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Register a new userScouting the Path to a Million-Client Server
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Added by
Ahmed Saeed
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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To keep up with demand, servers will scale up to handle hundreds of thousands of clients simultaneously. Much of the focus of the community has been on scaling servers in terms of aggregate traffic intensity (packets transmitted per second). However, bottlenecks caused by the increasing number of concurrent clients, resulting in a large number of concurrent flows, have received little attention. In this work, we focus on identifying such bottlenecks. In particular, we define two broad categories of problems; namely, admitting more packets into the network stack than can be handled efficiently, and increasing per-packet overhead within the stack. We show that these problems contribute to high CPU usage and network performance degradation in terms of aggregate throughput and RTT. Our measurement and analysis are performed in the context of the Linux networking stack, the the most widely used publicly available networking stack. Further, we discuss the relevance of our findings to other network stacks. The goal of our work is to highlight considerations required in the design of future networking stacks to enable efficient handling of large numbers of clients and flows.
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The growing size of data center and HPC networks pose unprecedented requirements on the scalability of simulation infrastructure. The ability to simulate such large-scale interconnects on a simple PC would facilitate research efforts. Unfortunately, as we first show in this work, existing shared-memory packet-level simulators do not scale to the sizes of the largest networks considered today. We then illustrate a feasibility analysis and a set of enhancements that enable a simple packet-level htsim simulator to scale to the unprecedented simulation sizes on a single PC. Our code is available online and can be used to design novel schemes in the coming era of omnipresent data centers and HPC clusters.
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