Demo Data from VLDB 2026 / reviewers in the wild / expert
Venkata Nagarjuna Dondapati
dblp:235/6127
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 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 · 50% Storage systems · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing › cloud service management
cloud service selection |
0.4 | 1 | 2019 | A Cloud Brokerage Architecture for Efficient Cloud Service Selection · IEEE Trans. Serv. Comput. 2019 |
Storage systems › indexing
indexing scheme |
0.4 | 1 | 2019 | A Cloud Brokerage Architecture for Efficient Cloud Service Selection · IEEE Trans. Serv. Comput. 2019 |
Methods — techniques the papers use, named apart from their topics
service selection algorithm · 0.4b$^{cloud}$-tree · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | A Cloud Brokerage Architecture for Efficient Cloud Service SelectionabstractThe expanding cloud computing services offer great opportunities for consumers to find the best service and best pricing. Meanwhile, it also raises new challenges for consumers who need to select the best service out of such a huge pool since it will be time-consuming for consumers to collect the necessary information and analyze all service providers to make the decision. Therefore, in this paper, we propose a novel brokerage-based architecture in the cloud, where the cloud brokers is responsible for the service selection. We also design an efficient indexing structure, called B$^{cloud}$-tree, for managing the information of a large number of cloud service providers. We then develop the service selection algorithm that recommends most suitable cloud services to the cloud consumers. We carry out extensive experimental studies on real and synthetic cloud data, and demonstrate a significant performance improvement over previous approaches. Dan Lin 0001, Anna Cinzia Squicciarini, Venkata Nagarjuna Dondapati, Smitha Sundareswaran |
IEEE Trans. Serv. Comput. | 3 |