Dynamic Traffic Engineering Research Articles

Dynamic Traffic Engineering Considering Service Grade in Integrated Service Network

This study proposes a dynamic traffic engineering method to improve network resource utilization efficiency without affecting important services in an integrated service network where multiple grades of service are accommodated. Network resource utilization is biased over time because future fluctuations in traffic demand are unpredictable. Although a service network provider prefers to re-optimize network resource utilization using a dynamic traffic engineering, it is often superfluous for users who do not want their mission-critical communications, for example, executive web meetings or online surgery, to be affected. Therefore, we classified traffic into very important packets and commoners, and only commoners were dynamically optimized. To classify traffic, we assigned the identifier to a packet on the application side. In this approach, only the application and edge nodes of the network required modification; the core network routers were unaffected. Furthermore, we established the architecture of the edge node using a software-defined network controller and P4 switch. We also provided an algorithm for optimization based on linear programming and demonstrated that it is possible to improve network resource utilization efficiency without affecting very important packets and that the computational and memory costs are practical.

Energy Efficiency in Sliceable-Transponder Enabled Elastic Optical Networks

The Sliceable Bandwidth-Variable Transponder (SBVT) enables greater flexibility in elastic optical networks (EON), providing energy efficiency and reducing the resource wastage. Another feature offered by SBVTs is the ability to perform optical grooming. Along with electric grooming and a multilevel adaptive modulation, it provides many possibilities in dynamic traffic engineering. A key problem is how to coordinate all these features while promoting energy efficiency and good network performance. In this paper, an unprecedented integer linear programming (ILP) model is developed to optimally solve this problem. The model covers all traffic engineering characteristics in translucent SBVT enabled EONs with electrical and optical grooming for dynamic traffic scenarios. Due to the non-scalability of the ILP model, a corresponding heuristic algorithm based on the auxiliary graph (AG) model is proposed. It can support various traffic engineering policies for different purposes by adjusting the weights of AG edges. Comprehensive comparisons are performed for the ILP model, the AG heuristic, and other online algorithms from the literature. Numerical results show that the AG heuristic provides a gain of two orders of magnitude in the bandwidth blocking ratio and achieves a gain of up to 51% in energy efficiency compared to literature proposals.

BiTE: a dynamic bi-level traffic engineering model for load balancing and energy efficiency in data center networks

With the recent significant growth of virtualization and cloud services, the data center network (DCN) as the underlying infrastructure is more important. The increasing and changing volume of workloads highlights critical issues such as load balancing and energy efficiency in data centers. Large path diversity in DCNs introduces multipath forwarding as a promising approach to improve load distribution. However, the over-provisioned DCNs consume large amounts of power while the network is under full capacity most of the time. Accordingly, this paper proposes BiTE, a dynamic bi-level traffic engineering (TE) scheme in a hierarchical Software Defined Networking (SDN)-based DCN to strike a balance between load balancing and energy efficiency objectives. BiTE consists of decision-making problems at two levels modeled as a multi-period bi-level optimization problem, where each decision maker optimizes one of objectives. According to the inherent complexity of bi-level programming, a co-evolutionary metaheuristic algorithm is proposed for solving BiTE. BiTE performance is evaluated in comparison to NSGA-II algorithm and four previously proposed TE schemes in terms of several load balancing and energy saving metrics under different scenarios. The results show that BiTE performs well in traffic load balancing while preserves the energy efficiency. We apply the Analytic Hierarchy Process (AHP) method to multi-criteria analyze and rank the performance of studied TE mechanisms. AHP results for different scenarios indicate that BiTE is in first or second place in terms of the overall performance score among six studied approaches.

