The concept of energy-efficient networking has begun to spread in the past few years, gaining increasing popularity. Besides the widespread sensitivity to ecological issues, such interest also stems from economic needs, since both energy costs and electrical requirements of telcos' and Internet Service Providers' infrastructures around the world show a continuously growing trend. In this respect, a common opinion among networking researchers is that the sole introduction of low consumption silicon technologies may not be enough to effectively curb energy requirements. Thus, for disruptively boosting the network energy efficiency, these hardware enhancements must be integrated with ad-hoc mechanisms that explicitly manage energy saving, by exploiting network-specific features. This paper aims at providing a twofold contribution to green networking. At first, we explore current perspectives in power consumption for next generation networks. Secondly, we provide a detailed survey on emerging technologies, projects, and work-in-progress standards, which can be adopted in networks and related infrastructures in order to reduce their carbon footprint. The considered approaches range from energy saving techniques for networked hosts, to technologies and mechanisms for designing next-generation and energy-aware networks and networking equipment.

Energy Efficiency in the Future Internet: A Survey of Existing Approaches and Trends in Energy-Aware Fixed Network Infrastructures

A roadmap for traffic engineering in SDN-OpenFlow networks

We present the Crossfire attack -- a powerful attack that degrades and often cuts off network connections to a variety of selected server targets (e.g., servers of an enterprise, a city, a state, or a small country) by flooding only a few network links. In Crossfire, a small set of bots directs low intensity flows to a large number of publicly accessible servers. The concentration of these flows on the small set of carefully chosen links floods these links and effectively disconnects selected target servers from the Internet. The sources of the Crossfire attack are undetectable by any targeted servers, since they no longer receive any messages, and by network routers, since they receive only low-intensity, individual flows that are indistinguishable from legitimate flows. The attack persistence can be extended virtually indefinitely by changing the set of bots, publicly accessible servers, and target links while maintaining the same disconnection targets. We demonstrate the attack feasibility using Internet experiments, show its effects on a variety of chosen targets (e.g., servers of universities, US states, East and West Coasts of the US), and explore several countermeasures.

The Crossfire Attack

SDN simplifies network management by relying on declarativity (high-level interface) and expressiveness (network flexibility). We propose a solution to support those features while preserving high robustness and scalability as needed in carrier-grade networks. Our solution is based on (i) a two-layer architecture separating connectivity and optimization tasks; and (ii) a centralized optimizer called framework, which translates high-level goals expressed almost in natural language into compliant network configurations. Our evaluation on real and synthetic topologies shows that framework improves the state of the art by (i) achieving better trade-offs for classic goals covered by previous works, (ii) supporting a larger set of goals (refined traffic engineering and service chaining), and (iii) optimizing large ISP networks in few seconds. We also quantify the gains of our implementation, running Segment Routing on top of IS-IS, over possible alternatives (RSVP-TE and OpenFlow).

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A Declarative and Expressive Approach to Control Forwarding Paths in Carrier-Grade Networks

In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms.

A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

Traffic engineering is an important mechanism for Internet network providers seeking to optimize network performance and traffic delivery. Routing optimization plays a key role in traffic engineering, finding efficient routes so as to achieve the desired network performance. In this survey we review Internet traffic engineering from the perspective of routing optimization. A taxonomy of routing algorithms in the literature is provided, dating from the advent of the TE concept in the late 1990s. We classify the algorithms into multiple dimensions: unicast/multicast, intra-/inter- domain, IP-/MPLS-based and offline/online TE schemes. In addition, we investigate some important traffic engineering issues, including robustness, TE interactions, and interoperability with overlay selfish routing. In addition to a review of existing solutions, we also point out some challenges in TE operation and important issues that are worthy of investigation in future research activities.

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Kin Ho

Papers

An overview of routing optimization for internet traffic engineering