Distributed Coordination for Massive Downloading
We presents a fully distributed scheduling framework called CASTLE (Client-side Adaptive Scheduler That minimizes Load
and Energy), which jointly optimizes the spectral efficiency of cellular networks and battery consumption of smart
devices. To do so, we focus on scenarios when many smart devices compete for cellular resources in the same base
station: spreading out transmissions over time so that only a few devices transmit at once improves both spectral
efficiency and battery consumption. To this end, we devise two novel features in CASTLE. First, we explicitly consider
inter-cell interference for accurate cellular load estimation. Based on our observations, we exploit the RSRQ (Reference
Signal Received Quality) and SINR as features in a machine learning algorithm to accurately estimate the cellular load.
Second, we propose a fully distributed scheduling algorithm that coordinates transmissions between clients based on the
locally estimated load level at each client. Our formulation for minimizing battery consumption at each device leads to
an optimized backoff-based algorithm that fits practical environments. To evaluate these features, we prototype a
complete LTE system testbed consisting of mobile devices, eNodeBs, EPC (Evolved Packet Core) and application servers.
Our comprehensive experimental results show that CASTLE’s load estimation is up to 91\% accurate, and that CASTLE
achieves higher spectral efficiency with less battery consumption, compared to existing centralized scheduling
algorithms as well as a distributed CSMA-like protocol. Furthermore, we develop a light-weight SDK that can expedite the
deployment of CASTLE into smart devices and evaluate it in a commercial LTE network.
CASTLE over the Air: Distributed Scheduling for Cellular Data Transmissions
by Jihoon Lee, Jinsung Lee, Youngbin Im, Sandesh Dhawaskar Sathyanarayana, and Parisa Rahimzadeh (University of Colorado Boulder); Xiaoxi Zhang (Carnegie Mellon University); Max Hollingsworth (University of Colorado Boulder); Carlee Joe-Wong (Carnegie Mellon University); Dirk Grunwald and Sangtae Ha (University of Colorado Boulder) was accepted to MobiSys 2019.