IP Backbone Network Research Articles

ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario

Origin–Destination (OD) matrices record directional flow data between pairs of OD regions. The intricate spatiotemporal dependency in the matrices makes the OD matrix forecasting (ODMF) problem not only intractable but also non-trivial. However, most of the related methods are designed for very short sequence time series forecasting in specific application scenarios, which cannot meet the requirements of the variation in scenarios and forecasting length of practical applications. To address these issues, we propose a Transformer-like model named ODformer, with two salient characteristics: (i) the novel OD Attention mechanism, which captures special spatial dependencies between OD pairs of the same origin (destination), greatly improves the ability of the model to predict cross application scenarios after combining with 2D-GCN that captures spatial dependencies between OD regions. (ii) a PeriodSparse Self-attention that effectively forecasts long sequence OD matrix series while adapting to the periodic differences in different scenarios. Generous experiments in three application backgrounds (i.e., transportation traffic, IP backbone network traffic, crowd flow) show that our method outperforms the state-of-the-art methods.

Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence

For telecom operators, it is of great significance to employ artificial intelligence (AI) and big data technology in a software-defined network (SDN) in order to achieve intelligent network control, traffic management and optimization. This paper proposes a solution for intelligent work control and traffic optimization. This paper is mainly focused on SDN-based network traffic algorithm optimization and experimental verification. In this paper, we design a network control mechanism for network intelligent control as well as solutions for traffic optimization based on SDN and artificial intelligence. We analyze operators’ network requirements (e.g., the carrying of the 5th generation mobile network (5G) service, multi-protocol label switching virtual private networks optimization, cloudification of services and the IP backbone network). Then, we propose an intelligent network control architecture based on SDN and artificial intelligence. The proposed architecture consists of three modules, including a network status collection/perception module, an AI intelligent analysis module and an SDN controller module. Moreover, this paper also analyzes the objects of traffic optimization as well as routing calculation algorithms (e.g., the greedy algorithm, the top-k-shortest paths (KSP) algorithm) and routing optimization algorithms (e.g., particle swarm optimization, simulated annealing and genetic algorithms). In addition, we also put forward three optimization algorithms for the operator’s network, namely, network congestion control and prevention algorithms, resource preemption algorithms and balance of the entire network traffic algorithms. Then, we propose optimization algorithms for the above three objectives of operator network optimization, respectively. Finally, we conduct large-scale experiments to verify the effectiveness of the control mechanism and algorithms. The experimental results demonstrate that the use of SDN and artificial intelligence in operator networks can realize network intelligent control and traffic optimization more intelligently.

Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction

SummaryThrough accurate network‐wide traffic prediction, network operators can agilely manage resources and improve robustness by proactively adapting to new traffic patterns, especially for traffic engineering, capacity planning and quality of service provisioning. However, due to the proliferation of backbone network traffic as well as the complexity and dynamics of network communication behavior, accurate and effective network‐wide traffic prediction is challenging. To address the challenges, this paper focuses on short‐term traffic matrix (TM) prediction in large‐scale IP backbone networks. In order to improve the prediction performance, a novel spatiotemporal graph convolutional recurrent network (SGCRN)—a deep learning framework that incorporates both spatial and temporal dependencies of traffic flows, is proposed to implement TM prediction with high accuracy and efficiency. By learning network‐wide traffic as graph‐structured TM time series, SGCRN jointly utilizes graph convolutional networks (GCN) and gated recurrent units (GRU) networks to extract comprehensive spatiotemporal correlations among traffic flows. Specifically, SGCRN employs GCNs to identify structural spatial features of traffic flows by considering topological properties, and utilizes GRUs to implement temporal features learning by considering short and long‐term dynamics of traffic flows. Extensive experimental results on the inter‐Points of Presence network traffic data from four real IP backbone networks show that SGCRN can effectively predict short‐term network‐wide TM with superior accuracy compared with other four widely used traffic prediction methods.

Risk Minimization Routing Against Geographically Correlated Failures

Regional failures, such as natural disaster or malicious attack, have become a major threat to the construction of future reliable communication network. The regional failures usually cause a large number of disconnected nodes simultaneously and influence the network for a long time. However, a routing scheme that is resilient to such geographically correlated failures is still unexplored. In this paper, we provide a comprehensive study of the disaster resilient routing dealing with the regional failure in operational IP backbone networks. It is notable that the path with minimal risk (i.e., minimal failure probability) is not necessarily the shortest path. The main challenge of finding such paths is that regional failure is unpredictable in terms of time, location, and the affected area. To this end, in combination with the computational geometry tool, we develop effective algorithms to find the minimal risk path between end node pairs to tolerate random regional failures. We show that in contrast to the conventional shortest path, a little longer path can be more effective to the disasters. After selecting such a path as the primary path, we turn to find a secondary backup path. In contrast to the conventional single link/node failure, a regional failure disrupts a large number of network components, simultaneously. As a result, how to find backup paths for re-establishment of the corrupted paths will raise a novel fairness issue. Specifically, during the backup path allocation, we focus on routing fairness to bound the worst-case user experience. A metric is proposed based on which an ILP is formulated. The extensive simulations validate that such an issue is non-negligible in face of regional failure scenarios.

