Giorgos Stylianakis

dblp:380/9871 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0006-1841-7076ORCID · reported

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

Systems, architecture and hardware · 1 · 1 first-author · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 77% Distributed systems · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems
key-value storage
0.812024
Index Shipping for Efficient Replication in LSM Key-Value Stores with Hybrid KV Placement · ACM Trans. Storage 2024
Storage systems › key-value storage
LSM-tree key-value store
0.812024
Index Shipping for Efficient Replication in LSM Key-Value Stores with Hybrid KV Placement · ACM Trans. Storage 2024
Distributed systems › replication
primary-backup replication
0.812024
Index Shipping for Efficient Replication in LSM Key-Value Stores with Hybrid KV Placement · ACM Trans. Storage 2024
Storage systems › data redundancy
replicated storage
0.812024
Index Shipping for Efficient Replication in LSM Key-Value Stores with Hybrid KV Placement · ACM Trans. Storage 2024
Storage systems
indexing
0.212024
Index Shipping for Efficient Replication in LSM Key-Value Stores with Hybrid KV Placement · ACM Trans. Storage 2024

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

pointer translation · 0.8LSM-tree compaction · 0.8
YearPublicationVenuePosition
2024 Index Shipping for Efficient Replication in LSM Key-Value Stores with Hybrid KV Placement
abstract
Key-value (KV) stores based on the LSM tree have become a foundational layer in the storage stack of datacenters and cloud services. Current approaches for achieving reliability and availability favor reducing network traffic and send to replicas only new KV pairs. As a result, they perform costly compactions to reorganize data in both the primary and backup nodes, which increases device I/O traffic and CPU overhead, and eventually hurts overall system performance. In this article, we describe Tebis , an efficient LSM-based KV store that reduces I/O amplification and CPU overhead for maintaining the replica index. We use a primary-backup replication scheme that performs compactions only on the primary nodes and sends pre-built indexes to backup nodes, avoiding all compactions in backup nodes. Our approach includes an efficient mechanism to deal with pointer translation across nodes in the pre-built region index. Our results show that Tebis reduces resource utilization on backup nodes compared to performing full compactions: throughput is increased by 1.06 to 2.90×, CPU efficiency is increased by 1.21 to 2.78×, and I/O amplification is reduced by 1.7 to 3.27×, whereas network traffic increases by up to 1.32 to 3.76x.
Giorgos Stylianakis, Giorgos Saloustros, Orestis Chiotakis, Giorgos Xanthakis, Angelos Bilas
ACM Trans. Storage1