RT Journal Article
T1 Energy-optimal collaborative file distribution in wired networks
A1 Verma, Kshitiz
A1 Rizzo, Gianluca
A1 Fernández Anta, Antonio
A1 Cuevas Rumín, Rubén
A1 Azcorra Saloña, Arturo
A1 Zaks, Shmuel
A1 García Martínez, Alberto
AB The impact of the ICT sector in worldwide power consumption is an increasing concern, motivating the research community to devote an important effort to define novel energy efficient networking solutions. Despite file distribution is responsible for a major portion of the current Internet traffic, little effort has been dedicated to address the issue of its energy efficiency so far. Most of the previous literature focuses on optimizing the download time of file distribution schemes (e.g. centralized server-based or distributed peer-to-peer solutions) while it is yet unclear how to optimize file distribution schemes from the point of view of energy consumed. In this paper, we present a general modelling framework to analyze the energy consumption of file distribution systems. First, we show that the general problem of minimizing energy consumption in file distribution is NP-hard. Then, for restricted versions of the problem, we establish theoretical bounds to minimal energy consumption. Furthermore, we define a set of optimal algorithms for a variety of system settings, which exploit the service capabilities of hosts in a P2P fashion. We show that our schemes are capable of reducing at least 50 % of the energy consumed by traditional (yet largely used) centralized distribution schemes even when considering effects such as network congestion and heterogeneous access speed across nodes.
PB Springer
SN 1936-6442
YR 2017
FD 2017-07-01
LK https://hdl.handle.net/10016/27519
UL https://hdl.handle.net/10016/27519
LA eng
NO Supported in part by Ministerio de Economia y Competitividad grant TEC2014- 55713-R, the DRONEXT project (TEC2014-58964-C2-1-R), Regional Government of Madrid (CM) grant Cloud4BigData (S2013/ICE-2894, co- funded by FSE & FEDER), and BRADE Project (P2013/ICE-2958), NSF of China grant 61520106005, and European Commission H2020 grants ReCred and NOTRE.
DS e-Archivo
RD 27 jul. 2024