With the rapid development of Artificial Intelligence Generated Content (AIGC), single data centers are increasingly unable to meet the growing demands for data and computational resources in distributed large-scale language model (LLM) training. In this context, distributed training across heterogeneous data centers has become a necessary choice to enhance computational power and flexibility. However, the networks in heterogeneous data centers are polymorphic, with diverse communication protocols and network architectures. This heterogeneity renders traditional routing devices ineffective in recognizing and processing gradient data. Moreover, frequent copying and excessive parsing of gradient data by routing devices across heterogeneous data centers significantly increase model training time. To address these challenges, we propose XAForward, a method for accelerating distributed LLM in heterogeneous data centers using eXpress Data Path (XDP). Specifically, XAForward introduces a polymorphic-compatible protocol that reconstructs the header of gradient data packets to enable efficient data forwarding across different communication protocols in heterogeneous data centers. Additionally, to accelerate distributed LLM computing and reduce gradient data copying and excessive parsing during training, XAForward leverages kernel-bypass techniques based on XDP for packet processing and kernel-level data forwarding using network index identifiers. Experimental results show that, compared to state-of-the-art methods, XAForward reduces the distributed LLM training time by approximately 35% to 40%.

As model sizes continue to grow and the capacity of a single data center becomes insufficient, training models across multiple data centers efficiently is becoming increasingly important. In this paper, we first show that existing parallelism strategies perform poorly under limited bandwidth and high latency of cross-DC links. To address this, we propose JEEVES, a framework that extends the pipeline parallelism across DCs to minimize iteration time under memory constraints. We identify that the key lies in a good schedule of computation and communication, and propose communication-aware schedule, memory-aware stage division and inter-replica coordinated schedule. Simulations show that JEEVES improves iteration time by up to 43% when training a 175B-parameter model.

Segment Routing over IPv6 (SRv6) is a promising source routing solution with wide-ranging applications in the field of Traffic Engineering. By adding SR tags into IPv6 packets, traffic can be directed to various SR segments, effectively distributing traffic. However, upgrading all network nodes to SRv6 nodes simultaneously is impractical. This paper addresses the incremental deployment of SRv6 from traffic engineering, aiming to minimize MLU within the network. We introduce the theory of celestial equilibrium and model the problem as a celestial equilibrium-like model with a global distribution of SR nodes and their corresponding areas of influence. To address this problem model, we propose a novel algorithm based on the EM algorithm, EM-SRTE. In our proposed framework, step E leverages a reinforcement learning algorithm combined with a self-attention module for graph learning to optimize the selection of SR nodes. Meanwhile, step M utilizes a similar algorithm to optimize the region range of SR nodes. Experimental results using publicly available datasets demonstrate that our model outperforms state-of-the-art baselines.

RDMA is promising for enhancing the performance of cross-datacenter (DC) services. However, deploying RDMA over wide-area networks introduces severe congestion control unfairness, primarily due to asymmetric congestion feedback delays between inter-DC flows and intra-DC flows. As a result, intra-DC flows often bear the full burden of congestion response, leading to drastically increased flow completion times (FCT). In this work, we identify two key forms of unfairness — near-source and near-destination — depending on whether congestion occurs near the sender or receiver of inter-DC flows. Based on this, we propose THEMIS, a fairness maintenance patch for long-haul RDMA networks. To mitigate near-source unfairness, THEMIS devises a Proactive Notification Point to shorten the congestion feedback loop within a single DC. To alleviate near-destination unfairness, THEMIS introduces a Temporary Reaction Point to temporarily slow down the target inter-DC flow until the sender receives the corresponding congestion feedback. We implement an open-source prototype of THEMIS, and evaluate it on both real-world testbed and large-scale simulations. Compared to DCQCN, Annulus and BiCC, THEMIS reduces the intra-DC FCT by up to 79.2%, 63.6% and 55.6%, and decreases overall FCT by up to 61.2%, 31.9% and 59.5% respectively.

The rise of Embodied AI, including autonomous robots, cooperative autonomous driving, and augmented reality agents, is driving a deep integration of intelligent systems with the physical world, imposing stringent demands on the underlying network for real-time, low-latency interaction. However, these Embodied AI systems typically operate in complex polymorphic network environments, simultaneously handling heterogeneous identifiers such as content, IP, and geographic location. This causes traditional routing mechanisms to suffer from significant latency overhead and compatibility bottlenecks due to protocol conversion and adaptation, severely limiting the performance and responsiveness of Embodied AI applications. To address this challenge, we propose the Polymorphic Compatible Segment Routing (PCSR) protocol. PCSR adopts an innovative paradigm of decoupling the protocol from the infrastructure. It dynamically maps native protocol semantics to lightweight 8 -byte identifiers and utilizes compatibility logic encapsulated at the packet tail to achieve smooth compatibility with traditional networks without requiring large-scale modification of existing equipment. Furthermore, we built a zero-copy forwarding engine using the kernel eXpress Data Path (XDP) technology to fundamentally optimize data transmission efficiency. Experimental validation on a 10-node heterogeneous testbed shows that PCSR reduces end-toend latency by 32.7% and maintains a high throughput rate in hybrid network environments. This work demonstrates that PCSR provides an efficient and deployable routing solution for latency-critical, cross-domain collaborative services required by Embodied AI.

Traffic engineering (TE) has attracted extensive attention over the years. Operators expect to design a TE scheme that accommodates traffic dynamics well and achieves good TE performance with little overhead. Some approaches like oblivious routing compute an optimal static routing based on a large traffic matrix (TM) range, which usually leads to much performance loss. Many approaches compute routing solutions based on one or a few representative TMs obtained from observed historical TMs. However, they may suffer from performance degradation for unexpected TMs and usually induce much overhead of system operating. In this paper, we propose ALTE, an adaptive and low-cost TE scheme based on TM classification. We develop a novel clustering algorithm to properly group a set of historical TMs into several clusters and compute a candidate routing solution for each TM cluster. A machine learning classifier is trained to infer the proper candidate routing solution online based on the features extracted from some easily measured statistics. We implement a system prototype of ALTE and do extensive simulations and experiments using both real and synthetic traffic traces. The results show that ALTE achieves near-optimal performance for dynamic traffic and introduces little overhead of routing updates.

The Domain Name System (DNS) is a growing center of cyber attacks, including both volumetric and non-volumetric attacks. Programmable switches provide a new opportunity for more efficient defense against DNS attacks since they can offer better cost, performance, and flexibility trade-offs compared to traditional defense systems. However, programmable switches have strict limitations on the operations and storage space supported to ensure line-speed packet processing. In this paper, we propose DNSGuard, an intelligent in-network defense framework that can handle volumetric and non-volumetric DNS attacks on programmable switches. We propose a recursive incremental parsing algorithm that can effectively extract variable-length domain names. To achieve real-time and accurate detection against two types of DNS attacks, we design a switch-optimized and resource-efficient algorithm to extract both independent features of each packet and domain-based cumulative features. Then, we propose a multi-phase hybrid model architecture to perform dynamic packet analysis at different time phases of a domain. Further, we design efficient model representation mechanisms to deploy tree-based ensemble models in the data plane. Experimental results show that DNSGuard can defend against diverse DNS attacks at the line rate. In addition, DNSGuard introduces a minimal nanosecond latency to normal traffic in heavily loaded networks.

