Optimal Energy Consumption with Communication, Computation, Caching and QoI-Guarantee

TL;DR

This paper addresses minimizing energy consumption in data networks by jointly optimizing communication, computation, caching, and QoI guarantees, introducing a novel algorithm for near-optimal solutions.

Contribution

It formulates a complex MINLP problem for energy-efficient data management with QoI guarantees and proposes a V-SBB algorithm for global optimization.

Findings

Energy efficiency improved by up to 88% with the proposed framework.

V-SBB outperforms other MINLP solvers in solution quality.

Joint optimization significantly reduces overall energy consumption.

Abstract

Energy efficiency is a fundamental requirement of modern data communication systems, and its importance is reflected in much recent work on performance analysis of system energy consumption. However, most works have only focused on communication and computation costs, but do not account for caching costs. Given the increasing interest in cache networks, this is a serious deficiency. {In this paper, we consider the problem of energy consumption in data communication, compression and caching (C$3$) with a Quality of Information (QoI) guarantee in a communication network. {Our goal is to identify the optimal data compression rate and data placement over the network to minimize the overall energy consumption in the network.} he formulated problem is a \emph{Mixed Integer Non-Linear Programming} (MINLP) problem with non-convex functions, which is NP-hard in general. } {We} propose a variant of spatial branch and bound algorithm (V-SBB), that can {provide} the $\epsilon$-global optimal solution to {our problem}. {We numerically show that our C3 optimization framework can improve the energy efficiency up to 88\% compared to any C2 optimization between communication and computation or caching. Furthermore, for our energy consumption problem, V-SBB {provides comparatively better solution than some other MINLP solvers.}}