Demo Data from SIGIR 2026 / reviewers in the wild / expert
T. Sivabalan
dblp:12/9104
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—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 |
Energy-efficient computing · 70% Performance modeling and evaluation · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Energy-efficient computing
datacenter power management |
0.1 | 1 | 2010 | Worth their watts? - an empirical study of datacenter servers · HPCA 2010 |
Energy-efficient computing › datacenter power management
server-level power monitoring |
0.1 | 1 | 2010 | Worth their watts? - an empirical study of datacenter servers · HPCA 2010 |
Performance modeling and evaluation
workload characterization |
0.1 | 1 | 2010 | Worth their watts? - an empirical study of datacenter servers · HPCA 2010 |
Energy-efficient computing
power management |
0.0 | 1 | 2010 | Worth their watts? - an empirical study of datacenter servers · HPCA 2010 |
Methods — techniques the papers use, named apart from their topics
empirical measurement · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Worth their watts? - an empirical study of datacenter serversabstractThe management of power consumption in datacenters has become an important problem. This needs a systematic evaluation of the as-is scenario to identify potential areas for improvement and quantify the impact of any strategy. We present a measurement study of a production datacenter from a joint perspective of power and performance at the individual server level. Our observations help correlate power consumption of production servers with their activity, and identify easily implementable improvements. We find that production servers are underutilized from an activity perspective; are overrated from a power perspective; execute temporally similar workloads over a granularity of weeks; do not idle efficiently; and have power consumptions that are well tracked by their CPU utilizations. Our measurements suggest the following steps for improvement: staggering periodic activities on servers; enabling deeper sleep states; and provisioning based on measurement. Arunchandar Vasan 0001, Anand Sivasubramaniam, Vikrant Shimpi, T. Sivabalan, Rajesh Subbiah |
HPCA | 4 |