Effective Route Scheme of Multicast Probing to Locate High-loss Links in OpenFlow Networks

With the prevalence of cloud computing and contents delivery networking, OpenFlow-based centrally-managed networks with flexible and dynamic traffic engineering are demanded. To maintain a high quality of service of the network, detecting and locating high-loss links is essential. Therefore, in this paper, a measurement framework is proposed to promptly locate all high-loss links with a minimized load on both data-plane and control-plane incurred by the measurement, which assumes only standard OpenFlow functions. It combines an active measurement by probing multicast packets along a designed route and a passive measurement by collecting flow-stats of the probing flow at selected switch ports in an appropriate sequential order to access switches. In particular, by designing the measurement route based on the backbone-and-branch tree (BBT) route scheme, the measurement accuracy and the measurement overhead (i.e., the number of accesses to switches until locating all high-loss links) can be balanced. The numerical simulation demonstrates the effectiveness of our proposal.

Traffic Engineering in Software-defined Networks using Reinforcement Learning: A Review

With the exponential increase in connected devices and its accompanying complexities in network management, dynamic Traffic Engineering (TE) solutions in Software-Defined Networking (SDN) using Reinforcement Learning (RL) techniques has emerged in recent times. The SDN architecture empowers network operators to monitor network traffic with agility, flexibility, robustness and centralized control. The separation of the control and the forwarding plane in SDN has enabled the integration of RL agents in the networking architecture to enforce changes in traffic patterns during network congestions. This paper surveys major RL techniques adopted for efficient TE in SDN. We reviewed the use of RL agents in modelling TE policies for SDNs, with agents’ actions on the environment guided by future rewards and a new state. We further looked at the SARL and MARL algorithms the RL agents deploy in forming policies for the environment. The paper finally looked at agents design architecture in SDN and possible research gaps.

The Joint Optimization of Online Traffic Matrix Measurement and Traffic Engineering For Software-Defined Networks

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

DTE-SDN: A Dynamic Traffic Engineering Engine for Delay-Sensitive Transfer

With ever-rapid development of information and communication technologies, e.g., smart city, industrial Internet, Internet of Things, etc., enormous amounts of data are explosively generated and delivered to the computing center for further processing, which brings additional burden to both transfer components (e.g., the Internet) and data processing units. To efficiently schedule data transfer especially for delay-sensitive traffic, a scalable network architecture with intelligent traffic engineering (TE) policy is indispensable. In this paper, we propose a TE engine DTE-SDN by utilizing the software defined networking (SDN) technology, aiming to schedule the transfer of delay-sensitive traffic. Particularly, with OpenFlow, DTE-SDN captures an overall view of the network in real-time and can monitor the quality of service (QoS) metrics (e.g., throughput and delay) of each network link. In order to schedule the delay-sensitive transfer, a dynamic-scheduling scheme capable of multipath routing is proposed, which allows DTE-SDN to compute the near-optimal scheduling (e.g., path selection and flow distribution), based on the instantaneous QoS metrics. Especially, in the scheduling scheme, we propose a probabilistic-matching approach that aims to distribute the traffic among multiple end-to-end paths according to the computed flow distribution policy and can be deployed in SDN-enabled switches (e.g., Open vSwitch). The simulation results demonstrate that DTE-SDN is able to measure the throughput and delay with acceptable error range and can dramatically enhance the transfer efficiency than the traditional scheduling algorithms.

