Optimal Energy Tradeoff Among Communication, Computation and Caching with QoI-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 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. The formulated problem is a 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 -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.

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

“…Instead of solving the MINLP problem in (6) directly, we reformulate the MINLP (6) into a standard and equivalent form, which is needed by the V-SBB, by using an approach called Symbolic Reformulation [21] that is suitable for small problems. We omit the details here due to space constraint and refer readers to [22] for a detailed discussion.…”

“…Here, we use the variable and the value selection rule specified in [21], under which the variable for the branching decision is the one that causes the maximal reduction in the feasibility gap between the solution of Step 2 and the exact problem. Then we partition R into R right and R left , and add them into L as well as delete R. Detailed discussion of the algorithm can be found in [22].…”

“…Though we have made modifications to obtain our V-SBB algorithm, the proof for -global optimality follows an argument similar to that of the Branch-and-Select method in [26]. We present the poof in [22] for completeness.…”

Energy-Efficient Proactive Caching with Multipath Routing

“…The inherent lack of user request arrival knowledge case is considered in [12] where caching, transcoding, and backhaul retrieving are jointly optimized. Another joint optimization research can be found in [16] which concentrates on the trade-offs between communication, computation and caching.…”

“…The majority of the above works do not take the routing policies into consideration [6], [8], [9], [12]- [15] or only adopt the shortest path [7], [10], [11], [16]. However, it is insufficient to only consider the shortest path routing especially when the network bandwidth is limited.…”