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  1. Free, publicly-accessible full text available June 17, 2024
  2. Today’s large-scale services (e.g., video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer general and accurate analytics in real time at reasonable costs. The root cause is the combinatorial explosion of data subpopulations and the diversity of summary statistics we need to monitor simultaneously. We present Hydra, an efficient framework for multidimensional analytics that presents a novel combination of using a “sketch of sketches” to avoid the overhead of monitoring exponentially-many subpopulations and universal sketching to ensure accurate estimates for multiple statistics. We build Hydra as an Apache Spark plugin and address practical system challenges to minimize overheads at scale. Across multiple real-world and synthetic multidimensional datasets, we show that Hydra can achieve robust error bounds and is an order of magnitude more efficient in terms of operational cost and memory footprint than existing frameworks (e.g., Spark, Druid) while ensuring interactive estimation times. 
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  3. null (Ed.)
    Sketching algorithms or sketches have emerged as a promising alternative to the traditional packet sampling-based network telemetry solutions. At a high level, they are attractive because of their high resource efficiency and provable accuracy guarantees. While there have been significant recent advances in various aspects of sketching for networking tasks, many fundamental challenges remain unsolved that are likely stumbling blocks for adoption. Our contribution in this paper is in identifying and formulating these research challenges across the ecosystem encompassing network operators, platform vendors/developers, and algorithm designers. We hope that these serve as a necessary fillip for the community to enable the broader adoption of sketch-based telemetry. 
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  4. At the core of Network Functions Virtualization lie Network Functions (NFs) that run co-resident on the same server, contend over its hardware resources and, thus, might suffer from reduced performance relative to running alone on the same hardware. Therefore, to efficiently manage resources and meet performance SLAs, NFV orchestrators need mechanisms to predict contention-induced performance degradation. In this work, we find that prior performance prediction frameworks suffer from poor accuracy on modern architectures and NFs because they treat memory as a monolithic whole. In addition, we show that, in practice, there exist multiple components of the memory subsystem that can separately induce contention. By precisely characterizing (1) the pressure each NF applies on the server's shared hardware resources (contentiousness) and (2) how susceptible each NF is to performance drop due to competing contentiousness (sensitivity), we develop SLOMO, a multivariable performance prediction framework for Network Functions. We show that relative to prior work SLOMO reduces prediction error by 2-5x and enables 6-14% more efficient cluster utilization. SLOMO's codebase can be found at https://github.com/cmu-snap/SLOMO. 
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