P4 Edge Node Enabling Stateful Traffic Engineering and Cyber Security

Next-generation edge nodes interfacing innovative IT clusters, 5G fronthaul, and internet of things (IoT) gateways to the optical metro/core network will require advanced and dynamic online quality of service (QoS) per-flow traffic treatment, assuring ultra-low latency requirements. However, current software-defined networking (SDN) implementations (e.g., OpenFlow) do not support forwarding procedures based on the network state, profile variations, and the history of flow statistics at the node level. Currently, such procedures require intervention by the SDN controller, leading to scalability issues and additional latency in data plane forwarding. Moreover, severe security challenges are expected to affect such nodes and threaten IT resources. Thus, increasing bandwidths will require direct deep packet inspection to avoid involvement of the SDN controller, as performed currently, or dedicated and costly security systems. This paper leverages on the potential of the programming protocol-independent packet processors (P4) open source language, recently introduced by the inventors of OpenFlow, to program the data plane structure and behavior of an SDN switch. P4 is able to instantiate custom pipelines and stateful objects, enabling complex workflows, user-defined protocols/headers, and finite state machines enforcement. Moreover, P4 allows portable implementations over different hardware targets, thus opening the way to open source fully programmable devices. Special effort is dedicated to motivate and apply P4 within a multilayer edge scenario, proposing the architecture and the applicability of an SDN P4-enabled packet-over-optical node. Moreover, three specific multilayer use cases covering dynamic traffic engineering (TE) (e.g., traffic offload and optical bypass) and cybersecurity (e.g., distributed denial of service port scan) are discussed and addressed through P4-based solutions. Experimental evaluations have been conducted over a multilayer SDN network exploiting reference P4 software switches (i.e., the behavioral model version 2, or BMV2) and field-programmable gate arrays (FPGAs) at 10 gigabit Ethernet optical interfaces. Extensive results report effective dynamic TE and cybersecurity mitigation enforcement at P4 switches without any controller intervention, showing excellent scalability performance and overall latencies practically in line with current commercial OpenFlow switches.

OpenIPTV: a comprehensive SDN-based IPTV service framework

IPTV is an emerging TV content delivery service that should guarantee Quality of Service (QoS) to deliver television contents over IP for their customers. However, providing such QoS regarding service level agreements (SLA) requires frequent service monitoring and adaptive configuration mechanisms. Nowadays, Software Defined Networks (SDNs) provide capabilities to deploy and manage networks dynamically and can maintain QoS. In this paper, a novel IPTV service framework (OpenIPTV) is proposed, which utilizes SDN as an underlying technology for providing QoS for IPTV customers in a shared backbone network. OpenIPTV is implemented in a well-known OpenDayLight controller and strictly followed a modular design for the sake of efficiency. OpenIPTV comprises all service requirements such as resource monitoring, channel changing, multicast group managing and dynamic QoS multicast traffic engineering. The performance of OpenIPTV is evaluated under different scenarios and experimental results confirmed the effectiveness of the proposed framework in terms of QoS metrics. Furthermore, experimental results show that OpenIPTV is a feasible and practical solution to deliver IPTV services with high level of QoS over SDN.

Energy-Efficient dynamic virtual network traffic engineering for north-south traffic in multi-location data center networks

We consider the problem of allocating data center (DC) resources for cloud enterprise customers who require guaranteed services on demand. In particular, a request from an enterprise customer is mapped to a virtual network (VN) class that is allocated both bandwidth and compute resources by connecting it from an entry point of a data center to one or more hosts while there are multiple geographically distributed data centers to choose from. We take a dynamic traffic engineering approach over multiple time periods in which an energy-aware resource reservation model is solved at each review point. For the energy-aware resource reservation problem, we present a mixed-integer linear programming (MILP) formulation (for small-scale problems) and a heuristic approach (for large-scale problems). Our heuristic is fast for solving large-scale problems where the MILP problem becomes difficult to solve. Through a comprehensive set of studies, we found that a VN class with a low resource requirement has a low blocking even in heavy traffic, while the VN class with a high resource requirement faces a high service denial. Furthermore, the VN class having randomly distributed resource requirement has a high provisioning cost and blocking compared to the VN class having the same resource requirement for each request although the average resource requirement is same for both these VN classes. We also observe that our approach reduces the maximum energy consumption by about one-sixth at the low arrival rate to by about one-third at the highest arrival rate—this also depends on how many different CPU frequency levels a server can run at.

