skip to main content

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Thursday, February 13 until 2:00 AM ET on Friday, February 14 due to maintenance. We apologize for the inconvenience.


Title: Scouting the path to a Million-Client Server
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 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  more » « less
Award ID(s):
1816331
PAR ID:
10281279
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
International Conference on Passive and Active Network Measurement
Page Range / eLocation ID:
337-354
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Traditional end-host network stacks are struggling to keep up with rapidly increasing datacenter access link bandwidths due to their unsustainable CPU overheads. Motivated by this, our community is exploring a multitude of solutions for future network stacks: from Linux kernel optimizations to partial hardware o!oad to clean-slate userspace stacks to specialized host network hardware. The design space explored by these solutions would bene"t from a detailed understanding of CPU ine#ciencies in existing network stacks. This paper presents measurement and insights for Linux kernel network stack performance for 100Gbps access link bandwidths. Our study reveals that such high bandwidth links, coupled with relatively stagnant technology trends for other host resources (e.g., core speeds and count, cache sizes, NIC bu$er sizes, etc.), mark a fundamental shift in host network stack bottlenecks. For instance, we "nd that a single core is no longer able to process packets at line rate, with data copy from kernel to application bu$ers at the receiver becoming the core performance bottleneck. In addition, increase in bandwidth-delay products have outpaced the increase in cache sizes, resulting in ine#cient DMA pipeline between the NIC and the CPU. Finally, we "nd that traditional loosely-coupled design of network stack and CPU schedulers in existing operating systems becomes a limiting factor in scaling network stack performance across cores. Based on insights from our study, we discuss implications to design of future operating systems, network protocols, and host hardware. 
    more » « less
  2. Advanced high-speed network cards have made packet processing in host operating systems a major performance bottleneck. The kernel network stack gives rise to various sources of overheads that limit the throughput and lengthen the per-packet processing latency. The problem is further exacerbated for short-lived, latency-sensitive network flows such as control packets, online gaming, database requests, etc. — in a highly utilized system, especially in virtualized (containerized) cloud environments, short flows can experience excessively long in-kernel queuing delays. As a consequence, recent research works propose to bypass the kernel network stack to enable lightweight, custom userspace network stacks for improved performance, but at a heavy cost of compatibility and security. In this paper, we take a different approach: We first analyze various sources of inefficiencies in the kernel network stack and propose ways to mitigate them without compromising systems compatibility, security, or flexibility. Further, we propose PRISM, a novel mechanism in the kernel network stack to differentiate incoming packets based on their performance requirements and streamline the processing stages of multi-stage packet processing pipelines (e.g., in container overlay networks). Our evaluation demonstrates that PRISM can significantly improve the latency of high-priority flows in container overly networks in the presence of heavy low-priority background traffic. 
    more » « less
  3. In many networking scenarios, long-lived flows can be rerouted to free up resources and accommodate new flows, but doing so comes at a cost in terms of disruption. An archetypical example is the transmission of live streams in a content delivery network: audio and video encoders (clients) generate live streams and connect to a server which rebroadcasts their stream to the rest of the network. Reconnecting a client to a different server mid-stream is very disruptive. We abstract these scenarios in the setting of a capacitated network where clients arrive one by one and request to send a unit of flow to a designated set of servers subject to edge/vertex capacity constraints. An online algorithm maintains a sequence of flows that route the clients present so far to the set of servers. The cost of a sequence of flows is defined as the net switching cost, i.e. total length of all augmenting paths used to transform each flow into its successor. We prove that for unit-vertex-capacitated networks, the algorithm that successively updates the flow using the shortest augmenting path from the new client to a free server incurs a total switching cost of O(n log2 n), where n is the number of vertices in the network. This result is obtained by reducing to the online bipartite matching problem studied in prior work and applying their result. Finally, we identify a slightly more general class of networks for which essentially the same reduction idea can be applied to get the same bound. 
    more » « less
  4. Full-system simulation of computer systems is critical for capturing the complex interplay between various hard-ware and software components in future systems. Modeling the network subsystem is indispensable for the fidelity of full-system simulations due to the increasing importance of scale-out systems. Over the last decade, the network software stack has undergone major changes, with userspace networking stacks and data-plane networks rapidly replacing the conventional kernel network stack. Nevertheless, the current state-of-the-art architectural simulator, gem5, still employs kernel networking, which precludes realistic network application scenarios. In this work, we first demonstrate the limitations of gem5's current network stack in achieving high network bandwidth. Then, we enable a userspace networking stack on gem5. We extend gem5's NIC hardware model and device driver to sup-port userspace device drivers running the DPDK framework. Additionally, we implement a network load generator hardware model in gem5 to generate various traffic patterns and per-form per-packet timestamp and latency measurements without introducing packet loss. We develop a suite of six network-intensive benchmarks for stress testing the host network stack. These applications, based on DPDK, can run on both gem5 and real systems. Our experimental results show that enabling userspace networking improves gem5's network bandwidth by 6.3× compared with the current Linux kernel software stack. We characterize the performance of DPDK benchmarks running on both a real system and gem5, and evaluate the sensitivity of the applications to various system and microarchitecture parameters. This work marks the first step in refactoring the networking subsystem in gem5. 
    more » « less
  5. null (Ed.)
    Fast networks and the desire for high resource utilization in data centers and the cloud have driven disaggregation. Application compute is separated from storage, but this leads to high overheads when data must move over the network for simple operations on it. Alternatively, systems could allow applications to run application logic within storage via user-defined functions. Unfortunately, this ties provisioning and utilization of storage and compute resources together again. We present a new approach to executing storage-level functions in an in-memory key-value store that avoids this problem by dynamically deciding where to execute functions over data. Users write storage functions that are logically decoupled from storage, but storage servers choose where to run invocations of these functions physically. By using a server-internal cost model and observing function execution, servers choose to directly run inexpensive functions, while preferring to execute functions with high CPU-cost at client machines. We show that with this approach storage servers can reduce network request processing costs, avoid server compute bottlenecks, and improve aggregate storage system throughput. We realize our approach on an in-memory key-value store that executes 3.2 million strict serializable user-defined storage functions per second with 100 us response times. When running a mix of logic from different applications, it provides throughput better than running that logic purely at storage servers (85% more) or purely at clients (10% more). For our workloads, it also reduces latency (up to 2x) and transactional aborts (up to 33%) over pure client-side execution. 
    more » « less