Name
Abstract | ||
|---|---|---|
Application offloads on modern high-speed programmable switches have been proposed in a variety of systems (e.g., key-value store systems and network middleboxes) so as to efficiently scale up the traditional server-oriented deployments. However, they largely achieve sub-optimal offloading efficiency due to the lack of (1) capability to perform control actions at sufficient rates, and (2) adaptability to workload changes. In this paper, we scrutinize the common stumbling blocks of existing frameworks with performance evaluations on real workloads. We present DySO (Dynamic State Offloading), a framework which enables expeditious on-demand control actions and self-tuning of management rules. Our software simulations show up to 100% performance improvement compared to existing systems for various real world traces. On top of that, we implement and evaluate DySO on a commodity programmable switch, showing two orders of magnitude faster responsiveness to sudden workload changes compared to the existing systems. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.23919/IFIPNetworking55013.2022.9829799 | 2022 IFIP Networking Conference (IFIP Networking) |
Keywords | DocType | ISBN |
Programmable networks,P4,Framework,Application acceleration,In-network caching | Conference | 978-1-6654-8726-9 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cha Hwan Song | 1 | 2 | 1.04 |
| Xin Zhe Khooi | 2 | 3 | 2.43 |
| Dinil Mon Divakaran | 3 | 7 | 2.83 |
| Mun Choon Chan | 4 | 1130 | 97.54 |