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Muhammad Ali Amer

dblp:85/10101 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Distributed systems › replication
data replication
0.112009
The Globus Replica Location Service: Design and Experience · IEEE Trans. Parallel Distributed Syst. 2009
Distributed systems › distributed data processing
distributed indexing
0.112009
The Globus Replica Location Service: Design and Experience · IEEE Trans. Parallel Distributed Syst. 2009
Distributed systems › replication
replica location service
0.112009
The Globus Replica Location Service: Design and Experience · IEEE Trans. Parallel Distributed Syst. 2009
Distributed systems
grid computing
0.012009
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
YearPublicationVenuePosition
2009 The Globus Replica Location Service: Design and Experience
abstract
Distributed 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