Demo Data from EDBT 2026 / reviewers in the wild / expert
Xiating Xie
dblp:244/9240
· DBLP profile ↗
3ranked-venue papers
1as first author
2since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
3 papers |
Storage systems · 56% Memory systems · 16% Interconnection networks and networks-on-chip · 16% | |
| Databases, data mining, and information retrieval
2 papers |
Graph data management · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Graph data management › graph algorithms
graph traversal |
1.1 | 2 | 2024 | Locality-Preserving Graph Traversal With Split Live Migration · IEEE Trans. Parallel Distributed Syst. 2024 Pragh: Locality-preserving Graph Traversal with Split Live Migration · USENIX ATC 2019 |
Storage systems › key-value storage
graph store |
0.8 | 1 | 2024 | Locality-Preserving Graph Traversal With Split Live Migration · IEEE Trans. Parallel Distributed Syst. 2024 |
Storage systems
key-value storage |
0.8 | 1 | 2024 | Locality-Preserving Graph Traversal With Split Live Migration · IEEE Trans. Parallel Distributed Syst. 2024 |
Memory systems
non-volatile memory |
0.5 | 1 | 2021 | Characterizing and Optimizing Remote Persistent Memory with RDMA and NVM · USENIX ATC 2021 |
Interconnection networks and networks-on-chip
remote direct memory access |
0.5 | 1 | 2021 | Characterizing and Optimizing Remote Persistent Memory with RDMA and NVM · USENIX ATC 2021 |
Cloud and datacenter computing › virtualization › virtual machine migration
live migration |
0.3 | 2 | 2024 | Locality-Preserving Graph Traversal With Split Live Migration · IEEE Trans. Parallel Distributed Syst. 2024 Pragh: Locality-preserving Graph Traversal with Split Live Migration · USENIX ATC 2019 |
Storage systems
data migration |
0.2 | 1 | 2024 | Locality-Preserving Graph Traversal With Split Live Migration · IEEE Trans. Parallel Distributed Syst. 2024 |
Methods — techniques the papers use, named apart from their topics
split live migration · 2.3RDMA · 2.0location cache · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Locality-Preserving Graph Traversal With Split Live MigrationabstractGraph models many real-world data like social, transportation, biology, and communication data. Hence, graph traversal including multi-hop or graph-walking queries has been the key operation atop graph stores. However, since different graph traversals may touch different sets of vertices, it is hard or even impossible to have a one-size-fits-all graph partitioning algorithm that preserves access locality for various graph traversal workloads. Meanwhile, prior shard-based migration faces a dilemma such that coarse-grained migration may incur more migration overhead over increased locality benefits, while fine-grained migration usually requires excessive metadata and incurs non-trivial maintenance costs. We present Pragh, an efficient locality-preserving live graph migration scheme for graph stores in the form of key-value pairs. The key idea of Pragh is a split migration model that only migrates values physically while retaining keys in the initial location. This allows fine-grained migration while avoiding the need to maintain excessive metadata. Pragh integrates an RDMA-friendly location cache from DrTM-KV to provide fully-localized access to migrated data and further makes a novel reuse of the cache replacement policy for lightweight monitoring. Pragh further supports evolving graphs through a check-and-forward mechanism to resolve the conflict between updates and migration of graph data. Evaluations on an 8-node RDMA-capable cluster (100 Gbps) using a representative graph traversal benchmark show that Pragh can increase the throughput by up to 19× and decrease the median latency by up to 94%, thanks to split live migration that eliminates 97% remote accesses. A port of split live migration to Wukong shows up to 2.53× throughput improvement on representative workloads like LUBM-10240, thanks to a reduction of 88% remote accesses. This further confirms the effectiveness and generality of Pragh. Finally, though Pragh focuses on RDMA-based graph traversal, we show its generality by extending it to support graph traversals under traditional networking. Evaluations on the graph traversal benchmarks and graph query workloads on the same cluster but with 10 Gbps TCP/IP network further confirm its effectiveness without RDMA. Specifically, when evaluating on the LUBM-10240, Wukong-TCP with Pragh can achieve up to 1.87× throughput improvement with a 56% decrease in remote accesses. Rong Chen 0001, Xingda Wei, Xiating Xie, Haibo Chen 0001 |
IEEE Trans. Parallel Distributed Syst. | 3 |
| 2021 | Characterizing and Optimizing Remote Persistent Memory with RDMA and NVM
Xingda Wei, Xiating Xie, Rong Chen 0001, Haibo Chen 0001, Binyu Zang |
USENIX ATC | 2 |
| 2019 | Pragh: Locality-preserving Graph Traversal with Split Live Migration
Xiating Xie, Xingda Wei, Rong Chen 0001, Haibo Chen 0001 |
USENIX ATC | 1 |