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Srinath Rao

dblp:28/28 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2002
—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
High-performance computing · 33% Parallel and multicore computing · 33% Cloud and datacenter computing · 33%
Computer networks
1 paper
Internet architecture and protocols · 100%

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

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing › datacenter services › online service systems › internet services
cluster-based web server
0.012002
User-Level Communication in Cluster-Based Servers · HPCA 2002
High-performance computing › cluster computing
server cluster
0.012002
User-Level Communication in Cluster-Based Servers · HPCA 2002
Parallel and multicore computing › parallel computing › parallel communication
user-level communication
0.012002
User-Level Communication in Cluster-Based Servers · HPCA 2002
Internet architecture and protocols › world wide web
web server
0.012002
User-Level Communication in Cluster-Based Servers · HPCA 2002

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

trace-driven evaluation · 0.1analytical modeling · 0.1
YearPublicationVenuePosition
2002 User-Level Communication in Cluster-Based Servers
abstract
Clusters of commodity computers are currently being used to provide the scalability required by several popular Internet services. In this paper we evaluate an efficient cluster-based WWW server, as a function of the characteristics of the intra-cluster communication architecture. More specifically, we evaluate the impact of processor overhead, network bandwidth, remote memory writes, and zero-copy data transfers on the performance of our server. Our experimental results with an 8-node cluster and four real WWW traces show that network bandwidth affects the performance of our server by only 6%. In contrast, user-level communication can improve performance by as much as 29%. Low processor overhead, remote memory writes, and zero-copy all make small contributions towards this overall gain. To be able to extrapolate from our experimental results, we use an analytical model to assess the performance of our server under different workload characteristics, different numbers of cluster nodes, and higher performance systems. Our modeling results show that higher gains (of up to 55%) can be accrued for workloads with large working sets and next-generation servers running on large clusters.
Enrique V. Carrera, Srinath Rao, Liviu Iftode, Ricardo Bianchini
HPCA2