telemetry capabilities. Fortunately, the maturity of Deep Reinforcement Learning (DRL) and programmable networking technologies could shed a light on realizing fast and intelligent anomaly detection and localization. In the paper, we design a fast anomaly detection and localization system for programmable networks by leveraging the in-band network telemetry and flexible control capabilities of programmable networks. Based on the system, we propose a DRL-based abnormal link detection and localization algorithm. It can iteratively infer abnormal links based on the ingress-to-egress performance metrics of flows and the one-hop performance metrics of the flows on the already identified abnormal links. The simulation results show that our proposals can detect and localize link anomalies in a matter of seconds to tens of seconds with low network telemetry overhead.

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.

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.

Fine-grained and accurate network flow measurements are essential for various network management tasks. In recent years, the evolution of programmable networks enables flow measurement on the switch. However, limited hardware resources on programmable switches drive the shift of measurement from a single switch to network-wide coordinations. This paper aims to optimize the allocation strategy of flow measurement among switches under the objective of measurement coverage and accuracy in network-wide measurement scenarios. We design a Graph Neural Network model, NeuralMon, that can model and solve the above problem precisely. NeuralMon converts network topologies and network flows into a hypergraph and transforms the flow measurement task allocation problem into a node classification problem. NeuralMon is effective in learning the task allocation solution from the network topologies and flows directly. Even on untrained real-world network topologies, NeuralMon still provides excellent performance.

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.

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.

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.

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.

As Network Function Virtualization (NFV) becomes reality and cloud computing offers a scalable pay-as-you-go charging model, more network operators would like to outsource their Service Function Chains (SFC) to the public clouds in order to reduce the operational cost. However, how to minimize the operational cost with Quality of Service (QoS) guarantee when outsourcing SFC is still an open problem. In this paper, we are to study this problem when there are large number of candidate cloud providers with diverse pricing schemes of network functions. In addition, extra delay is introduced as the result of outsourcing SFCs. Firstly, we formulate this problem as an Integer Linear Programming (ILP) model. Then we design an efficient heuristic algorithm named QoS-Guaranteed SFC Outsourcing algorithm (QGSO) based on Hidden Markov Model (HMM). The extensive simulations show that QGSO saves up to 75.8% cost compared with that of deploying network functions in local network. QGSO also achieves up to 42.6% cost savings compared with the result of first-fit based optimization algorithm.

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.

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.

A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the hard constraints of network measurement resources. This paper proposes a new framework, SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can leads to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications including per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring 8.8\% of per-flow path delays in Harvard network, congested paths can be detected with probability 0.94. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet.

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.

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.

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.

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.