Stateful multicast protocols manage multicast group memberships by maintaining state information about active groups and their members. They have seen limited adoption in the modern internet due to lack of scalability, simplicity, and flexibility. Although stateless multicast protocols, like BIER, eliminate extensive state management, they still face complex tree computation and limited scalability for concurrent requests. In this paper, we propose Hawkeye, a stateless multicast mechanism with deep reinforcement learning (DRL) for real-time responses to dynamic multicast requests with near-optimal multicast TE performance. This mechanism is suited for Software-Defined Networking (SDN) environment where the controller has a global view of the network and supports flexible configuration of network resources for traffic engineering. For real-time responses to multicast requests, we leverage DRL enhanced by a temporal convolutional network (TCN) to model the sequential feature of dynamic group membership, and thus are able to build multicast trees proactively for upcoming requests. We develop a novel source aggregation mechanism to facilitate the convergence of the DRL agent under high volume of multicast requests. Moreover, to improve the practicality and robustness of Hawkeye, we design incremental deployment and single failure handling mechanisms, which take advantages of source aggregation and fit well with multicast routing. Evaluation with real-world topologies and multicast requests demonstrates that Hawkeye responds effectively to dynamic multicast requests. Itoffers rapid routing decisions, e.g., making routing decisions in under 5ms on a tested topology, and reduces path latency variation by up to 89.5%, with less than a 10% increase in bandwidth consumption compared to the offline theoretical minimum.

In the age of 5G and the advent of 6G technology, meeting various applications' diverse and stringent QoS requirements has gained greater urgency. Most existing works only address this problem by optimizing network resource allocation or routing generation, ignoring their mutual influence. In this paper, we jointly optimize resource allocation and routing generation while comprehensively considering the mutual influence and demonstrating that joint optimization is a bilevel optimization problem. We propose BIJO, consisting of two-level agents, to explore using DRL to address the bilevel optimization problem. The upper agent allocates resources for different types of services and the lower agents choose the forwarding path for each type of service. The two-level agents are optimized interactively and iteratively. Extensive experiments on Mininet confirm that BIJO provides a win-win outcome, where various requirements are met as much as possible and network resources are utilized evenly.

Ternary Content Addressable Memory (TCAM) enables fast lookup and is widely used by routers and switches to support policy-based forwarding. Due to high cost and small capacity, only a small subset of important rules can be cached in TCAM, so determining it is critical to increasing the hit ratio. This is more challenging than traditional caching problems because of complicated rule dependency relationships. Existing works are based on heuristics and they don’t work well under all practical scenarios. Worse still, the lack of fundamental understanding of the design space, complexity, and optimality makes all explorations in mystery. In this paper, we use a modeling-based method to formulate the problem, prove its complexity, and propose DROPS, a dynamic rule caching framework with a much higher hit ratio. In particular, we deduce the rule selection problem into a multi-dimensional rule space transformation problem. Thus, we are no longer limited by using the intrinsic rules; rather, we can transform original rules into “new rules” equivalently without rule dependency. We design non-trivial rule placement and update algorithms and implement them in programmable switches. In the experimental evaluation, we show that our method outperforms all existing methods.

Border Gateway Protocol (BGP) is the only inter-domain routing protocol that plays an important role on the Internet. However, BGP suffers from route leaks, which can cause serious security threats. To mitigate the effects of route leaks, accurate and timely route leak locating is of great importance. Prior studies leverage AS business relationships to locate route leaks in real time. However, they fail to achieve high locating accuracy. Recent studies apply machine learning to accurately detect route leaks from statistical features of massive BGP messages. Nevertheless, they have high detection latency and cannot further locate route leaks. In this paper, we propose a real-time and accurate route leak locating system named RoLL+. It leverages distinctive AS triplet features to accurately locate AS triplets with route leaks from each BGP message in real time. Considering that RoLL+ may receive a substantial volume of BGP update messages per second, we integrate a cache-like design and a lazy update mechanism into the system to effectively identify route leaks at scale. Our experimental results on real-world BGP route leak data demonstrate that it can achieve 92% locating accuracy with less than 1 ms locating latency. Furthermore, the results show that RoLL+ can process over 7,000 AS triplets per second, meeting real-world throughput requirements.

Robust network design, which aims to guarantee network availability under various failure scenarios while optimizing performance/cost objectives, has received significant attention. Existing approaches often rely on model-based mixed-integer optimization that is hard to scale or employ deep learning to solve specific engineering problems yet with limited generalizability. In this paper, we show that failure evaluation provides a common kernel to improve the tractability and scalability of existing solutions. By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design. FERN represents rich problem inputs as a graph and captures both local and global views by attentively performing feature extraction from the graph. It enables a broad range of robust network design problems, including robust network validation, network upgrade optimization, and fault-tolerant traffic engineering that are discussed in this paper, to be recasted with respect to the common kernel and thus computed efficiently using neural networks and over a small set of critical failure scenarios. Extensive experiments on real-world network topologies show that FERN can efficiently and accurately identify key failure scenarios for both OSPF and optimal routing scheme, and generalizes well to different topologies and input traffic patterns. It can speed up multiple robust network design problems by more than 80x, 200x, 10x, respectively with negligible performance gap.

Given the high packet processing efficiency of programmable switches (e.g., P4 switches of Tbps), several works are proposed to offload the decision tree (DT) to P4 switches for in-network classification. Although the DT is suitable for the match-action paradigm in P4 switches, the range match rules used in the DT may not be supported across devices of different P4 standards. Additionally, emerging models including neural networks (NNs) and ensemble models, have shown their superior performance in networking tasks. But their sophisticated operations pose new challenges to the deployment of these models in switches. In this paper, we propose Mousikav2 to address these drawbacks successfully. First, we design a new tree model, i.e., the binary decision tree (BDT). Unlike the DT, our BDT consists of classification rules in the form of bits, which is a good fit for the standard ternary match supported by different hardware/software switches. Second, we introduce a teacher-student knowledge distillation architecture in Mousikav2, which enables the general transfer from other sophisticated models to the BDT. Through this transfer, sophisticated models are indirectly deployed in switches to avoid switch constraints. Finally, a lightweight P4 program is developed to perform classification tasks in switches with the BDT after knowledge distillation. Experiments on three networking tasks and three commodity switches show that Mousikav2 not only improves the classification accuracy by 3.27%, but also reduces the switch stage and memory usage by$2.00\times $and 28.67%, respectively. Code is available athttps://github.com/xgr19/Mousika.

