Next-generation communication networks are envisioned to extensively utilize storage-enabled caching units to alleviate unfavorable surges of data traffic by pro-actively storing anticipated highly popular contents across geographically distributed storage devices during off-peak periods. This resource pre-allocation is envisioned not only to improve network efficiency, but also to increase user satisfaction. In this context, the present paper designs optimal caching schemes for distributed caching scenarios. In particular, we look at networks where a central node (base station) communicates with a number of regular nodes (users or pico base stations) equipped with local storage infrastructure. Given the spatio-temporal dynamics of content popularities, and the decentralized nature of our setup, the problem boils down to select what, when and where to cache. To address this problem, we define fetching and caching prices that vary across contents, time and space, and formulate a global optimization problem which aggregates the costs across those three domains. The resultant optimization is solved using decomposition and dynamic programming techniques, and a reduced-complexity algorithm is finally proposed. Preliminary simulations illustrating the behavior of our algorithm are finally presented.
|Original language||English (US)|
|Title of host publication||2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - May 2019|
|Event||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom|
Duration: May 12 2019 → May 17 2019
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019|
|Period||5/12/19 → 5/17/19|
Bibliographical noteFunding Information:
The work in this paper has been supported by USA NSF grants 1423316, 1508993, 1514056, 1711471, and by the Spanish MINECO grants OMI-CROM (TEC2013-41604-R) and KLINILYCS (TEC2016-75361-R).
© 2019 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.
- Dynamic pricing
- Dynamic programming
- Value iteration