Demo Data from EDBT 2026 / reviewers in the wild / expert
Muhammad Ali Amer
dblp:85/10101
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1
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 |
Distributed systems · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › replication
data replication |
0.1 | 1 | 2009 | The Globus Replica Location Service: Design and Experience · IEEE Trans. Parallel Distributed Syst. 2009 |
Distributed systems › distributed data processing
distributed indexing |
0.1 | 1 | 2009 | The Globus Replica Location Service: Design and Experience · IEEE Trans. Parallel Distributed Syst. 2009 |
Distributed systems › replication
replica location service |
0.1 | 1 | 2009 | The Globus Replica Location Service: Design and Experience · IEEE Trans. Parallel Distributed Syst. 2009 |
Distributed systems
grid computing |
0.0 | 1 | 2009 | The Globus Replica Location Service: Design and Experience · IEEE Trans. Parallel Distributed Syst. 2009 |
Methods — techniques the papers use, named apart from their topics
soft-state protocol · 0.1bloom filter · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | The Globus Replica Location Service: Design and ExperienceabstractDistributed computing systems employ replication to improve overall system robustness, scalability, and performance. A replica location service (RLS) offers a mechanism to maintain and provide information about physical locations of replicas. This paper defines a design framework for RLSs that supports a variety of deployment options. We describe the RLS implementation that is distributed with the Globus toolkit and is in production use in several grid deployments. Features of our modular implementation include the use of soft-state protocols to populate a distributed index and Bloom filter compression to reduce overheads for distribution of index information. Our performance evaluation demonstrates that the RLS implementation scales well for individual servers with millions of entries and up to 100 clients. We describe the characteristics of existing RLS deployments and discuss how RLS has been integrated with higher-level data management services. Ann L. Chervenak, Robert Schuler, Matei Ripeanu, Muhammad Ali Amer, Shishir Bharathi, Ian T. Foster, Adriana Iamnitchi, Carl Kesselman |
IEEE Trans. Parallel Distributed Syst. | 4 |