EDBT 2026 Demo / reviewers in the wild / expert
Aadharsh Kannan
dblp:198/3348
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2Databases, data management, data science and information retrieval · 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 |
Cloud and datacenter computing · 100% | |
| Computer networks
1 paper |
Network optimization and economics · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing
cloud economics |
0.3 | 1 | 2017 | Usage Patterns and the Economics of the Public Cloud · WWW 2017 |
Cloud and datacenter computing › utility computing
cloud pricing |
0.3 | 1 | 2017 | Usage Patterns and the Economics of the Public Cloud · WWW 2017 |
Cloud and datacenter computing › resource management
datacenter resource management |
0.3 | 1 | 2017 | Usage Patterns and the Economics of the Public Cloud · WWW 2017 |
Cloud and datacenter computing › utility computing › cloud pricing
dynamic pricing |
0.3 | 1 | 2017 | Usage Patterns and the Economics of the Public Cloud · WWW 2017 |
Network optimization and economics
resource allocation |
0.1 | 1 | 2017 | Usage Patterns and the Economics of the Public Cloud · WWW 2017 |
Methods — techniques the papers use, named apart from their topics
utilization analysis · 0.6economic modeling · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Scale Effects in Web Search
Di He 0001, Aadharsh Kannan, Tie-Yan Liu, R. Preston McAfee, Tao Qin 0001, Justin M. Rao |
WINE | 2 |
| 2017 | Usage Patterns and the Economics of the Public CloudabstractWe examine the economics of demand and supply in cloud computing. The public cloud offers three main benefits to firms: 1) utilization can be scaled up or down easily; 2) capital expenditure (on-premises servers) can be converted to operating expenses, with the capital incurred by a specialist; 3) software can be ``pay-as-you-go.'' These benefits increase with the firm's ability to dynamically scale resource utilization and thus point to the need for dynamic prices to shape demand to the (short-run) fixed datacenter supply. Detailed utilization analysis reveals the large swings in utilization at the hourly, daily or weekly level are very rare at the customer level and non-existent at the datacenter level. Furthermore, few customers show volatility patterns that are excessively correlated with the market. These results explain why fixed prices currently prevail despite the seeming need for time-varying dynamics. Examining the actual CPU utilization provides a lens into the future. Here utilization varies by order half the datacenter capacity, but most firms are not dynamically scaling their assigned resources at-present to take advantage of these changes. If these gains are realized, demand fluctuations would be on par with the three classic industries where dynamic pricing is important (hotels, electricity, airlines) and dynamic prices would be essential for efficiency. Cinar Kilcioglu, Justin M. Rao, Aadharsh Kannan, R. Preston McAfee |
WWW | 3 |