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首页> 外文期刊>Control of Network Systems, IEEE Transactions on >Optimal Energy Consumption for Communication, Computation, Caching, and Quality Guarantee
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Optimal Energy Consumption for Communication, Computation, Caching, and Quality Guarantee

机译:通信,计算,缓存和质量保证的最佳能耗

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摘要

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, computation 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 nonlinear programming (MINLP) problem with nonconvex functions, which is non-deterministic polynomial-time hard (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 solutions than some other MINLP solvers at the cost of additional computation time.
机译:能源效率是现代数据通信系统的基本要求,其重要性在最近的近期对系统能源消耗的绩效分析工作中反映出来。但是,大多数工作仅侧重于通信和计算成本,而无需核算数据缓存成本。鉴于对缓存网络的兴趣越来越大,这是一个严重的缺陷。在本文中,我们考虑了在通信网络中的信息质量(Qoi)保证的数据通信,计算和高速缓存(C3)中的能耗问题。我们的目标是通过网络识别最佳数据压缩速率和高速缓存放置,从而最大限度地减少网络中的整体能量消耗。我们将问题与非凸函数的混合整数非线性编程(MINLP)问题配制作,这是一般的非确定性多项式 - 时间硬(NP-Hard)。我们提出了一种空间分支和绑定算法(V-SBB)的变体,可以为问题提供全局最佳解决方案。通过广泛的数值实验,我们表明,与只考虑通信和缓存或通信和计算的任何优化相比,C3优化框架将能量效率提高至88%。此外,V-SBB技术在额外计算时间的成本提供比其他一些MINLP求解器的相对较好的解决方案。

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