Search

Public clouds are one of the most thriving technologies of the past decade. Major applications over public clouds require world-wide distribution and large amounts of data exchange between their distributed servers. To that end, major cloud providers have invested tens of billions of dollars in building world-wide inter-region networking infrastructure that can support high performance communication into, out of, and across public cloud geographic regions. In this paper, we lay the foundation for a comprehensive study and real time monitoring of various characteristic of networking within and between public clouds. We start by presenting CloudCast, a world-wide and expandable measurements and analysis system, currently (January 2019)collecting data from three major public clouds (AWS, GCPand Azure), 59 regions, 1184 intra-cloud and 2238 cross-cloud links (each link represents a direct connection between a pair of regions), amounting to a total of 3422 continuously monitored links and providing active measurements every minute.CloudCast is composed of measurement agents automatically installed in each public cloud region, centralized control, measurement data base, analysis engine and visualization tools. Then we turn to analyze the latency measurement data collected over almost a year . Our analysis yields surprising results. First, each public cloud exhibits a unique set of link latency behaviors along time. Second, using a novel, fair evaluation methodology, termed similar links, we compare the three clouds. Third, we prove that more than 50% of all links do not provide the optimal RTT through the methodology of triangles. Triangles also provide a framework to get around bottlenecks, benefiting not only the majority (53%-70%) of the cross-cloud links by 30% to 70%, but also a significant portion (29%-45%) of intra-cloud links by 14%-33%., 15 pages

Recently, deep learning has been successfully applied to a variety of networking problems. A fundamental challenge is that when the operational environment for a learning-augmented system differs from its training environment, such systems often make badly informed decisions, leading to bad performance. We argue that safely deploying learning-driven systems requires being able to determine, in real-time, whether system behavior is coherent, for the purpose of defaulting to a reasonable heuristic when this is not so. We term this the online safety assurance problem (OSAP). We present three approaches to quantifying decision uncertainty that differ in terms of the signal used to infer uncertainty. We illustrate the usefulness of online safety assurance in the context of the proposed deep reinforcement learning (RL) approach to video streaming. While deep RL for video streaming bests other approaches when the operational and training environments match, it is dominated by simple heuristics when the two differ. Our preliminary findings suggest that transitioning to a default policy when decision uncertainty is detected is key to enjoying the performance benefits afforded by leveraging ML without compromising on safety.

Recently, deep learning has been successfully applied to a variety of networking problems. A fundamental challenge is that when the operational environment for a learning-augmented system differs from its training environment, such systems often make badly informed decisions, leading to bad performance. We argue that safely deploying learning-driven systems requires being able to determine, in real time, whether system behavior is coherent, for the purpose of defaulting to a reasonable heuristic when this is not so. We term this the online safety assurance problem (OSAP). We present three approaches to quantifying decision uncertainty that differ in terms of the signal used to infer uncertainty. We illustrate the usefulness of online safety assurance in the context of the proposed deep reinforcement learning (RL) approach to video streaming. While deep RL for video streaming bests other approaches when the operational and training environments match, it is dominated by simple heuristics when the two differ. Our preliminary findings suggest that transitioning to a default policy when decision uncertainty is detected is key to enjoying the performance benefits afforded by leveraging ML without compromising on safety., Comment: 8 pages, to appear in The 19th ACM Workshop on Hot Topics in Networks (HotNets 2020)

We present and investigate a novel and timely application domain for deep reinforcement learning (RL): Internet congestion control. Congestion control is the core networking task of modulating traffic sources' data-transmission rates to efficiently utilize network capacity, and is the subject of extensive attention in light of the advent of Internet services such as live video, virtual reality, Internet-of-Things, and more. We show that casting congestion control as RL enables training deep network policies that capture intricate patterns in data traffic and network conditions, and leverage this to outperform the state-of-the-art. We also highlight significant challenges facing real-world adoption of RL-based congestion control, including fairness, safety, and generalization, which are not trivial to address within conventional RL formalism. To facilitate further research and reproducibility of our results, we present a test suite for RL-guided congestion control based on the OpenAI Gym interface., Comment: 10 pages, accepted to ICML 2019