scispace - formally typeset
Search or ask a question
Author

Klaus-Tycho Foerster

Other affiliations: Technical University of Dortmund, Microsoft, ETH Zurich  ...read more
Bio: Klaus-Tycho Foerster is an academic researcher from University of Vienna. The author has contributed to research in topics: Network topology & Failover. The author has an hindex of 17, co-authored 76 publications receiving 814 citations. Previous affiliations of Klaus-Tycho Foerster include Technical University of Dortmund & Microsoft.

Papers published on a yearly basis

Papers
More filters
Journal ArticleDOI
TL;DR: This paper identifies the different desirable consistency properties that should be provided throughout a network update, the algorithmic techniques which are needed to meet these consistency properties, and the implications on the speed and costs at which updates can be performed.
Abstract: Computer networks have become a critical infrastructure. In fact, networks should not only meet strict requirements in terms of correctness, availability, and performance but they should also be very flexible and support fast updates, e.g., due to policy changes, increasing traffic, or failures. This paper presents a structured survey of mechanism and protocols to update computer networks in a fast and consistent manner. In particular, we identify and discuss the different desirable consistency properties that should be provided throughout a network update, the algorithmic techniques which are needed to meet these consistency properties, and the implications on the speed and costs at which updates can be performed. We also explain the relationship between consistent network update problems and classic algorithmic optimization ones. While our survey is mainly motivated by the advent of software-defined networks and their primary need for correct and efficient update techniques, the fundamental underlying problems are not new, and we provide a historical perspective of the subject as well.

88 citations

Proceedings ArticleDOI
07 Aug 2018
TL;DR: This work argues for adapting the capacity of fiber optic links based on their signal-to-noise ratio (SNR), and proposes RADWAN, a traffic engineering system that allows optical links to adapt their rate based on the observed SNR to achieve higher throughput and availability while minimizing the churn during capacity reconfigurations.
Abstract: Fiber optic cables connecting data centers are an expensive but important resource for large organizations. Their importance has driven a conservative deployment approach, with redundancy and reliability baked in at multiple layers. In this work, we take a more aggressive approach and argue for adapting the capacity of fiber optic links based on their signal-to-noise ratio (SNR). We investigate this idea by analyzing the SNR of over 8,000 links in an optical backbone for a period of three years. We show that the capacity of 64% of 100 Gbps IP links can be augmented by at least 75 Gbps, leading to an overall capacity gain of over 134 Tbps. Moreover, adapting link capacity to a lower rate can prevent up to 25% of link failures. Our analysis shows that using the same links, we get higher capacity, better availability, and 32% lower cost per gigabit per second. To accomplish this, we propose RADWAN, a traffic engineering system that allows optical links to adapt their rate based on the observed SNR to achieve higher throughput and availability while minimizing the churn during capacity reconfigurations. We evaluate RADWAN using a testbed consisting of 1,540 km fiber with 16 amplifiers and attenuators. We then simulate the throughput gains of RADWAN at scale and compare them to the gains of state-of-the-art traffic engineering systems. Our data-driven simulations show that RADWAN improves the overall network throughput by 40% while also improving the average link availability.

57 citations

Proceedings ArticleDOI
23 Jul 2018
TL;DR: The results show that classical matching algorithms, as used in prior work, are optimal only when the topology consists of one reconfigurable switch, and the routing policy is enforced to be segregated, and it is shown that optimally routing even two flows in a network with multiple reconfigured switches is an NP-hard problem.
Abstract: Emerging data center architectures are becoming reconfigurable. While prior work has shown the practical benefits of reconfigurable topologies, the underlying algorithmic complexity is not yet well understood. In particular, most reconfigurable topologies are hybrid, where parts of the network are reconfigurable (consisting of optical or wireless devices) while other parts are static (consisting of electrical switches). Current proposals enforce a routing policy that routes flows on either part "exclusively" by labeling flows as mice or elephant. We show that such artificial segregation in routing policy results in non-optimal paths and argue for algorithms that route packets across the network seamlessly. In doing so, we present the first algorithmic study of reconfigurable network architectures and provide optimality and hardness proofs in terms of topology and routing policy. Our results show that classical matching algorithms, as used in prior work, are optimal only when the topology consists of one reconfigurable switch, and the routing policy is enforced to be segregated. In other words, if there is an option of routing flows seamlessly along reconfigurable and non-reconfigurable parts of the network, matching algorithms are not optimal. In fact, when the hybrid network is seen from a joint perspective, optimal routing is an NP-hard problem. We further show that optimally routing even two flows in a network with multiple reconfigurable switches is an NP-hard problem as well.

