Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Xiating Xie

dblp:244/9240 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Graph data management › graph algorithms
graph traversal
1.122024
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
0.812024
Locality-Preserving Graph Traversal With Split Live Migration · IEEE Trans. Parallel Distributed Syst. 2024
Storage systems
key-value storage
0.812024
Locality-Preserving Graph Traversal With Split Live Migration · IEEE Trans. Parallel Distributed Syst. 2024
Memory systems
non-volatile memory
0.512021
Characterizing and Optimizing Remote Persistent Memory with RDMA and NVM · USENIX ATC 2021
Interconnection networks and networks-on-chip
remote direct memory access
0.512021
Characterizing and Optimizing Remote Persistent Memory with RDMA and NVM · USENIX ATC 2021
Cloud and datacenter computing › virtualization › virtual machine migration
live migration
0.322024
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
0.212024
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
YearPublicationVenuePosition
2024 Locality-Preserving Graph Traversal With Split Live Migration
abstract
Graph 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 ATC2
2019 Pragh: Locality-preserving Graph Traversal with Split Live Migration
Xiating Xie, Xingda Wei, Rong Chen 0001, Haibo Chen 0001
USENIX ATC1