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Abstract
Containers are prevalently adopted due to the deployment and performance advantages over virtual machines. For many containerized data-intensive applications, however, bulky data transfers may pose performance issues. In particular, communication across colocated containers on the same host incurs large overheads in memory copy and the kernel’s TCP stack. Existing solutions such as shared-memory networking and RDMA have their own limitations, including insufficient memory isolation and limited scalability.
This paper presents PipeDevice, a new system for low overhead intra-host container communication. PipeDevice follows a hardware-software co-design approach — it offloads data forwarding entirely onto hardware, which accesses application data in hugepages on the host, thereby eliminating CPU overhead from memory copy and TCP processing. PipeDevice preserves memory isolation and scales well to connections, making it deployable in public clouds. Isolation is achieved by allocating dedicated memory to each connection from hugepages. To achieve high scalability, PipeDevice stores the connection states entirely in host DRAM and manages them in software. Evaluation with a prototype implementation on commodity FPGA shows that for delivering 80 Gbps across containers PipeDevice saves 63.2% CPU compared to kernel TCP stack, and 40.5% over FreeFlow. PipeDevice provides salient benefits to applications. For example, we port baidu-allreduce to PipeDevice and obtain ∼2.2× gains in allreduce throughput.
This paper presents PipeDevice, a new system for low overhead intra-host container communication. PipeDevice follows a hardware-software co-design approach — it offloads data forwarding entirely onto hardware, which accesses application data in hugepages on the host, thereby eliminating CPU overhead from memory copy and TCP processing. PipeDevice preserves memory isolation and scales well to connections, making it deployable in public clouds. Isolation is achieved by allocating dedicated memory to each connection from hugepages. To achieve high scalability, PipeDevice stores the connection states entirely in host DRAM and manages them in software. Evaluation with a prototype implementation on commodity FPGA shows that for delivering 80 Gbps across containers PipeDevice saves 63.2% CPU compared to kernel TCP stack, and 40.5% over FreeFlow. PipeDevice provides salient benefits to applications. For example, we port baidu-allreduce to PipeDevice and obtain ∼2.2× gains in allreduce throughput.
| Original language | English |
|---|---|
| Title of host publication | CoNEXT ’22 - Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery |
| Pages | 126-139 |
| Number of pages | 14 |
| ISBN (Print) | 978-1-4503-9508-3 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 18th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2022) - Hybrid, Rome, Italy Duration: 6 Dec 2022 → 9 Dec 2022 https://conferences2.sigcomm.org/co-next/2022/#!/home |
Conference
| Conference | 18th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2022) |
|---|---|
| Abbreviated title | ACM CoNEXT 2022 |
| Place | Italy |
| City | Rome |
| Period | 6/12/22 → 9/12/22 |
| Internet address |
Bibliographical note
Research Unit(s) information for this publication is provided by the author(s) concerned.Funding
We thank the anonymous CoNEXT reviewers and our shepherd Gianni Antichi for their valuable comments. This work is supported in part by funding from the Research Grants Council of Hong Kong (11209520) and from CUHK (4937007, 4937008, 5501329, 5501517), and a gift fund from Microsoft (6906276).
Research Keywords
- Container Communication
- Hardware-Software Co-Design
RGC Funding Information
- RGC-funded
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- 1 Finished
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GRF: Enabling Deep Learning for Traffic Engineering in Software Defined WANs
XU, H. (Principal Investigator / Project Coordinator)
1/01/21 → 1/01/21
Project: Research
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