Many applications must ingest and analyze data that are continuously generated over time from geographically distributed sources such as users, sensors and devices. This results in the need for efficient data analytics in geo-distributed systems. Energy efficiency is a fundamental requirement in these geo-distributed 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, and do not account for caching costs. Given the increasing interest in cache networks, this is a serious deficiency. In this paper, we consider the energy consumption tradeoff among communication, computation, and caching (C3) for data analytics under a Quality of Information (QoI) guarantee in a geo-distributed system. To attain this goal , we formulate an optimization problem to capture the C3 costs, which turns out to be a non-convex Mixed Integer Non-Linear Programming }  (MINLP) Problem. We then  propose a variant of spatial branch and bound algorithm (V-SBB), that can achieve e-global  optimal solution to the original MINLP. We show numerically that V-SBB is more stable and robust than other candidate MINLP solvers under different network scenarios. More importantly, we observe that the energy efficiency under { our C3 optimization framework improves by as much as 88 \% compared to any C2 optimization between communication and computation or caching.%DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > 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.}}
 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 (C3) 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 aMixed 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 %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > 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.}}
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