Traffic Engineering Using Segment Routing and Considering Requirements of a Carrier IP Network

Internet Service Providers are challenged by increasing traffic demands. Advanced Traffic Engineering (TE) is one way to overcome this challenge. Segment Routing (SR) is a relatively new approach for TE. To decide whether SR is a good approach for deployment in carrier IP backbone networks, it has to show its benefits in real-world scenarios and still needs to be feasible from the network operation and management point of view. In this paper, we analyze traffic data from a European tier one backbone network from 2011 to 2015. The total traffic increases significantly throughout that period. We analyze geographic differences to select representative traffic peak times as reference scenarios for an evaluation of TE using SR for real-world topologies and traffic demands. Finally, we extend the existing SR formulations to consider requirements from network operation and management. Our evaluation results show that the SR yields close to optimal results while still being deployable with reasonable effort.

Including Failure Correlation in Availability Modeling of a Software-Defined Backbone Network

Software-defined networking (SDN) promises to improve the programmability and flexibility of networks, but also brings new challenges that need to be explored. The main objective of this paper is to include failure correlation in a quantitative assessment of the properties of SDN backbone networks to determine whether they can provide similar availability as the traditional IP backbone networks. To achieve this goal, this paper has formalized a two-level availability model that captures the global network connectivity without neglecting the essential details and which includes a failure correlation assessment. This paper proposes a modular and systematic approach for characterizing the principal minimal-cut sets in both SDN and traditional networks, and stochastic activity network models for characterizing the single network elements. To demonstrate the feasibility of the model, an extensive sensitivity analysis has been carried out on a national backbone network.

Lifetime-Aware ISP Networks: Optimal Formulation and Solutions

We propose a framework to manage the link lifetime in an IP backbone network by exploiting the sleep mode (SM). In particular, when an SM feature is available, two different effects coexist: 1) during the SM state, the lifetime tends to be increased and 2) however, when the link changes its power state (from SM to full power or vice-versa), the lifetime tends to be decreased. We, therefore, define an optimal formulation of the lifetime-aware network problem. Moreover, we propose a heuristic, called Acceleration Factor Algorithm, to practically manage the device lifetime. We solve the problem both optimally and with our heuristic, considering two representative case studies. Results show that our approach outperforms the previous energy-aware algorithms, which instead do not consider the lifetime decrease triggered by the power state change. Thus, we argue that a lifetime-aware network management should be pursued when deciding to set an SM state for each device in an Internet Service Provider network.

An energy‐efficient algorithm based on sleep‐scheduling in IP backbone networks

SummaryThis paper relates to the problem of energy conservation in Internet backbone networks. We propose a new Distributed Green Algorithm based on Sleep‐scheduling (DGAS) to switch off some network links and nodes resulting in less energy consumption. Our solution does not depend on any centralized controller and uses a link‐state protocol like Open Shortest Path First to share required information. In addition, DGAS does not require any knowledge of traffic matrix and switches off network nodes and links in 2 phases. In the first phase, some core nodes are switched off on the basis of their usage in network paths. In the second phase, DGAS starts with a minimal topology on the basis of resulted topology from the first phase and adds some links to it to minimize network paths length. Results, obtained by several real network scenarios, show that DGAS can switch off up to 27% of core nodes and up to 80% of links compared to the maximum number of switchable links.

A study of configuration evolution of an operational IP backbone network

Configuration errors are proved to be the main reasons for network interruption and anomalies. Many researchers have paid their attention to configuration analysis and provisioning, but few works focus on understanding the configuration evolution. In this paper, we uncover the configuration evolution of an operational IP backbone based on the weekly reports gathered from January 2006 to January 2013. We find that rate limiting and launching routes for new customers are configured most frequently. In addition, we conduct an analysis of network failures and find that link failures are the main causes for network failures. We suggest that we should configure redundant links for the links which are easy to break down. At last, according to the analysis results, we illustrate how to provide semi-automated configuration for rate limiting and adding customers.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.

