INDIGO: Page Migration for Hardware Memory Disaggregation Across a Network
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917966611742720 |
|---|---|
| author | Patke, Archit Pinto, Christian Jha, Saurabh Qiu, Haoran Kalbarczyk, Zbigniew Iyer, Ravishankar |
| author_facet | Patke, Archit Pinto, Christian Jha, Saurabh Qiu, Haoran Kalbarczyk, Zbigniew Iyer, Ravishankar |
| contents | Hardware memory disaggregation (HMD) is an emerging technology that enables access to remote memory, thereby creating expansive memory pools and reducing memory underutilization in datacenters. However, a significant challenge arises when accessing remote memory over a network: increased contention that can lead to severe application performance degradation. To reduce the performance penalty of using remote memory, the operating system uses page migration to promote frequently accessed pages closer to the processor. However, previously proposed page migration mechanisms do not achieve the best performance in HMD systems because of obliviousness to variable page transfer costs that occur due to network contention. To address these limitations, we present INDIGO: a network-aware page migration framework that uses novel page telemetry and a learning-based approach for network adaptation. We implemented INDIGO in the Linux kernel and evaluated it with common cloud and HPC applications on a real disaggregated memory system prototype. Our evaluation shows that INDIGO offers up to 50-70% improvement in application performance compared to other state-of-the-art page migration policies and reduces network traffic up to 2x. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18140 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | INDIGO: Page Migration for Hardware Memory Disaggregation Across a Network Patke, Archit Pinto, Christian Jha, Saurabh Qiu, Haoran Kalbarczyk, Zbigniew Iyer, Ravishankar Distributed, Parallel, and Cluster Computing Hardware memory disaggregation (HMD) is an emerging technology that enables access to remote memory, thereby creating expansive memory pools and reducing memory underutilization in datacenters. However, a significant challenge arises when accessing remote memory over a network: increased contention that can lead to severe application performance degradation. To reduce the performance penalty of using remote memory, the operating system uses page migration to promote frequently accessed pages closer to the processor. However, previously proposed page migration mechanisms do not achieve the best performance in HMD systems because of obliviousness to variable page transfer costs that occur due to network contention. To address these limitations, we present INDIGO: a network-aware page migration framework that uses novel page telemetry and a learning-based approach for network adaptation. We implemented INDIGO in the Linux kernel and evaluated it with common cloud and HPC applications on a real disaggregated memory system prototype. Our evaluation shows that INDIGO offers up to 50-70% improvement in application performance compared to other state-of-the-art page migration policies and reduces network traffic up to 2x. |
| title | INDIGO: Page Migration for Hardware Memory Disaggregation Across a Network |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.18140 |