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Anvitha Nandigam

dblp:225/5371 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author

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 networks
1 paper
Network optimization and economics · 30% Network performance modeling · 30% Optical networks · 30%

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

TopicWeightPapersLastEvidence papers
Optical networks › routing and wavelength assignment
blocking probability analysis
0.412019
Sharing Within Limits: Partial Resource Pooling in Loss Systems · IEEE/ACM Trans. Netw. 2019
Network performance modeling
loss systems
0.412019
Sharing Within Limits: Partial Resource Pooling in Loss Systems · IEEE/ACM Trans. Netw. 2019
Network optimization and economics
resource allocation
0.412019
Sharing Within Limits: Partial Resource Pooling in Loss Systems · IEEE/ACM Trans. Netw. 2019
Wireless networking › cognitive radio
spectrum sharing
0.112019
Sharing Within Limits: Partial Resource Pooling in Loss Systems · IEEE/ACM Trans. Netw. 2019

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

large-system approximation · 0.4erlang-b loss system · 0.4bargaining theory · 0.4
YearPublicationVenuePosition
2019 Sharing Within Limits: Partial Resource Pooling in Loss Systems
abstract
Fragmentation of expensive resources, e.g., the spectrum for wireless services, between providers can introduce inefficiencies in resource utilization and worsen overall system performance. In such cases, resource pooling between independent service providers can be used to improve performance. However, for providers to agree to pool their resources, the arrangement has to be mutually beneficial. The traditional notion of resource pooling, which implies complete sharing, need not have this property. For example, under full pooling, one of the providers may be worse off and hence has no incentive to participate. In this paper, we propose partial resource sharing models as a generalization of full pooling, which can be configured to be beneficial to all participants. We formally define and analyze two partial sharing models between two service providers, each of which is an Erlang-B loss system with the blocking probabilities as the performance measure. We show that there always exist partial sharing configurations that are beneficial to both providers, irrespective of the load and the number of circuits of each of the providers. A key result is that the Pareto frontier has at least one of the providers sharing all its resources with the other. Furthermore, full pooling may not lie inside this Pareto set. The choice of the sharing configurations within the Pareto set is formalized based on the bargaining theory. Finally, large system approximations of the blocking probabilities in the quality-efficiency-driven regime are presented.
Anvitha Nandigam, Suraj Jog, D. Manjunath, Jayakrishnan Nair 0001, Balakrishna J. Prabhu
IEEE/ACM Trans. Netw.1