Caching is vital for high-throughput networks in data-intensive applications. Dynamic random-access memory (DRAM), often used for caching due to its high data transfer rate, faces limitations in capacity and cost, hindering scalability needed to meet growing demand. Evolving flash storage can augment DRAM, but necessitates caching techniques adapted to its charac-teristics for optimal network performance. This paper models the cache as a set of storage blocks with varying rate parameters and utilization costs. Utilizing a framework that enables joint caching and forwarding, we introduce an optimization technique based on the drift-plus-penalty method. Our approach minimizes the drift-plus-penalty expression in a virtual control plane and offers a throughput-cache utilization cost trade-off. We implement a corresponding practical policy in the data plane. Simulations demonstrate the superior performance of our approach in total delay and cache utilization costs.

We study the problem of minimizing energy costs for content delivery in wireless heterogeneous networks by jointly optimizing caching and power control strategies. This can be equivalently cast as a problem of maximizing the joint caching and power gain subject to meeting minimum signal-to-interference-plus-noise ratio constraints. The offline version of this problem is NP-hard, but we show that there exist polynomial-time approximation algorithms producing solutions within a constant factor 1 - 1/$e$from the optimal. We further provide an adaptive algorithm based on projected subgradient ascent over a concave relaxation of the expected joint caching and power gain, which yields the same approximation guarantee. We show that our proposed algorithm outperforms the alternating optimization method and other baseline algorithms in a number of network scenarios, in total power consumption and run time.

A fundamental challenge in wireless heterogeneous networks (HetNets) is to effectively utilize the limited transmission and storage resources in the presence of increasing deployment density and backhaul capacity constraints. To alleviate bottlenecks and reduce resource consumption, we design optimal caching and power control algorithms for multi-hop wireless HetNets. We formulate a joint optimization framework to minimize the average transmission delay as a function of the caching variables and the signal-to-interference-plus-noise ratios (SINR) which are determined by the transmission powers, while explicitly accounting for backhaul connection costs and the power constraints. Using convex relaxation and rounding, we obtain a reduced-complexity formulation (RCF) of the joint optimization problem, which can provide a constant factor approximation to the globally optimal solution. We then solve RCF in two ways: 1) alternating optimization of the power and caching variables by leveraging biconvexity, and 2) joint optimization of power control and caching. We characterize the necessary (KKT) conditions for an optimal solution to RCF, and use quasi-convexity to show that the KKT points are Pareto optimal for RCF. We then devise a subgradient projection algorithm to jointly update the caching and power variables under general SINR conditions. Finally, our analytical findings are supported by results from extensive numerical experiments.

A fundamental challenge in wireless heterogeneous networks (HetNets) is to effectively use the limited transmission and storage resources in the presence of increasing deployment density and backhaul capacity constraints. To alleviate bottlenecks and reduce resource consumption, we design optimal caching and power control algorithms for multi-hop wireless HetNets. We devise a joint optimization framework to minimize the average transmission delay as a function of the caching variables and the signal-to-interference-plus-noise ratios (SINR) as determined by the transmission powers, while explicitly accounting for backhaul connection costs and the power constraints.Using convex relaxation and rounding, we obtain a reduced-complexity formulation (RCF) of the joint optimization problem, which can provide a constant factor approximation to the globally optimal solution. We characterize the necessary (KKT) conditions for an optimal solution to RCF, and use strict quasi-convexity to show that the KKT points are Pareto optimal for RCF. We then devise a subgradient projection algorithm to jointly update the caching and power variables, and show that under appropriate conditions, the algorithm converges at a linear rate to the local minima of RCF, under general SINR. We support our analytical findings with results from numerical experiments.