IPv6 has shown notable growth in recent years, imposing the need for high-speed IPv6 lookup. As the forwarding rate of virtual switches continues increasing, software-based IPv6 lookup without using special hardware such as TCAM, GPU, and FPGA is of academic interest and industrial importance. Existing studies achieve fast software IPv4 lookup by reducing the operation number, as well as reducing the memory footprint to benefit from CPU cache. However, in the situation of 128-bit IPv6 addresses, it is challenging to keep both operation numbers and memory footprints small. To address the issue, we propose the Neurotrie data structure, which supports fast lookup and arbitrary strides. Thus, a good balance can be made between trie depth and memory footprint by computing the proper stride for each Neurotrie node. We model the optimal Neurotrie problem which minimizes the depth with limited memory footprint and develop a pseudo-polynomial time baseline algorithm to construct Neurotrie using dynamic programming. To improve the performance and reduce the computation complexity, we develop a deep reinforcement learning-based approach, which leverages a deep neural network to construct Neurotrie efficiently, based on characteristics captured from real IPv6 prefixes. We further refine the data structure called Neurotrie-S and develop an efficient mechanism for routing updates. Experiments on real routing tables show that Neurotrie-S achieves a lookup rate 34% higher than that of state-of-the-art approaches. We implement a Neurotrie-based software switch, and the forwarding rate of Neurotrie-S is about 10% to 345% higher than other algorithms.

The optimal read strategy for strong consistent key-value applications is to enable the per-replica local reads that each replica has the ability to serve reads locally. Unfortunately, current schemes for the per-replica local reads are perplexed by two issues. First, some schemes have to violate the per-replica local reads when the workload is skewed, degrading the throughput. Second, most of current schemes rely on leases or a specialized hardware to guarantee the linearizability, bringing difficulties to the deployment.

A variety of model representation methods have been used in recent works to translate machine learning models into programmable switch rules to address network classification tasks at line-speed, i.e., in-network classification. These works generally deploy a complete but heavy model on a switch with limited hardware resources, causing both network-wide waste of resources and unsatisfactory accuracy. Therefore, we propose In-Forest, a general distributed in-network classification framework. Firstly, to improve accuracy with limited resources, we develop a Lightweight Ensemble Generic Optional Model (LEGO), which can be further enhanced into multiple enhanced base models with full functionality. Each switch only needs to deploy a simple base model, rather than the complete ensemble model. Thus, hardware resources required for both switches and the entire network can be significantly reduced. Secondly, as traffic traverses multiple switches, In-Forest aggregates the classification results from different enhanced base models for higher accuracy. Furthermore, we design a two-phase resource-aware model allocation strategy that assigns enhanced base models to switches under different scenarios. We use stable deep reinforcement learning to respond to dynamic traffic changes. Experimental results show that when compared to SwitchTree, Planter, and Netbeacon in two real network topologies, In-Forest can increase accuracy by up to 19.31%, while reducing the number of switch rules by 89.98%.

Multicast traffic is growing rapidly due to the development of multimedia streaming. Lately, stateless multicast protocols, such as BIER, have been proposed to solve the excessive routing states problem of traditional multicast protocols. However, the high complexity of multicast tree computation and the limited scalability for concurrent requests still pose daunting challenges, especially under dynamic group membership. In this paper, we propose Hawkeye, a dynamic and stateless multicast mechanism with deep reinforcement learning (DRL) approach. For real-time responses to multicast requests, we leverage DRL enhanced by a temporal convolutional network (TCN) to model the sequential feature of dynamic group membership and thus is able to build multicast trees proactively for upcoming requests. Moreover, an innovative source aggregation mechanism is designed to help the DRL agent converge when faced with a large amount of multicast requests, and relieve ingress routers from excessive routing states. Evaluation with real-world topologies and multicast requests demonstrates that Hawkeye adapts well to dynamic multicast: it reduces the variation of path latency by up to 89.5% with less than 12% additional bandwidth consumption compared with the theoretical optimum.

The emerging Software-Defined Networking (SDN) is being adopted by data centers and cloud service providers to enable flexible control. Meanwhile, the current SDN design brings new vulnerabilities. In this paper, we explore a stealthy attack that uses a minimum rate of attack packets to disrupt SDN data plane. To achieve this, we propose the LOFT attack that computes the lower bound of attack rate to overflow flow tables based on the inferred network configurations. Particularly, each attack packet always triggers or maintains consumption of one flow rule. LOFT can ensure the attack effect under various network configurations while reducing the possibility of being captured. We demonstrate its feasibility and effectiveness in a real SDN testbed consisting of commercial hardware switches. The experimental results show that LOFT incurs significant network performance degradation and potential network DoS at an attack rate of only tens of Kbps. To defeat the attack, we develop a data-to-control plane collaborative defense system named LOFTGuard, which is lightweight and transparent to SDN applications. Evaluations show that LOFTGuard effectively protects SDN against the attack and introduces a small overhead.

To improve the performance of mobile web services, a new transport protocol, QUIC, has been recently proposed as a substitute for TCP. However, with pros and cons of QUIC, it is challenging to decide whether and when to use QUIC in large-scale real-world mobile web services. Complex temporal correlation of network conditions, high user heterogeneity in a nationwide deployment, implementation diversity of QUIC variants limited, and resources on mobile devices all affect the selection of transport protocols. In this paper, we present WiseTrans, an adaptive transport protocol selection mechanism, to switch transport protocols for mobile web services online and improve the completion time of web requests. WiseTrans introduces machine learning techniques to deal with temporal heterogeneity, makes decisions with historical information to handle spatial heterogeneity, adopts an online learning method to keep pace with implementation variation, and switches transport protocols at the request level to reach high performance with acceptable overhead. We implement WiseTrans on two platforms (Android and iOS) in a popular mobile web service application of Baidu. Comprehensive experiments demonstrate that WiseTrans can reduce request completion time by up to 25.8% on average compared to the usage of a single protocol.

Emerging applications raise critical QoS requirements for the Internet. The improvements in flow classification technologies, software-defined networks (SDN), and programmable network devices make it possible to fast identify users’ requirements and control the routing for fine-grained traffic flows. Meanwhile, the problem of optimizing the forwarding paths for traffic flows with multiple QoS requirements in an online fashion is not addressed sufficiently. To address the problem, we propose DRL-OR-S, a highly scalable online routing algorithm using multi-agent deep reinforcement learning. DRL-OR-S adopts a comprehensive reward function, an efficient learning algorithm, and a novel deep neural network structure to learn appropriate routing strategies for different types of flow requirements. In order to enhance the generalization and scalability, we propose a novel graph-based actor-critic network architecture and a carefully designed input state for DRL-OR-S. To accelerate the training process and guarantee reliability, we further introduce an NN-simulator for efficient offline training and a safe learning mechanism to avoid unsafe routes during the online routing process. We implement DRL-OR-S under SDN architecture and conduct Mininet-based experiments using real network topologies and traffic traces. The results validate that DRL-OR-S can well satisfy the requirements of latency-sensitive, throughput-sensitive, latency-throughput-sensitive, and latency-loss-sensitive flows at the same time, while exhibiting great adaptiveness and reliability under the scenarios of link failure, traffic change, unseen large topology and partial deployment.

