Generating Representative ISP Technologies From First-Principles

Understanding and modeling the factors that underlie the growth and evolution of network topologies are basic questions that impact capacity planning, forecasting, and protocol research. Early topology generation work focused on generating network-wide connectivity maps, either at the AS-level or the router-level, typically with an eye towards reproducing abstract properties of observed topologies. But recently, advocates of an alternative "first-principles" approach question the feasibility of realizing representative topologies with simple generative models that do not explicitly incorporate real-world constraints, such as the relative costs of router configurations, into the model. Our work synthesizes these two lines by designing a topology generation mechanism that incorporates first-principles constraints. Our goal is more modest than that of constructing an Internet-wide topology: we aim to generate representative topologies for single ISPs. However, our methods also go well beyond previous work, as we annotate these topologies with representative capacity and latency information. Taking only demand for network services over a given region as input, we propose a natural cost model for building and interconnecting PoPs and formulate the resulting optimization problem faced by an ISP. We devise hill-climbing heuristics for this problem and demonstrate that the solutions we obtain are quantitatively similar to those in measured router-level ISP topologies, with respect to both topological properties and fault-tolerance

Dynamic update of shortest path tree in OSPF

2003-2004 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Low-Latency Routing on Mesh-Like Backbones

Early in in the Internet's history, routing within a single provider's WAN centered on placing traffic on the shortest path. More recent traffic engineering efforts aim to reduce congestion and/or increase utilization within the status quo of greedy shortest-path first routing on a sparse topology. In this paper, we argue that this status quo of routing and topology is fundamentally at odds with placing traffic so as to minimize latency for users while avoiding congestion. We advocate instead provider backbone topologies that are more mesh-like, and hence better at providing multiple low-latency paths, and a routing system that directly considers latency minimization and congestion avoidance while dynamically placing traffic on multiple unequal-cost paths. We offer a research agenda for achieving this new low-latency approach to WAN topology design and routing

Efficient Algorithms to Enhance Recovery Schema in Link State Protocols

With the increasing demands for real-time applications traffic in net- works such as video and voice a high convergence time for the existing routing protocols when failure occurred is required. These applications can be very sensitive to packet loss when link/node goes down. In this paper, we propose two algorithms schemas for the link state protocol to reroute the traffic in two states; first, pre-calculated an alternative and disjoint path with the primary one from the source to the destination by re-routing traffic through it, regardless of the locations of failure and the number of failed links. Second, rerouting the traffic via an alternative path from a node whose local link is down without the need to wait until the source node knows about the failure. This is achieved by creating a new backup routing table based on the original routing table which is computed by the dijkstra algorithm. The goal of these algorithms is to reduce loss of packets, end-to-end delay time, improve throughput and avoiding local loop when nodes re-converge the topology in case of failure.Comment: 15 page

Self-Management GRID Services – A Programmable Network Approach

Due to the complexity and size of service oriented GRIDs, it is essential that GRID systems should be autonomous i.e. a self-management system is needed. This paper identifies the requirements of such a self-management GRID system and the required supporting services. This paper suggests that these supporting services should be deployed in the form of software modules through programmable techniques. This paper presents a communication protocol for dynamic self-configuration in programmable GRIDs as an example for supporting new network services

Analisa Performansi Penanganan Kegagalan Link Pada Layer 2 pada Jaringan Sofware-Defined Network OpenFlow

Software Defined Networking (SDN) adalah sebuah paradigma yang merubah cara merancang, mengatur dan mengontrol jaringan. Inti dari SDN adalah membuat jaringan yang dapat diprogram. Salah satu protokol jaringan yang dapat mendukung SDN yaitu OpenFlow. OpenFlow didesain dan dikembangkan oleh Standford University yang memisahkan antara perangkat kontrol (control plane) dan perangkat penerus/penyalur paket data (data plane). Pada pengembangan OpenFlow muncul permasalahan utama yaitu reliability. Dalam penelitian ini dibuat sebuah algoritma prototipe penanganan kegagalan link dan menguji kinerjanya dengan membandingkan waktu pergantian jalur dan overhead dengan metode-metode yang sudah ada pada kontroler POX OpenFlow. Pada penelitian ini algoritma prototipe penanganan kegagalan link memerlukan waktu rata-rata 59 mili detik dengan overhead 10.1%. Ini menunjukan masih belum memenuhi standar carrier grade sebesar 50 mili detik. Tetapi algoritma prototipe memiliki kinerja lebih baik dibanding dengan metode yang sudah ada pada kontroler POX OpenFlow. Kata kunci : SDN, OpenFlow, kegagalan lin

Is machine learning ready for traffic engineering optimization?

Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution).This work was supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia as well as the European Social Fund.Peer ReviewedPostprint (author's final draft

MAGNNETO: A graph neural network-based multi-agent system for traffic engineering

Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based solutions for Traffic Engineering (TE), which is a fundamental problem in Internet Service Provider (ISP) networks. Nowadays, state-of-the-art TE optimizers rely on traditional optimization techniques, such as Local search, Constraint Programming, or Linear programming. In this paper, we present MAGNNETO, a distributed ML-based framework that leverages Multi-Agent Reinforcement Learning and Graph Neural Networks for distributed TE optimization. MAGNNETO deploys a set of agents across the network that learn and communicate in a distributed fashion via message exchanges between neighboring agents. Particularly, we apply this framework to optimize link weights in Open Shortest Path First (OSPF), with the goal of minimizing network congestion. In our evaluation, we compare MAGNNETO against several state-of-the-art TE optimizers in more than 75 topologies (up to 153 nodes and 354 links), including realistic traffic loads. Our experimental results show that, thanks to its distributed nature, MAGNNETO achieves comparable performance to state-of-the-art TE optimizers with significantly lower execution times. Moreover, our ML-based solution demonstrates a strong generalization capability to successfully operate in new networks unseen during training.This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GBC21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA), the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia, and the European Social Fund.Peer ReviewedPostprint (author's final draft