TY - GEN
T1 - Multi-layer Decomposition of Optimal Resource Sharing Problems
AU - Karakoc, Nurullah
AU - Scaglione, Anna
AU - Nedich, Angelia
N1 - Funding Information:
The work has been partially supported by the NSF grant CCF-1717391, the ONR grant no. N00014-12-1-0998 and the NSF grant NeTS: 1716121.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - We describe a distributed framework for resource sharing problems that we face in communications, microeconomics and various networking applications. In particular, we consider a hierarchical multi-layer decomposition for network utility maximization (NUM), where functionalities are assigned to different layers. The proposed methodology creates solutions having central management and distributed computations. The technique aims to respond to the dynamics of the network by decreasing the communication cost, while shifting more computational load to the edges of the network. The main contribution of this work is the provision of a detailed analysis under the assumption that the network changes are in the same time-scale with the convergence time of the algorithms used for local computations. For this scenario, assuming strong concavity and smoothness of the users' objective functions, we present convergence rates for each layer.
AB - We describe a distributed framework for resource sharing problems that we face in communications, microeconomics and various networking applications. In particular, we consider a hierarchical multi-layer decomposition for network utility maximization (NUM), where functionalities are assigned to different layers. The proposed methodology creates solutions having central management and distributed computations. The technique aims to respond to the dynamics of the network by decreasing the communication cost, while shifting more computational load to the edges of the network. The main contribution of this work is the provision of a detailed analysis under the assumption that the network changes are in the same time-scale with the convergence time of the algorithms used for local computations. For this scenario, assuming strong concavity and smoothness of the users' objective functions, we present convergence rates for each layer.
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U2 - 10.1109/CDC.2018.8619777
DO - 10.1109/CDC.2018.8619777
M3 - Conference contribution
AN - SCOPUS:85062169184
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 178
EP - 183
BT - 2018 IEEE Conference on Decision and Control, CDC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 57th IEEE Conference on Decision and Control, CDC 2018
Y2 - 17 December 2018 through 19 December 2018
ER -