This paper explores opportunities to utilize Large Language Models (LLMs) to make network configuration human-friendly, simplifying the configuration of network devices & development of routing algorithms and minimizing errors. We design a set of benchmarks (NetConfEval) to examine the effectiveness of different models in facilitating and automating network configuration. More specifically, we focus on the scenarios where LLMs translate high-level policies, requirements, and descriptions (i.e., specified in natural language) into low-level network configurations & Python code. NetConfEval considers four tasks that could potentially facilitate network configuration, such as (i) generating high-level requirements into a formal specification format, (ii) generating API/function calls from high-level requirements, (iii) developing routing algorithms based on high-level descriptions, and (iv) generating low-level configuration for existing and new protocols based on input documentation. Learning from the results of our study, we propose a set of principles to design LLM-based systems to configure networks. Finally, we present two GPT-4-based prototypes to (i) automatically configure P4-enabled devices from a set of high-level requirements and (ii) integrate LLMs into existing network synthesizers.

Energy consumption,of the Internet is already substantial and it is likely to increase as operators deploy faster equipment,to handle popular bandwidth- intensive services, such as streaming and video-on-demand. Existing work on energy saving considers local adaptation relying primarily on hardware- based techniques, such as sleeping and rate adaptation. We argue that a complete solution requires a network-wide approach that works in conjunc- tion with local measures. However, traditional traffic engineering objectives do not include energy. This paper presents Energy-Aware Traffic engineer- ing (EATe), a technique that takes energy consumption into account while achieving the same traffic rates as the energy-oblivious approaches. EATe uses a scalable, online technique to spread the load among multiple paths so as to increase energy savings. Our extensive ns-2 simulations over re- alistic topologies show that EATe succeeds in moving 21% of the links to the sleep state, while keeping the same sending rates and being close to the optimal energy-aware solution. Further, we demonstrate that EATe suc- cessfully handles changes in traffic load and quickly restores a low overall energy state. Alternatively, EATe can move links to lower energy levels, re- sulting in energy savings of 8%. Finally, EATe can succeed in making 16% of active routers sleep.

The increasing adoption of Software Defined Networking, and OpenFlow in particular, brings great hope for increasing extensibility and lowering costs of deploying new network functionality. A key component in these networks is the OpenFlow agent, a piece of software that a switch runs to enable remote programmatic access to its forwarding tables. While testing high-level network functionality, the correct behavior and interoperability of any OpenFlow agent are taken for granted. However, existing tools for testing agents are not exhaustive nor systematic, and only check that the agent’s basic functionality works. In addition, the rapidly changing and sometimes vague OpenFlow specifications can result in multiple implementations that behave differently. This paper presents SOFT, an approach for testing the interoperability of OpenFlow switches. Our key insight is in automatically identifying the testing inputs that cause different OpenFlow agent implementations to behave inconsistently. To this end, we first symbolically execute each agent under test in isolation to derive which set of inputs causes which behavior. We then crosscheck all distinct behaviors across different agent implementations and evaluate whether a common input subset causes inconsistent behaviors. Our evaluation shows that our tool identified several inconsistencies between the publicly available Reference OpenFlow switch and Open vSwitch implementations.QC 20140704