55 citations

Journal ArticleDOI
TL;DR: This paper provides an overview of the algorithmic problems introduced by this technology, and surveys rst solutions.
Abstract: Emerging optical technologies introduce opportunities to recon gure network topologies at runtime. The resulting topological exibilities can be exploited to design novel demand-aware and self-adjusting networks. This paper provides an overview of the algorithmic problems introduced by this technology, and surveys rst solutions.

52 citations

Journal ArticleDOI
TL;DR: An overview of the cost models used in the literature as well as the algorithmic problems introduced by these technologies, their first solutions, and discuss systems and implementation aspects are provided.

37 citations


Cited by
More filters
Book ChapterDOI
Eric V. Denardo1
01 Jan 2011
TL;DR: This chapter sees how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models” and finds an optimal solution that is integer-valued.
Abstract: In this chapter, you will see how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models.” You will also see that if a network flow model has “integer-valued data,” the simplex method finds an optimal solution that is integer-valued.

828 citations

Book
01 Jan 2002
TL;DR: In this paper, the value of the variable in each equation is determined by a linear combination of the values of the variables in the equation and the variable's value in the solution.
Abstract: Determine the value of the variable in each equation.

635 citations

01 Dec 2015
TL;DR: TensorFlow 2.0 in ActionTensor Flow 1.x Deep Learning Cookbook machine Learning with TensorFlow, Second EditionTensor flow 2 Pocket PrimerProgramming with Tensing, Tensor Flow Machine Learning Projects, and Hands-On Neural Networks.
Abstract: TensorFlow 2.0 in ActionTensorFlow 1.x Deep Learning CookbookMachine Learning with TensorFlow 1.xMachine Learning with TensorFlow, Second EditionTensorFlow 2 Pocket PrimerProgramming with TensorFlowTensorFlow Machine Learning ProjectsHands-On Neural Networks with TensorFlow 2.0TensorFlow for Deep LearningTensor Flow Pocket PrimerNatural Language Processing with TensorFlowTensorFlow: Powerful Predictive Analytics with TensorFlowHands-On Convolutional Neural Networks with TensorFlowTensorFlow 2.0 Computer Vision CookbookIntelligent Mobile Projects with TensorFlowLearning TensorFlow.jsDeep Learning with TensorFlow 2 and KerasLearning TensorFlowTensorFlow 2 Pocket ReferenceMachine Learning Using TensorFlow CookbookTensorFlow 2.0 Quick Start GuideTensorFlow Machine Learning CookbookLearn TensorFlow 2.0Learn TensorFlow in 24 HoursHands-On Computer Vision with TensorFlow 2Mastering Computer Vision with TensorFlow 2.xPro Deep Learning with TensorFlowHands-On Machine Learning with TensorFlow.jsTensorFlow for Deep LearningTinyMLLearning TensorFlow.jsDeep Learning with TensorFlow 2 and Keras Second EditionDeep Learning with TensorFlowMastering TensorFlow 1.xAdopting TensorFlow for Real-World AITensorFlow For DummiesArtificial Intelligence with PythonHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlowLearn TensorFlow EnterpriseThe TensorFlow Workshop

306 citations

Journal ArticleDOI
TL;DR: A comprehensive up-to-date survey of the research and development in the field of hybrid SDN networks is presented and guidelines for future research on hybridSDN networks are derived.
Abstract: Software defined networking (SDN) decouples the control plane from the data plane of forwarding devices. This separation provides several benefits, including the simplification of network management and control. However, due to a variety of reasons, such as budget constraints and fear of downtime, many organizations are reluctant to fully deploy SDN. Partially deploying SDN through the placement of a limited number of SDN devices among legacy (traditional) network devices, forms a so-called hybrid SDN network. While hybrid SDN networks provide many of the benefits of SDN and have a wide range of applications, they also pose several challenges. These challenges have recently been addressed in a growing body of literature on hybrid SDN network structures and protocols. This paper presents a comprehensive up-to-date survey of the research and development in the field of hybrid SDN networks. We have organized the survey into five main categories, namely hybrid SDN network deployment strategies, controllers for hybrid SDN networks, protocols for hybrid SDN network management, traffic engineering mechanisms for hybrid SDN networks, as well as testing, verification, and security mechanisms for hybrid SDN networks. We thoroughly survey the existing hybrid SDN network studies according to this taxonomy and identify gaps and limitations in the existing body of research. Based on the outcomes of the existing research studies as well as the identified gaps and limitations, we derive guidelines for future research on hybrid SDN networks.

236 citations