Detection of Link Fault Using Risk Modelling and Improving QOS Parameters in IP/WDM Networks

An operational backbone network is connected with many routers and other devices. Identifying faults in the network is very difficult, so a fault localization mechanism is necessary to identify fault and alleviate it and correct the faults in order to reduce the network performance degradation. A risk model needs to be devised based on the dynamic database by creating alternate path and the network is reconfigured by identifying dynamic paths. In this paper, an on-demand link state routing approach is used for handling failures in IP backbone networks and a localization algorithm is used to improve QOS parameters based on threshold value of gateway. It is proved that on-demand link state routing guarantees loop-free forwarding to reachable destinations regardless of the number of failures in the network, and in case of localization algorithm using modification process packet loss is avoided based on threshold value of gateway. Heuristic algorithm is also used for reconfiguration of dynamic path for effective fault localization. In this paper, in order to change the traffic condition, reconfiguration strategic is dynamically used. Dijikstra’s shortest path algorithm has been used to determine the shortest path between node pairs. Using risk modeling mechanism, a small set of candidate faults is identified. The concept of Fault Localization is used to minimize the fault occurring in the node and sends original path to node pairs. The localization algorithm based on MODIFICATION PROCESS, packet loss is avoided in the network by checking threshold value of gateway. If the threshold value is maximum, router directly forwards the packet to destination through gateway and if the threshold value is minimum, router compresses the packet and forwards the packet to destination with notification via gateway.

Future research directions in design of reliable communication systems

In this position paper on reliable networks, we discuss new trends in the design of reliable communication systems. We focus on a wide range of research directions including protection against software failures as well as failures of communication systems equipment. In particular, we outline future research trends in software failure mitigation, reliability of wireless communications, robust optimization and network design, multilevel and multirealm network resilience, multiple criteria routing approaches in multilayer networks, resilience options of the fixed IP backbone network in the interplay with the optical layer survivability, reliability of cloud computing networks, and resiliency of software-defined networks. Described research directions are frequently enhanced with examples.

End-to-End Network Traffic Reconstruction Via Network Tomography Based on Compressive Sensing

Traffic matrices (TM) represent the volumes of end-to-end network traffic between each of the origin---destination pairs. Accurate estimates of TM are used by network operators to perform network management functions and traffic engineering tasks. Despite a large number of methods devoted to the problem of traffic matrix estimation, the inference of end-to-end network traffic is still a main challenge in the large-scale IP backbone network, due to an ill-posed nature of itself. In this paper, we focus on the problem of end-to-end network traffic reconstruction. Based on the network tomography method, we propose a simple method to estimate end-to-end network traffic from the aggregated data. By analyzing, in depth, the properties of the network tomography method, compressive sensing reconstruction algorithms are put forward to overcome the ill-posed nature of the network tomography model. In this case, to satisfy the technical conditions of compressive sensing, we propose a modified network tomography model. Besides, we give a further discussion that the proposed model follows the constraints of compressive sensing. Finally, we validate our method by real data from the Abilene and GEANT backbone networks.

One QoS Routing Algorithm for Load Balancing in IP Backbone Networks: Design and Simulation

Quality of Service (QoS) routing is intent to find feasible routes according to QoS constraints and optimize utilization of whole network resources. In order to solve bandwidth-guaranteed QoS routing problem, a simple but effective routing algorithm based on precomputation scheme with load balancing consideration, is proposed. The algorithm is implemented and simulated using Network Simulator (NS2). Research results showed that the proposal can keep some links free from becoming bottlenecks earlier, avoid congestion caused by unbalanced traffic load distribution, and improve performance of IP backbone networks.

Handling Multiple Failures in IP Networks through Localized On-Demand Link State Routing

It has been observed that transient failures are fairly common in IP backbone networks and there have been several proposals based on local rerouting to provide high network availability despite failures. While most of these proposals are effective in handling single failures, they either cause loops or drop packets in the case of multiple independent failures. To ensure forwarding continuity even with multiple failures, we propose Localized On-demand Link State (LOLS) routing. Under LOLS, each packet carries a blacklist, which is a minimal set of failed links encountered along its path, and the next hop is determined by excluding the blacklisted links. We show that the blacklist can be reset when the packet makes forward progress towards the destination and hence can be encoded in a few bits. Furthermore, blacklist-based forwarding entries at a router can be precomputed for a given set of failures requiring protection. While the LOLS approach is generic, this paper describes how it can be applied to ensure forwarding to all reachable destinations in case of any two link or node failures. Our evaluation of this failure scenario based on various real network topologies reveals that LOLS needs 6 bits in the worst case to convey the blacklist information. We argue that this overhead is acceptable considering that LOLS routing deviates from the optimal path by a small stretch only while routing around failures.