IPv6 has shown notable growth in recent years, imposing the need for high-speed IPv6 lookup. As the forwarding rate of virtual switches continues increasing, software-based IPv6 lookup without using special hardware such as TCAM, GPU, and FPGA is of academic interest and industrial importance. Existing studies achieve fast software IPv4 lookup by reducing the operation number, as well as reducing the memory footprint so as to benefit from CPU cache. However, in the situation of 128-bit IPv6 addresses, it is challenging to keep both operation numbers and memory footprints small. To address the issue, we propose the Neurotrie data structure, which supports fast lookup and arbitrary strides. Thus, a good balance can be made between trie depth and memory footprint by computing the proper stride for each Neurotrie node. We model the optimal Neurotrie problem which minimizes the depth with limited memory footprint and develop a pseudo-polynomial time baseline algorithm to construct Neurotrie using dynamic programming. To improve the performance and reduce the computation complexity, we develop a deep reinforcement learning-based approach, which leverages a deep neural network to construct Neurotrie efficiently, based on characteristics captured from real IPv6 prefixes. We further refine the data structure and develop an efficient mechanism for routing updates. Experiments on real routing tables show that Neurotrie achieves a lookup rate 34% higher than that of state-of-the-art approaches.

Network Function Virtualization (NFV) is a new networking paradigm to enable dynamic network function deployment in networks. Existing studies focused on optimized function deployment and management in NFV. Unfortunately, these studies did not well address the problem of efficient security function enforcement in networks, which is the goal of deploying network functions (NFs), i.e., for real-time security function enforcement on the traffic, since optimal function deployment does not mean efficient security function enforcement on network traffic. In particular, they incurred significant NF enforcement cost. In order to address this issue, in this paper, we propose${\textsf {FuncE}}$that aims to solve the efficient real-time security function enforcement problem by developing unified dynamic flow and function scheduling. We formulate the problem as an integer linear programming problem and prove that it is NP-hard. We tackle the problem by decomposing it and developing heuristics to achieve near-optimal solutions. We conduct comprehensive experiments by using real topologies to demonstrate the effectiveness of the${\textsf {FuncE}}$design. The experimental results demonstrate that${\textsf {FuncE}}$achieves near-optimal network function enforcement, which incurs over 100 times less latency than the existing the optimal solver. In particular, compared to the state-of-art defenses,${\textsf {FuncE}}$processes the same number of candidate flows using over 50% less VNFs, while ensuring the same level of function enforcement.

Software-Defined Networking (SDN). SDN enables network innovations with a centralized controller controlling the whole network through the control channel. Because the control channel delivers all network control traffic, its security and reliability are of great importance. For the first time in the literature, we propose the CrossPath attack that disrupts the SDN control channel by exploiting the shared links in paths of control traffic and data traffic. In this attack, crafted data traffic can implicitly disrupt the forwarding of control traffic in the shared links. As the data traffic does not enter the control channel, the attack is stealthy and cannot be easily perceived by the controller. In order to identify the target paths containing the shared links to attack, we develop a novel technique called adversarial path reconnaissance. Our experimental results show its feasibility and efficiency of identifying the target path. We systematically study the impacts of the attack on various network applications in a real SDN testbed. Experiments show the attack significantly degrades the performance of existing network applications and causes serious network anomalies, e.g., routing blackhole, flow table resetting, and even network-wide DoS. To defeat the CrossPath attack, we design a lightweight defense system named CrossGuard. Experiments demonstrate that it can effectively protect the control channel and quickly locate the attack flow with 98% accuracy while introducing a small overhead.

Satellite Internet is a critical part of future Internet infrastructure. Many existing studies focus on leveraging limited satellite resources to provide high quality of service (QoS), under the assumption that each satellite node has identical capacity. We propose to use customized satellite capacity based on traffic demands, and improve QoS from the network deployment point of view. In this paper, we tackle the problem of satellite resource allocation for civil aviation, which is an important application scenario of satellite Internet. We make analysis on real flight traces, model the access capacity allocation problem formally, which is most significant since it will subsequently influence the routing capacity and the satellite-to-ground-station (S2GS) capacity allocation, and prove that the problem is NP-hard. We propose the SARACA scheme and develop several algorithms to solve the access part, the routing part, and the S2GS part of the resource allocation problem and guarantee to meet the maximum traffic demand under dynamic circumstances. Simulation results show that SARACA achieves the least total deployment cost with good QoS compared with current approaches.

Emerging applications raise critical QoS requirements for the Internet. The improvements of flow classification technologies, software defined networks (SDN), and programmable network devices make it possible to fast identify users' requirements and control the routing for fine-grained traffic flows. Meanwhile, the problem of optimizing the forwarding paths for traffic flows with multiple QoS requirements in an online fashion is not addressed sufficiently. To address the problem, we propose DRL-OR, an online routing algorithm using multi-agent deep reinforcement learning. DRL-OR organizes the agents to generate routes in a hop-by-hop manner, which inherently has good scalability. It adopts a comprehensive reward function, an efficient learning algorithm, and a novel deep neural network structure to learn an appropriate routing policy for different types of flow requirements. To guarantee the reliability and accelerate the online learning process, we further introduce safe learning mechanism to DRL-OR. We implement DRL-OR under SDN architecture and conduct Mininet-based experiments by using real network topologies and traffic traces. The results validate that DRL-OR can well satisfy the requirements of latency-sensitive, throughput-sensitive, latency-throughput-sensitive, and latency-loss-sensitive flows at the same time, while exhibiting great adaptiveness and reliability under the scenarios of link failure, traffic change, and partial deployment.

Lots of studies focus on distributed traffic engineering (TE) where routers make routing decisions independently. Existing approaches usually tackle distributed TE problems through traditional optimization methods. However, due to the intrinsic complexity of the distributed TE problems, routing decisions cannot be obtained efficiently, which leads to significant performance degradation, especially for highly dynamic traffic. Emerging machine learning technologies like deep reinforcement learning (DRL) provide a new choice to address TE problems in an experience-driven method. In this paper, we propose DATE, a distributed and adaptive TE framework with DRL. DATE distributes well-trained agents to the routers in the located network. Each agent makes local routing decisions independently based on link utilization ratios flooded by each router periodically. To coordinate the distributed agents to achieve the global optimization in different traffic conditions, we construct candidate paths, develop the agents carefully, and realize a virtual environment to train the agents with a DRL algorithm. We do extensive simulations and experiments using real-world network topologies with both real and synthetic traffic traces. The results show that DATE outperforms some existing approaches and yields near-optimal performance with superior robustness.

