Optimal Energy Consumption for Communication, Computation, Caching, and Quality Guarantee

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 work has only focused on communication and computation costs without accounting for data 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 (C3) with a quality-of-information (QoI) guarantee in a communication network. Our goal is to identify the optimal data compression rates and cache placement over the network that minimizes the overall energy consumption in the network. We formulate the problem as a Mixed Integer Non-Linear Programming (MINLP) problem with non-convex functions, which is NP-hard in general. We propose a variant of the spatial branch and bound algorithm (V-SBB) that can provide an -global optimal solution to the problem. By extensive numerical experiments, we show that the C3 optimization framework improves the energy efficiency by up to 88% compared to any optimization that only considers either communication and caching or communication and computation. Furthermore, the V-SBB technique provides comparatively better solution than some other MINLP solvers at the cost of added computation time.

Connection Quality Prediction and Nonlinear Control of Power Grid Communication Wireless Network

“…is method uses a differential autoregressive mobile model (ARIMA) to effectively process autocorrelation data sequence and significantly improves the anti-interference of the communication link. However, due to a large amount of calculation of its model, it cannot meet the application of the smart grid in practical communication [4]. Ren is method has correct accuracy, but it is easy to cause early data loss in multiple data processing, resulting in low accuracy of long sequences [10].…”

A Review on Green Caching Strategies for Next Generation Communication Networks

“…In this model, the voluntary devices gathered signals from nearby devices and transmitted the aggregated signal to the BSs. In a sensor network, Zafari et al [175] optimally compressed data and allocated content. The study reduced energy consumption without degrading quality-of-information.…”

“…network [176] Solved optimization problem using simulated annealing algorithm [176] and spatial brunch-and-bound method [175].…”

“…Execution time is usually investigated for task caching [144], [175], [197]. A joint strategy incorporating caching and MEC not only caches data but also executes offloaded tasks at the edge nodes.…”

“…Mobility issues and the ever-increasing number of smart vehicles worsen the situation. To overcome Energy consumption [13], [26], [47], [49]- [51], [118]- [120], [126], [127], [130], [132], [134], [136], [143]- [145], [147], [149]- [158], [160], [161], [173], [175], [176], [180], [181], [189], [197]- [200], [207], [211], [218].…”