Name
Abstract | ||
|---|---|---|
Recent proposals for the disaggregation of compute, memory, storage, and accelerators in data centers promise substantial operational benefits. Unfortunately, for resources like memory, this comes at the cost of performance overhead due to the potential insertion of network latency into every load and store operation. This effect is particularly felt by data-intensive systems due to the size of their working sets, the frequency at which they need to access memory, and the relatively low computation per access. This performance impairment offsets the elasticity benefit of disaggregated memory. This paper presents TELEPORT, a compute pushdown framework for data-intensive systems that run on disaggregated architectures; compared to prior work on compute pushdown, TELEPORT is unique in its efficiency and flexibility. We have developed optimization principles for several popular systems including a columnar in-memory DBMS, a graph processing system, and a MapReduce system. The evaluation results show that using TELEPORT to push down simple operators improves the performance of these systems on state-of-the-art disaggregated OSes by an order of magnitude, thus fully exploiting the elasticity of disaggregated data centers. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3514221.3517856 | PROCEEDINGS OF THE 2022 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA (SIGMOD '22) |
Keywords | DocType | ISSN |
Memory disaggregation, Data processing, Compute pushdown | Conference | 0730-8078 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingpu Zhang | 1 | 79 | 10.17 |
| Xu Chen | 2 | 1590 | 112.25 |
| S Sankhe | 3 | 0 | 0.34 |
| Zibin Zheng | 4 | 3731 | 199.37 |
| K Zhong | 5 | 0 | 0.34 |
| Sebastian Angel | 6 | 31 | 8.25 |
| Anthony Chen | 7 | 209 | 18.25 |
| Vincent Liu | 8 | 186 | 12.40 |