Low earth orbit (LEO) satellite networks are promising at constructing the satellite Internet. Dynamics of a large-scale LEO network topology induces critical problems on routing efficiency and scalability. Existing approaches either suffer from such routing problems, or have limited resilience against unpredictable link/node failures. We propose Area-based SatellitE Routing (ASER), a fully distributed routing protocol for LEO networks with high efficiency and scalability. ASER uses a hierarchical routing mechanism, which groups satellites into areas, in such a way that the inter-area routing never changes due to handovers of inter-satellite links. Routing can be quickly reconstructed with little overhead when either regular or unpredictable topology changes occur. We develop ASER based on OSPF, and propose an efficient algorithm to construct the forwarding table, enabling standard packet forwarding. Simulations validate that ASER can reduce the convergence time by more than 50%, and reduce computation and control overhead by orders of magnitude.

To improve the performance of mobile web service, a new transport protocol, QUIC, has been recently proposed. However, for large-scale real-world deployments, deciding whether and when to use QUIC in mobile web service is challenging. Complex temporal correlation of network conditions, high spatial heterogeneity of users in a nationwide deployment, and limited resources on mobile devices all affect the selection of transport protocols. In this paper, we present WiseTrans to adaptively switch transport protocols for mobile web service online and improve the completion time of web requests.

Traffic engineering (TE) attracts extensive researches over the years. Operators expect to design a TE scheme which accommodates traffic dynamics well and achieves good TE performance with little overhead. Some approaches like oblivious routing compute an optimal static routing based on a large traffic matrix (TM) range, which usually leads to much performance loss. Many approaches compute routings based on one or a few representative TMs obtained from observed historical TMs. However, they may suffer performance degradation for unexpected TMs and usually induce much overhead of system operating. In this paper, we propose ALTE, an adaptive and low-cost TE scheme based on TM classification. We develop a novel clustering algorithm to properly group a set of historical TMs into several clusters and compute a candidate routing for each TM cluster. A machine learning classifier is trained to infer the proper candidate routing online based on the features extracted from some easily measured statistics. We implement a system prototype of ALTE and do extensive simulations and experiments using both real and synthetic traffic traces. The results show that ALTE achieves near-optimal performance for dynamic traffic and introduces small overhead of routing updates.

Traffic engineering (TE) in multi-region networks is a challenging problem due to the requirement that each region must independently compute its routing decisions based on local observations, yet with the goal of optimizing global TE objectives. Traditional approaches often lack the agility to adapt to changing traffic patterns and thus may suffer hefty performance loss under highly dynamic traffic demands. In this paper, we propose a data-driven framework for multi-region TE problems, which makes novel use of multi-agent deep reinforcement learning. In particular, we propose two reinforcement learning agents for each region, namely T-agents and O-agents, to control the terminal traffic and outgoing traffic, respectively. These distributed agents collect local link utilization statistics within their regions, optimize local routing decisions, and observe the resulting congestion-related reward. To facilitate these agents for optimizing global TE objectives, we tailor the agent design carefully including input, output, and reward functions. The proposed framework is evaluated extensively using real-world network topologies (e.g., Telstra and Google Cloud) and synthetic traffic patterns (e.g., the Gravity model). Numerical results show that comparing with existing protocols and single-agent learning algorithms, our solution can significantly reduce congestion and achieve nearly-optimal performance with both superior scalability and robustness. Throughout our simulations, over 90% of tests limit congestion within 1.2 times the global optimal solution.

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this article, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. We have implemented DCMPTCP in both the Linux kernel and ns-3 simulator. Our comprehensive testbed experiments and simulations show that DCMPTCP outperforms MPTCP in both 1 Gbps testbed, and 10 Gbps large-scale simulation network.

Fast rerouting (FRR) has become an important technology for networks to enhance the resilience against failures. On the other hand, there is an increasing interest on integrating routing control and network functions, enabling highly programmable, adaptive, and cross-layer networking. In this paper, we propose a unified FRR scheme (UFRR) under the circumstance of network function virtualization and service function chain (SFC). By exposing the SFC information to the network layer, UFRR can make rerouting decisions that are more flexible, and protect the routing against various types of failures, including switch, router, and server failures. We propose the system architecture, and develop two algorithms to compute rerouting paths, which consider path length and forwarding entry number concurrently. Simulation results show that the unified FRR is effective, and the path stretch can be reduced by more than 10% compared to a typical network layer FRR scheme, while the forwarding entry number remains similar.

Network function virtualization has become a promising technology recently. To accommodate dynamic traffic such as burst, NFV can scale virtual network functions (VNFs) across several physical servers, which is known as horizontal scaling, or enlarge the capacity of a VNF leveraging idle resources in one physical server, which is called vertical scaling. Vertical scaling should be used as much as possible to accommodate user traffic increment immediately, and horizontal scaling is only performed when vertical scaling cannot allocate sufficient resource to meet the demand. We find that just to reserve redundant resource is far from sufficient to enable vertical scaling with good performance, and VNF placement plays a crucial role. In this paper, we model the scalable VNF placement problem formally, and prove that the problem is NP-hard. Then, we develop two online heuristic algorithms gradually to solve the problem. The sVNFP algorithm focuses on maximizing the vertical scalability, while the sVNFP-adv algorithm considers path length and server utilization concurrently. Simulation results show that our algorithms can reduce packet loss ratio by 15% to 40%, with a short path length and a high server utilization.

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this paper, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. DCMPTCP has a good compatibility and is easy to deploy. We implement DCMPTCP in ns-3 simulator and evaluate the performance by comprehensive simulations. The results show that DCMPTCP achieves ~65-771X and ~10-15X better FCT than MPTCP for rack-local and inter-rack traffic respectively.

A fine-grained traffic engineering (TE) that enables per-flow control is considered to be necessary in future Internet. In this paper, we study to realize flow-level TE in conventional networks, where hop-by-hop routing is available, and advanced technologies such as SDN and MPLS are not deployed. Based on analysis and modelling on real Internet traffic, we propose to detect and schedule a few large flows in real time, which dominate the traffic amount. The proposed scheme leverages advanced algorithms for detection, computes the rerouting paths in a centralized server, uses extended OSPF to distribute the routing, and uses a few ACL entries for flow-level forwarding. We formalize the link weight assignment-based large flow scheduling problem and prove that the problem is NP-hard. We develop algorithms to compute the routing and reduce extra LSA number required. We present a set of theoretical results on the TE performance bounds when the number of large flows varies. Experiment and simulation results show that our scheme can reroute large flows within 0.5 second, and the maximum link utilization is within 102% of the optimal solution for source and destination addresses-based flows, while the extra LSA number is small.

