Augmenting max-weight with explicit learning for wireless scheduling with switching costs

Abstract

In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to switch off dynamically a subset of BSs to minimize energy costs. We consider two types of energy cost: (i) the cost of maintaining a BS in the active state, and (ii) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach -a Max-Weight algorithm along with a Lyapunov-based stability argument -does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight based channel scheduler that has fast temporal dynamics. We show using convergence of timeinhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability.

Timely-Throughput Optimal Coded Computing over Cloud Networks

“…Another difference is in the proof techniques to show the optimality of the proposed algorithms. The Lyapunov arguments for the adaptive scheme used in [14] is quite different from our approach.…”

“…At a conceptual level, this paper has some interesting comparisons/connections with [14]. Under wireless networks, [14] investigates how to turn base stations on or off, in order to adapt to the unknown load arrival and channel statistics. Under cloud computing networks, our paper focuses on how to do the computation load assignment in order to adapt to unknown computing networks.…”

“…Under cloud computing networks, our paper focuses on how to do the computation load assignment in order to adapt to unknown computing networks. So, at a high-level, the corresponding scheduling problems can be seen as dual of each other: [14] assigns base stations to good (on) or bad (off) states in order to meet the demands, while our goal is to assign the computation loads in order to optimally exploit the (unknown) state of the workers. However, we also point out that the setting and objective of the two papers are quite different.…”

“…However, we also point out that the setting and objective of the two papers are quite different. We consider cloud computing platforms and focus on the timely computation throughput, which is very different from [14]. Another difference is in the proof techniques to show the optimality of the proposed algorithms.…”

Dynamic Cloud Network Control Under Reconfiguration Delay and Cost

“…Regarding reconfiguration cost, [13] addressed the cost of flow reconfigurations in SDN by designing a control policy that minimizes total flow allocation cost subject to a given reconfiguration cost budget. In [14], the reconfiguration cost associated with switching base stations on and off in a dynamic wireless network setting was considered. The proposed approach requires arrival and channel statistics for activation decisions, and leverages an explore-exploit policy in the case that this information is not available.…”