Name
Abstract | ||
|---|---|---|
Hardware memory disaggregation is an emerging trend in datacenters that provides access to remote memory as part of a shared pool or unused memory on machines across the network. Memory disaggregation aims to improve memory utilization and scale memory-intensive applications. Current stateof-the-art prototypes have shown that hardware disaggregated memory is a reality at the rack-scale. However, the memory utilization benefits of memory disaggregation can only be fully realized at larger scales enabled by a datacenter-wide network. Introduction of a datacenter network results in new performance and reliability failures that may manifest as higher network latency. Additionally, sharing of the network introduces new points of contention between multiple applications. In this work, we characterize the impact of variable network latency and contention in an open-source hardware disaggregated memory prototype - ThymesisFlow. To support our characterization, we have developed a delay injection framework that introduces delays in remote memory access to emulate network latency. Based on the characterization results, we develop insights into how reliability and resource allocation mechanisms should evolve to support hardware memory disaggregation beyond rack-scale in datacenters. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/IPDPSW55747.2022.00210 | 2022 IEEE 36TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW 2022) |
DocType | ISSN | Citations |
Conference | 2164-7062 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Archit Patke | 1 | 0 | 1.35 |
| Haoran Qiu | 2 | 0 | 1.35 |
| Saurabh Jha | 3 | 0 | 0.34 |
| Srikumar Venugopal | 4 | 0 | 0.68 |
| Michele Gazzetti | 5 | 0 | 0.34 |
| Christian Pinto | 6 | 0 | 0.68 |
| Zbigniew Kalbarczyk | 7 | 0 | 0.34 |
| Ravishankar Iyer | 8 | 0 | 0.34 |