Multi-link failures may incur heavy packet loss and degrade the network performance. Fast rerouting has been proposed to address this issue by enabling routing protections. However, the effectiveness and efficiency issues of fast rerouting are not well addressed. In particular, the protection performance of existing approaches is not satisfactory even if the overhead is high, and topology constraints need to be met for the approaches to achieve a complete protection. To optimize the efficiency, we first answer the question that whether label-free routing can provide a complete protection against arbitrary multi-link failures in any networks. We propose a model for interface-specific-routing which can be seen as a general label-free routing. We analyze the conditions under which a multi-link failure will induce routing loops. And then, we present that there exist some networks in which no interface-specific-routing (ISR) can be constructed to protect the routing against any k-link failures (k ≥ 2). Then, we propose a tunneling on demand (TOD) approach, which covers most failures with ISR, and activate tunneling only when failures cannot be detoured around by ISR. We develop algorithms to compute ISR properly so as to minimize the number of activated tunnels, and compute the protection tunnels if necessary. We prove that TOD can protect routing against any single-link failures and dual-link failures. We evaluate TOD by simulations with real-world topologies. The results show that TOD can achieve a near 100% protection ratio with small tunneling overhead for multi-link failures, making a better tradeoff than the state-of-the-art label-based approaches.

Recently, QUIC proposed by Google has drawn great attentions due to several attractive features which improve the page load time for Web applications. The features of QUIC lie across different protocol layers, and the benefits and the limitations introduced by each feature have not been understood sufficiently. In this paper, we focus on the transport aspect of QUIC, with the packet pacing mechanism for congestion control. We conduct an experimental study to evaluate the performance of QUIC as a general-purpose transport protocol, especially when QUIC is used concurrently with TCP, which is still the dominating transport protocol on the Internet. By doing so, we can exploit the potential of QUIC as a transport protocol in other application scenarios besides HTTP/2. It is also useful for the improvement of QUIC itself, because each object transmitted in one stream of a QUIC connection has to compete for resources with other transport protocols on the Internet. We do both testbed and real Internet experiments, on typical network environments such as 4G and WiFi. Our main findings indicate that, QUIC is less competitive than TCP in a network with little loss rate, large buffer, or large propagation delay; the 0-RTT connection establishment feature of QUIC has little advantage over TCP in practice; and the main benefit of QUIC lies in the multi-stream-based multiplexing mechanism.

It has been widely agreed that the architecture of the Internet is ossified. Many advanced technologies were proposed to deal with the shortcomings of the Internet, such as Quality of Service (QoS), IP multicast, and BGP multipath, but they have not been deployed widely in the Internet. Information-Centric Networking (ICN) has attracted many interests in the research community. The key insight of ICN is that the usage of the Internet has dramatically changed from connecting nodes to accessing information. By the inspiration of ICN thinking, we argue that it is not the ossification of the IP architecture which matters, but the ossification of the business model between ISPs. In this paper, we propose a Supply Chain Management (SCM) based business model to regulate the business relationships between ISPs. The payment flow along Internet Service Providers (ISPs) is changed from the conventional bottom up mode to a chain like mode, very similar to the payment flow of SCM. Simulation results show that the average Autonomous System (AS) hops can be reduced by 47% at most. The hit ratio of replicated contents can be improved by 28% at most, and the profit change of various ASes is almost balanced.

We present a study on minimizing non-renewable energy for the Internet. The classification of renewable and non-renewable energy brings in several challenges. First, it is necessary to understand how the routing system can distinguish the two types of energy in the power supply. Second, the routing problem changes due to renewable energy; and so do the algorithm designs and analysis. We first clarify the model of how routers can distinguish renewable and non-renewable energy supporting their power supply. This cannot be determined by the routing system alone, and involves modeling the energy generation and supply of the grid. We then present the router power consumption model, which has a fixed startup power and a dynamic traffic-dependent power. We formulate a minimum non-renewable energy routing problem, and two special cases representing either the startup power dominates or the traffic-dependent power dominates. We analyze the complexity of these problems, develop optimal and sub-optimal algorithms, and jointly consider QoS requirements such as path stretch. We evaluate our algorithms using real data from both National and European centers. As compared to the algorithms minimizing the total energy, our algorithms can reduce the non-renewable energy consumption for more than 20% under realistic assumptions.

Information-Centric Networking (ICN) is proposed to address the inefficiency of content delivery of IP networks from the perspective of architecture. In contrast, Content Delivery Network (CDN) is an overlay solution in current IP networks. We believe that even though ICN is fully deployed, there is still a role for CDNs to play in ICN networks. Since ISPs in ICN will replicate and forward contents according to their policies and interests, it may not align with the objectives of Content Providers (CPs). Therefore, CPs are willing pay a third party (i.e., CDN providers) a certain fee to meet their own requirements. In this paper, we propose to use the inventory model of Supply Chain Management (SCM) in logistics to formulate the content delivery process of ICN networks. The product-centric model of SCM is well-suited for the content-centric content delivery process of ICN networks. Also, we propose the system framework of inventory Centric Delivery Network (iCDN). Simulation results show that the average cost and link usage of the SCM-based algorithm can be reduced by 52% and 15% respectively compared to the baseline approach.

Multi-link failures in the Internet may incur heavy packet loss and degrade the network performance. Existing approaches have been proposed to address this issue by enabling routing protections. However, the effectiveness and efficiency issues of these approaches are not well addressed. In particular, it has not been answered that whether label-free routing can provide full protection against arbitrary multi-link failures in any networks. We propose a model for interface-specific-routing (ISR) which can be seen as a general label-free routing. We present that there exist some networks in which no ISR can be constructed to protect the routing against any k-link failures (k ≥ 2). To improve the protection effectiveness with little overhead in such cases, we propose a tunneling on demand (TOD) approach in this paper. With our approach, most failures can be covered by ISR, and tunneling is activated only when failures cannot be detoured around by ISR. We develop algorithms to compute ISR properly so as to minimize the number of activated tunnels, and compute the protection tunnels if necessary. We prove that TOD can protect routing against any single-link failures and dual-link failures. We evaluate TOD by simulations with real world topologies. The results show that TOD can achieve a protection ratio higher than 98% with small tunneling overhead for multi-link failures, better than existing tunnel-free approach whose protection ratio is 85% to 95%.

BGP measurement is important for monitoring and understanding the Internet anomalies. Most of the previous works on BGP measurement rely on aggregated statistics from BGP monitors, e.g., total updates. However, BGP events may have quite limited visibility. Therefore, merely investigating aggregated data may lead to misunderstanding Internet instability, e.g., overestimating the impact of monitor-local events. In this empirical study, we demonstrate how BGP data are distributed among a large number of monitors. We define eleven features as the analysis targets, and three metrics to quantify disparity. We apply the method to 1.14 TB data and find that the distribution of most of the features is quite uneven, and different types of feature illustrate different levels of disparity. We also observe long periods of persistent high disparity, and a small set of cross-feature highly active monitors. Our analysis highlights the necessity of per-monitor data analysis in future BGP measurement study.