Segment Routing for Effective Recovery and Multi-domain Traffic Engineering

Segment routing is an emerging traffic engineering technique relying on Multi-protocol Label-Switched (MPLS) label stacking to steer traffic using the source-routing paradigm. Traffic flows are enforced through a given path by applying a specifically designed stack of labels (i.e., the segment list). Each packet is then forwarded along the shortest path toward the network element represented by the top label. Unlike traditional MPLS networks, segment routing maintains a per-flow state only at the ingress node; no signaling protocol is required to establish new flows or change the routing of active flows. Thus, control plane scalability is greatly improved. Several segment routing use cases have recently been proposed. As an example, it can be effectively used to dynamically steer traffic flows on paths characterized by low latency values. However, this may suffer from some potential issues. Indeed, deployed MPLS equipment typically supports a limited number of stacked labels. Therefore, it is important to define the proper procedures to minimize the required segment list depth. This work is focused on two relevant segment routing use cases: dynamic traffic recovery and traffic engineering in multi-domain networks. Indeed, in both use cases, the utilization of segment routing can significantly simplify the network operation with respect to traditional Internet Protocol (IP)/MPLS procedures. Thus, two original procedures based on segment routing are proposed for the aforementioned use cases. Both procedures are evaluated including a simulative analysis of the segment list depth. Moreover, an experimental demonstration is performed in a multi-layer test bed exploiting a software-defined-networking-based implementation of segment routing.

Dynamic traffic engineering for high-throughput data forwarding in wireless mesh networks

High-throughput data delivery in Wireless Mesh Networks (WMNs) is a challenging problem due to dynamic changes of link quality, interference and congestion. In this work, we first develop an optimization framework for Dynamic Traffic Engineering (O-DTE) in WMNs that aims to minimize the interference and congestion at each hop through joint power and rate control so as to achieve high-throughput data delivery. Due to NP-hardness of the O-DTE framework, we then develop a greedy heuristic alternate solution (G-DTE) that enables routers, at each hop, to select outgoing links offering higher data rates and reduced interferences. Thus, the proposed G-DTE produces near optimal results by taking multi-path data forwarding decisions in distributed fashion; it exploits single-hop neighborhood information only and thus it is scalable. The simulation results, carried out in ns-3, demonstrate that the proposed G-DTE significantly outperforms the state-of-the-art works in terms of throughput, delay, reliability and fairness performances.

OpenFlow-controlled QoS-aware dynamic traffic engineering for differentiated service provisioning in hybrid IPv4/IPv6 networks

OpenFlow-controlled QoS-aware dynamic traffic engineering for differentiated service provisioning in hybrid IPv4/IPv6 networks

Traffic prediction for dynamic traffic engineering

Traffic engineering with traffic prediction is a promising approach to accommodate time-varying traffic without frequent route changes. In this approach, the routes are decided so as to avoid congestion on the basis of the predicted traffic. However, if the range of variation including temporal traffic changes within the next control interval is not appropriately decided, the route cannot accommodate the shorter-term variation and congestion still occurs. To solve this problem, we propose a prediction procedure to consider the short-term and longer-term future traffic demands. Our method predicts the longer-term traffic variation from the monitored traffic data. We then take account of the short-term traffic variation in order to accommodate prediction uncertainty incurred by temporal traffic changes and prediction errors. We use the standard deviation to estimate the range of short-term fluctuation. Through the simulation using actual traffic traces on a backbone network of Internet2, we show that traffic engineering using the traffic information predicted by our method can set up routes that accommodate traffic variation for several or more hours with efficient load balancing. As a result, we can reduce the required bandwidth by 18.9% using SARIMA with trend component compared with that of the existing traffic engineering methods.