An OSPF-Integrated Routing Strategy for QoS-Aware Energy Saving in IP Backbone Networks

This paper deals with an energy saving routing solution, called Energy Saving IP Routing (ESIR), to be applied in an IP network. ESIR operation is integrated with Open Shorthest Path First (OSPF) protocol and allows the selection of the links to be switched off so that the negative effects of the IP topology reconfiguration procedures are avoided. The basic mechanisms which ESIR is based on are the concepts of SPT exportation and move. These mechanisms allow to share a Shortest Path Tree (SPT) between neighbor routers, so that the overall set of active network links can be reduced. Properties of moves are defined and the energy saving problem in an IP network is formulated as the problem of finding the Maximum Set of Compatible Moves (MSCM). The MSCM problem is investigated in two steps: firstly, a relaxed version of the problem, named basic MSCM problem, is considered in which QoS requirements are neglected; in the second step, the solution of the full problem, named QoS-aware MSCM problem, is faced. We prove that the basic MSCM problem can be formulated as the well-known Maximum Clique Problem in a graph; instead the QoS-aware MSCM introduces a condition equivalent to the Knapsack problem. ILP formulations to solve both the problems are given and heuristics to solve them in practical cases are proposed. The performance evaluation shows that in a real ISP network scenario ESIR is able to switch off up to 30% of network links by exploiting over-provisioning adopted by operators in the network resource planning phase and typical daily traffic trend.

DiffServ-Aware Pricing for Wireless Internet

In this work we propose a new pricing scheme for the wireless Internet services over WiMAX system. First, let us review the characteristics of wireless network which is based on multi-hop relay WiMAX system. Next, we show why usage-based and QoS-aware pricing scheme is needed in the wireless Internet. After that, we propose a theoretical model for the price of multimedia services called a DAP (DiffServ-aware pricing) scheme for the WiMAX multimedia network which takes into account the consumed radio resource of WiMAX system as well as the supported QoS in the IP backbone network. Finally, we present explicit formulae for the packet price, price of consumed radio resource, and price of consumed bytes.

GRiDA: GReen Distributed Algorithm for energy-efficient IP backbone networks

In this work, we face the problem of reducing the power consumption of Internet backbone networks. We propose a novel algorithm, called GRiDA, to selectively switch off links in an Internet Service Provider IP-based network to reduce the system energy consumption. Differently from approaches that have been proposed in the literature, our solution is completely distributed and thus not requiring any centralized oracle. It leverages link-state protocol like OSPF to share a limited amount of information, and to reduce the problem complexity. Another key feature of GRiDA is that it does not require the knowledge of the actual and past/future traffic matrices, being able to run in real-time, where this information would not be available. Results, obtained on realistic case studies, show that GRiDA achieves performance comparable to several existing centralized algorithms, guaranteeing energy savings up to 50%.

Characterisation of router and link failure processes in UNINETT's IP backbone network

Backbone networks must be highly reliable. The offered availability can be predicted prior to operation if the stochastic behaviour of network components is known. The aim of this paper is provide information about failures and repairs processes in an operational network. Operational logs from UNINETT's core network were analysed to obtain distributions of the time between failures and downtimes of routers and links. The network components were classified according to their role in the network. The measured processes were fit with well-known distributions. The inter-failure times of routers and short distance links may be characterised by a Weibull distribution, but for the long distance links the gamma distribution yielded a better characterisation. The difference is discussed based on the hazard function. The parameters of each network component are made available and provide a detailed insight that may be used for dependability predictions and research.

An approximation method of origin–destination flow traffic from link load counts

Traffic matrix (TM) is a key input of traffic engineering and network management. However, it is significantly difficult to attain TM directly, and so TM estimation is so far an interesting topic. Though many methods of TM estimation are proposed, TM is generally unavailable in the large-scale IP backbone networks and is difficult to be estimated accurately. This paper proposes a novel method of TM estimation in large-scale IP backbone networks, which is based on the generalized regression neural network (GRNN), called GRNN TM estimation (GRNNTME) method. Firstly, building on top of GRNN, we present a multi-input and multi-output model of large-scale TM estimation. Because of the powerful capability of learning and generalizing of GRNN, the output of our model can sufficiently capture the spatio-temporal correlations of TM. This ensures that the estimation of TM can accurately be attained. And then GRNNTME uses the procedure of data posttreating further to make the output of our model closer to real value. Finally, we use the real data from the Abilene Network to validate GRNNTME. Simulation results show that GRNNTME can perform well the accurate and fast estimation of TM, track its dynamics, and holds the stronger robustness and lower estimation errors.