Border Gateway Protocol (BGP) has been and will still be the de-facto standard for inter-domain routing in the Internet. However, the problem of routing oscillations in BGP has not been well addressed, which can introduce lots of unnecessary routing updates and severely degrade network performance. In particular, existing studies need a great effort to be deployed or introduce a large overhead. In this paper, we propose to first detect a routing oscillation quickly after the oscillation happened, and then, we eliminate the routing oscillation by disseminating only one additional path (Add-path). Based on analysis of BGP updates in the routers where oscillations have already happened, we present a general method to detect a routing oscillation within a couple of routing replacements. Then, we show that one more Add-path is enough to stop the oscillation. We propose the Minimal Add-paths BGP (MA-BGP) approach, develop algorithms, and prove that MA-BGP can guarantee stable iBGP by a classical model that captures the underlying semantics of any path vector protocol including BGP. The simulation results show the effectiveness and efficiency of our approach.

Measuring the instability of IP prefixes in BGP is critical for network operation and management. In particular, identifying and investigating the most active prefixes assist in detecting, analyzing, and understanding network problems. The traditional metric to assess the activeness of a prefix is the quantity of BGP update. However, this metric may be strongly affected by monitor-local events: the large amount of updates for a highly active prefix may be caused by an event with rather limited impact area. To cope with the issue, we propose a two-dimensional method: in addition to the traditional metric, Update Quantity (UQ), we introduce Update Visibility (UV). The key idea is that we mark a prefix as a `Highly Active Prefix' only when the large number of updates for it are widely observable. We define five types of active prefixes and propose a measurement method. We apply the method to 947 GB updates; the measurement results show that the two-dimensional method provides a more comprehensive picture of the highly active prefixes in the Internet than traditional single-metric schemes, and provides insights into network operations.

Satellite networks are drawing more and more attention, since they can provide various services to everywhere on the earth. Communication devices in satellites are typically powered by solar panels and battery cells, which are carefully designed to guarantee power supply and avoid deficiency. However, we find that unrestrained use of energy will cause a satellite to age quickly, because the number of recharge/discharge of battery cells is limited. Due to the extremely high cost of satellites, the development of energy-efficient satellite routing to save energy and prolong satellite lifetimes has become significantly important. In this paper, we do comprehensive studies. First, we model the power consumption of a space router, power supply by solar panels, and aging of battery cells formally. Second, we define the energy-efficient satellite routing (EESR) problem, and prove that the EESR problem is NP-hard. Then, we develop three algorithms to gradually solve the EESR problem. GreenSR-B is a baseline algorithm which computes link costs iteratively to compute a routing that minimizes the total recharge/discharge cycle number. GreenSR-A selects space routers to switch into sleep mode to improve energy conservation. GreenSR jointly considers energy efficiency and QoS requirements of path length and the maximum link utilization ratio. We evaluate our algorithms by simulations on a low earth orbit satellite network with real Internet usage traces. The results show that GreenSR can prolong the lifetime of satellite battery cells by more than 40%, with little increment in path length and a small link utilization ratio.

In this paper we study energy conservation in the Internet. We observe that different traffic volumes on a link can result in different energy consumption; this is mainly due to such technologies as trunking (IEEE 802.1AX), adaptive link rates, etc. We design a green Internet routing scheme, where the routing can lead traffic in a way that is green. We differ from previous studies where they switch network components, such as line cards and routers, into sleep mode. We do not prune the Internet topology. We first develop a power model, and validate it using real commercial routers. Instead of developing a centralized optimization algorithm, which requires additional protocols such as MPLS to materialize in the Internet, we choose a hop-by-hop approach. It is thus much easier to integrate our scheme into the current Internet. We progressively develop three algorithms, which are loop-free, substantially reduce energy consumption, and jointly consider green and QoS requirements such as path stretch. We further analyze the power saving ratio, the routing dynamics, and the relationship between hop-by-hop green routing and QoS requirements. We comprehensively evaluate our algorithms through simulations on synthetic, measured, and real topologies, with synthetic and real traffic traces. We show that the power saving in the line cards can be as much as 50 percent.

Energy conservation has become a major challenge to the Internet. Switching part of components into sleep mode is an effective way for energy conservation. Many existing approaches compute routing based on traffic matrices, to balance energy saving and traffic engineering goals, e.g., the maximum link utilization ratio (MLUR). However, accurate traffic matrices are difficult to obtain and change frequently, resulting in lack of routing stability and robustness. We propose to find one energy-aware routing robust to a set of traffic matrices. Such demand-oblivious routing problem has been studied without energy conservation, but it becomes more challenging when energy is considered. To overcome the challenges, we define a metric that reflects the MLUR distance from a routing to the optimal routing under certain energy conservation requirement. We model the problem of minimizing the metric, and analyze the upper bounds. Then, we propose a robust energy-aware routing (REAR) scheme to solve the problem by selecting sleeping links and computing the routing, based on a classical demand-oblivious routing algorithm. We evaluate our algorithms by simulations on real topologies. The results show that REAR is much more robust than existing approaches while more than 35\% line card power can be saved.

Distributed Software Defined Networking federates multiple controllers In a network to solve the problems In single controller networks, e.g., to improve network reliability and reduce the delay between controllers and switches. However, in the current distributed SDN schemes, the mapping between SDN switches and controllers is statically configured, which may result in uneven load distribution among controllers. These schemes cannot fully benefit from the distributed SDN architecture. In order to address this issue, this paper proposes an adaptive elastic distributed SDN architecture. The architecture dynamically selects a minimum. number of active controllers that switches attached to, and changes the mapping between switches and controllers according to the network load. Specially, a switch can migrate from one controller domain to another so that the mapping is adaptive to the network load. We formalize the controller selection problem as an optimization problem, and prove that the problem is NP-Hard. We solve the problem by using greedy algorithms. With the algorithms, controllers In a network are dynamically changed with respect to the network load and the number of active controll0ers is close to the optimal value. We validate the algorithms and evaluate the performance by simulations. In particular, the simulation results show that the number of active controllers is reduced by 60% when the whole network load decreases.

Many attempts have been made to detect and analyze anomalous Internet events through dissecting BGP updates and tables, and substantial progress has been made in detecting and quantifying the impact of major Internet disruptions. However, we notice that most works in this realm either deploy/use a limited quantity of monitors or analyze aggregated statistics, and such practice may result in overestimating the impact of monitor-local events, which can be viewed only by a rather small portion of the Internet. To eliminate the impact of such local events on the detection of Internet-level anomalies, we raise the concept of Large-scale BGP Event (LBE), which affects a large amount of IP prefixes (high impact) and is widely observable (non-local). To detect LBE, we record update data in the Update Visibility Matrix (UVM) according to the prefix and monitor related to each update. At first, we formulate the problem of identifying LBE in UVM as a bi-clustering problem; after proving it is NP-hard, we describe our heuristic algorithm. Next, we apply our scheme to more than 2 TB of historical data. We find that LBE is highly correlated with many well-known disruptive incidents. Furthermore, we also identify some abnormal events that have never been investigated. We believe our work can assist in network operation tasks such as problem prevention, diagnosis, and recovery.