First Demonstration of a Cross-Layer Enabled Network Node

Exploding traffic demands and increasing energy consumptions facing today's networks are driving the designs of next-generation networking technologies. Cross-layer enabled approaches will allow for the packet-level control of the optical layer, to enable dynamic resource allocation and traffic engineering at the physical layer. We demonstrate an intelligent cross-layer enabled network node that can support high-bandwidth, all-optically routed packets, using emerging photonic technologies including optical packet switched fabrics and packet-scale performance monitoring. Using a cross-layer control and management plane, the node can dynamically optimize optical switching based on higher-layer constraints such as quality-of-service and energy consumption, as well as quality-of-transmission metrics such as link integrity and bit-error rates. We demonstrate a first-generation prototype of the cross-layer node, outlining its architecture and major implemented subsystems. The packet-rate physical-layer reconfiguration of the node's fabric is shown using an implemented performance monitor and control plane. The realized node supports 8 40-Gb/s wavelength-striped optical packets with pseudorandom data with error-free transmission (bit-error rates less than ), in conjunction with the heterogeneous transmission of video traffic using 10-Gigabit Ethernet optical network interface cards based on field-programmable gate arrays.

Managing the future internet through intelligent in-network substrates

The current Internet has been founded on the architectural premise of a simple network service used to interconnect relatively intelligent end systems. While this simplicity allowed it to reach an impressive scale, the predictive manner in which ISP networks are currently planned and configured through external management systems and the uniform treatment of all traffic are hampering its use as a unifying multi-service network. The future Internet will need to be more intelligent and adaptive, optimizing continuously the use of its resources and recovering from transient problems, faults and attacks without any impact on the demanding services and applications running over it. This article describes an architecture that allows intelligence to be introduced within the network to support sophisticated self-management functionality in a coordinated and controllable manner. The presented approach, based on intelligent substrates, can potentially make the Internet more adaptable, agile, sustainable, and dependable given the requirements of emerging services with highly demanding traffic and rapidly changing locations. We discuss how the proposed framework can be applied to three representative emerging scenarios: dynamic traffic engineering (load balancing across multiple paths); energy efficiency in ISP network infrastructures; and cache management in content-centric networks.

Towards low-complexity Internet traffic engineering: The Adaptive Multi-Path algorithm

In this paper, we describe a novel algorithm for dynamic traffic engineering in the Internet called Adaptive Multi-Path routing (AMP). The main objective of AMP is to distribute load within a network domain in a continuous manner by offloading congested links in real-time. The algorithm's recursive signaling structure represents one of our central contributions, as it restricts information exchange to neighboring nodes while still maintaining global propagation of congestion information. This paper starts with a comprehensive survey of related literature, before we introduce AMP in depth and present our exhaustive performance evaluation which is carried out using full implementations of the algorithm both in packet-level and flow-level simulation environments. The investigated state-of-the-art topologies and traffic models provide a detailed insight into the behavior of AMP in realistic operators' networks of substantial size and complexity, whereas our results demonstrate both the traffic engineering efficiency and the stability of AMP. Finally, we present important areas of application for AMP in emerging networking architectures and conclude the paper with an outlook on future research.

Dynamic traffic engineering for mixed traffic on international networks

In this paper, a novel distributed dynamic traffic engineering (Dynamic TE) mechanism is proposed. The mechanism periodically updates bandwidth reservation and selects the optimum path (resizing and rerouting) for each TE-LSP according to its computed traffic load, leading to path reoptimization and better network utilization. Different resizing policies are investigated and their effect on QoS is analyzed. Detailed performance analysis is then undertaken using simulations on conditions similar to an international transit network. A mixed load of voice and data traffic originating in different timezones is used on a realistic network where all the links have an independent probability of failure. The simulation results show significant performance improvement using Dynamic TE for several metrics of interest and give insight into several scenarios that could benefit from its deployment.

Generic multipath routing concept for dynamic traffic engineering

This letter presents a generic adaptive multipath routing solution. The method, called Core-State-Limited Load Sharing (CSLLS), has two parameters. One determines the adaptation time to load changes and the other one controls how long the path of a flow is unchanged. With these two parameters CSLLS is capable of modelling previous multipath routing concepts like ECMP, OMP and QoSPF. CSLLS overcomes the limitations of earlier approaches by being adaptive (opposed to ECMP), by ensuring path integrity for a flow (opposed to OMP), and by relying on only a few stored states in core routers (opposed to QoSPF)