Demo Data from SIGIR 2026 / reviewers in the wild / expert
Alan Andrade
dblp:402/3615
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 44% Distributed systems · 44% Storage systems · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › data consistency
distributed coherence |
0.9 | 1 | 2025 | Concord: Rethinking Distributed Coherence for Software Caches in Serverless Environments · HPCA 2025 |
Cloud and datacenter computing
serverless computing |
0.9 | 1 | 2025 | Concord: Rethinking Distributed Coherence for Software Caches in Serverless Environments · HPCA 2025 |
Methods — techniques the papers use, named apart from their topics
transactional storage access · 0.9hardware cache coherence inspiration · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Concord: Rethinking Distributed Coherence for Software Caches in Serverless EnvironmentsabstractCostly accesses to global storage substantially limit the performance of serverless functions. To mitigate this overhead, data can be cached in the memory of the nodes where functions are executed. Existing caching schemes either (1) restrict a data item to be cached in a single node, causing frequent remote reads or (2) allow a data item to be cached in multiple nodes concurrently, adding substantial overhead to maintain cache coherence. Unfortunately, current approaches are suboptimal for the access patterns present in serverless workloads, which are characterized by frequent reads to small data items, strong temporal locality, and a small number of nodes that concurrently execute functions of the same application. Driven by these insights, we propose Concord, a distributed software caching system tailored to serverless environments. Concord allows multiple copies of the same data item to be cached in different nodes concurrently, allowing each cache to satisfy local reads. To maintain coherence across software caches, Concord proposes a directory-based distributed coherence protocol. The protocol is inspired by hardware cache coherence, and is enhanced to minimize coherence traffic, reduce contention points, and be robust to node failures and frequent coherence domain changes. Further, with the Concord coherence protocol, we unlock two new capabilities in serverless environments: transactional storage accesses and transparent data-aware function placement. Compared to state-of-the-art severless caching schemes, Concord running on a 16-node cluster speeds-up execution by 2.4 × and improves throughput by 1.7 ×, while using only 6.2MB of otherwise idle application memory (i.e., 4.8% of the total application memory). Jovan Stojkovic, Chloe Alverti, Alan Andrade, Nikoleta Iliakopoulou, Hubertus Franke, Tianyin Xu, Josep Torrellas |
HPCA | 3 |