We propose an "α%-Green" network benchmark, whose spirit is that α% of the network power should come from the renewable energy. We argue that, as long as such a benchmark is satisfied, the network should always set its primary objective to its own optimization concerns.

Anomalous BGP events can deteriorate Internet performance and connectivity thus have always been a research topic. However, most measurement works in this realm are prone to monitor-local events, namely, the events local to only few BGP monitors. Besides, events that are widely observed can also have negligible impact, e.g., prefix-local events. In contrast, a Large-scale BGP Event (LBE) makes a large quantity of prefixes be updated and can be observed by a large portion of monitors. Such events are anomalous even harmful. We formulate the problem of identifying LBEs from BGP updates, then propose the Iterative Cut-off Algorithm to solve it. We apply the method to some famous disruptive events and some `innocent' data, which are collected from more than 400 monitors. The measurement results validate the effectiveness of our method. Moreover, we detect a severe and persistent misconfiguration event that has remained unreported before.

In the current IP networks, content replication and inter-domain Traffic Engineering (TE) are manipulated by different entities with respective objectives, and work at different layers as well. In Information-Centric Networking (ICN), however, they can both be administered by Internet Service Providers (ISPs) and work at the same network layer. In this paper, we present our study of jointly optimizing content replication and inter-domain TE in ICN, which aims at maximizing the profit of inter-domain traffic for an ISP while constrained by the limits of link bandwidth and the availability of contents. Results show that our algorithms can increase the ISP's profit by 66% and reduce the link utilization by 23% on average, which are near-optimal and with much less running time.

Ternary Content Addressable Memory(TCAM)-based multidimensional tables are widely used to implement Access Control Lists (ACLs) for Internet packet classification and filtering, and have also become attractive for constructing the forwarding tables of Internet routers and the flow tables of Openflow switches, where multiple fields are generally used to match incoming packets. However, as such tables can grow quickly as the Internet develops fast, and sometimes even expand in size because of TCAMs limitation in storing rules with range fields, it becomes imperative to compress these tables. In this paper, we propose a fast and efficient approach to multidimensional table compression. We divide the multidimensional space iteratively to obtain a series of cells, and then combine those cells that are associated with the same action. Our approach applies to tables of any dimension, addresses the range expansion problem, and provides efficient compression for TCAM-based tables. The experiments show that our approach has low computing cost in time, which is significant for the online update of tables. On average, it reduces 23.0% entries of the real-life ACLs, 25.8% to 55.1% of the generated two-dimension tables, 55.1% of the generated ACLs, and 28.4% of the generated Openflow flow tables.

The Internet is generally not energy-efficient since all network devices are running all the time and only a small fraction of consumed power is actually related to traffic forwarding. Existing studies try to detour around links and nodes during traffic forwarding to save powers for energy-efficient routing. However, energy-efficient routing in traditional IP networks is not well addressed. The most challenges within an energy-efficient routing scheme in IP networks lie in safety and practicality. The scheme should ensure routing stability and loop- and congestion-free packet forwarding, while not requiring modifications in the traditional IP forwarding diagram and shortest-path routing protocols. In this paper, we propose a novel energy-efficient routing approach called safe and practical energy-efficient detour routing (SPEED) for power savings in IP networks. We provide theoretical insight into energy-efficient routing and prove that determining if energy-efficient routing exists is NP-complete. We develop a heuristic in SPEED to maximize pruned links in computing energy-efficient routings. Extensive experimental results show that SPEED significantly saves power consumptions without incurring network congestions using real network topologies and traffic matrices.

In this paper we study energy conservation in the Internet. We observe that different traffic volumes on a link can result in different energy consumption; this is mainly due to such technologies as trunking (IEEE 802.1AX), adaptive link rates, etc. We design a green Internet routing scheme, where the routing can lead traffic in a way that is green. We differ from previous studies where they switch network components, such as line cards and routers, into sleep mode. We do not prune the Internet topology. We first develop a power model, and validate it using real commercial routers. Instead of developing a centralized optimization algorithm, which requires additional protocols such as MPLS to materialize in the Internet, we choose a hop-by-hop approach. It is thus much easier to integrate our scheme into the current Internet. We progressively develop three algorithms, which are loop-free, maximize energy conservation, and jointly consider green and QoS requirements such as path stretch. We comprehensively evaluate our algorithms through simulations on synthetic and real topologies and traffic traces. We show that the power saving in the line cards can be as much as 50%.

Routing failures are common on the Internet and routing protocols can not always react fast enough to recover from them, which usually cause packet delivery failures. To address the problem, fast reroute solutions have been proposed to guarantee reroute path availability and to avoid high packet loss after network failures. However, existing solutions are often specific to single type of routing protocol. It is hard to deploy these solutions together to protect Internet routing including both intra- and inter-domain routing protocols because of their individual computational and storage complexity. Moreover, most of them can not provide effective protection for traffic over failed links, especially for the bi-directional traffic. In this paper, we propose a unified fast reroute solution for routing protection under network failures. Our solution leverages identifier based direct forwarding to guarantee the effectiveness of routing protection and supports incremental deployment. In particular, enhanced protection cycle (e-cycle) is proposed to construct rerouting paths and to provide node and link protection for both intra- and inter-domain routing protocols. We evaluate our solution by simulations, and the results show that the solution provides 100% failure coverage for all end-to-end routing paths with approximately two extra Forwarding Information Base (FIB) entries. Furthermore, we report an experimental evaluation of the proposed solution in operational networks. Our results show that the proposed solution effective provides failure recovery and does not introduce processing overhead to packet forwarding.

IP Fast ReRoute (IPFRR) has received increasing attention as a means to effectively shorten traffic disruption under failures. A major approach for IPFRR is to pre-calculate backup paths for nodes and links. Such approach is, however, hard to deploy due to the tremendous computational overhead. Thus, a lightweight IPFRR scheme is desired to effectively detour failures and provide routing protection. In this paper, we propose a Fast Tunnel Selection (FTS) algorithm to achieve tunnel-based IPFRR. The FTS algorithm can find an effective tunnel endpoint before complete computation of entire SPT and effectively reduce computation overhead. We simulate FTS with different size of generated topologies, and the results show that FTS algorithm reduces much computation overhead compared to existing approaches, and achieves a 99.10% average link protection rate and a 91.97% average node protection rate.