| Title | Decentralized placement of data and analytics in wireless networks for energy-efficient execution |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Basu, Prithwish, Salonidis, Theodoros, Kraczek, Brent, Saghaian, Sayed M., Sydney, Ali, Ko, Bongjun, La Porta, Tom, Chan, Kevin |
| Conference Name | IEEE INFOCOM 2020 - IEEE Conference on Computer Communications |
| Date Published | July 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-6412-0 |
| Keywords | Analytical models, composability, Computational modeling, Computing Theory, Data models, Distributed databases, Optimization, pubcrawl, Task Analysis, wireless networks |
| Abstract | We address energy-efficient placement of data and analytics components of composite analytics services on a wireless network to minimize execution-time energy consumption (computation and communication) subject to compute, storage and network resource constraints. We introduce an expressive analytics service hypergraph model for representing k-ary composability relationships (k ≥ 2) between various analytics and data components and leverage binary quadratic programming (BQP) to minimize the total energy consumption of a given placement of the analytics hypergraph nodes on the network subject to resource availability constraints. Then, after defining a potential energy functional Φ(·) to model the affinities of analytics components and network resources using analogs of attractive and repulsive forces in physics, we propose a decentralized Metropolis Monte Carlo (MMC) sampling method which seeks to minimize Φ by moving analytics and data on the network. Although Φ is non-convex, using a potential game formulation, we identify conditions under which the algorithm provably converges to a local minimum energy equilibrium placement configuration. Trace-based simulations of the placement of a deep-neural-network analytics service on a realistic wireless network show that for smaller problem instances our MMC algorithm yields placements with total energy within a small factor of BQP and more balanced workload distributions; for larger problems, it yields low-energy configurations while the BQP approach fails. |
| URL | https://ieeexplore.ieee.org/document/9155222 |
| DOI | 10.1109/INFOCOM41043.2020.9155222 |
| Citation Key | basu_decentralized_2020 |
Decentralized placement of data and analytics in wireless networks for energy-efficient execution