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 $\epsilon$ -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.

中文翻译：

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用于通信，计算，缓存和质量保证的最佳能耗

能源效率是现代数据通信系统的基本要求，其重要性在最近有关系统能耗性能分析的工作中得到了体现。但是，大多数工作只集中在通信和计算成本上，而没有考虑数据缓存成本。鉴于对缓存网络的兴趣日益增加，这是一个严重的缺陷。在本文中，我们考虑了通信网络中具有信息质量（QoI）保证的数据通信，计算和缓存（C3）中的能耗问题。我们的目标是确定最佳的数据压缩率和网络上的缓存位置，以最大程度地减少网络中的总体能耗。我们将问题表述为混合整数非线性规划非凸函数的（MINLP）问题，通常是非确定性的多项式时间硬（NP-hard）。我们提出了一种空间分支定界算法（V-SBB）的变体，它可以提供$ \ epsilon $ -全局最佳解决方案。通过大量的数值实验，我们证明，与仅考虑通信和缓存或通信和计算的任何优化相比，C3优化框架最多可将能源效率提高88％。此外，与其他一些MINLP求解器相比，V-SBB技术提供了相对更好的解决方案，但付出了额外的计算时间。