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Johann K. Miller

dblp:350/5935 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-5290-7796ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 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.

Databases, data mining, and information retrieval
1 paper
Transaction processing and concurrency control · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Transaction processing and concurrency control
concurrency control
0.712023
Detock: High Performance Multi-region Transactions at Scale · Proc. ACM Manag. Data 2023
Transaction processing and concurrency control › deadlock
deadlock resolution
0.712023
Detock: High Performance Multi-region Transactions at Scale · Proc. ACM Manag. Data 2023
Distributed systems › replication
geo-replication
0.712023
Detock: High Performance Multi-region Transactions at Scale · Proc. ACM Manag. Data 2023
Distributed systems › distributed database
multi-site transactions
0.712023
Detock: High Performance Multi-region Transactions at Scale · Proc. ACM Manag. Data 2023

Methods — techniques the papers use, named apart from their topics

pessimistic concurrency control · 1.3optimistic concurrency control · 1.3
YearPublicationVenuePosition
2023 Detock: High Performance Multi-region Transactions at Scale
abstract
Many globally distributed data stores need to replicate data across large geographic distances. Since synchronously replicating data across such distances is slow, those systems with high consistency requirements often geo-partition data and direct all linearizable requests to the primary region of the accessed data. This significantly improves performance for workloads where most transactions access data close to where they originate from. However, supporting serializable multi-geo-partition transactions is a challenge, and they often degrade the performance of the whole system. This becomes even more challenging when they conflict with single-partition requests, where optimistic protocols lead to high numbers of aborts, and pessimistic protocols lead to high numbers of distributed deadlocks. In this paper, we describe the design of concurrency control and deadlock resolution protocols, built within a practical, complete implementation of a geographically replicated database system called Detock, that enables processing strictly-serializable multi-region transactions with near-zero performance degradation at extremely high conflict and order of magnitude higher throughput relative to state-of-the art geo-replication approaches, while improving latency by up to a factor of 5.
Cuong D. T. Nguyen, Johann K. Miller, Daniel J. Abadi
Proc. ACM Manag. Data2