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Aadharsh Kannan

dblp:198/3348 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
cloud economics
0.312017
Usage Patterns and the Economics of the Public Cloud · WWW 2017
Cloud and datacenter computing › utility computing
cloud pricing
0.312017
Usage Patterns and the Economics of the Public Cloud · WWW 2017
Cloud and datacenter computing › resource management
datacenter resource management
0.312017
Usage Patterns and the Economics of the Public Cloud · WWW 2017
Cloud and datacenter computing › utility computing › cloud pricing
dynamic pricing
0.312017
Usage Patterns and the Economics of the Public Cloud · WWW 2017
Network optimization and economics
resource allocation
0.112017
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
YearPublicationVenuePosition
2017 Scale Effects in Web Search
Di He 0001, Aadharsh Kannan, Tie-Yan Liu, R. Preston McAfee, Tao Qin 0001, Justin M. Rao
WINE2
2017 Usage Patterns and the Economics of the Public Cloud
abstract
We 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
WWW3