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Network cache allocation and management are important aspects of the design of an Information-Centric Network (ICN), such as one based on Named Data Networking (NDN). We address the problem of optimal cache size allocation and content placement in an ICN in order to maximize the caching gain resulting from routing cost savings. While prior art assumes a given cache size at each network node and focuses on content placement, we study the problem when a global, network-wide cache storage budget is given and we solve for the optimal per-node cache allocation. This problem arises in cloud-based network settings where each network node is virtualized and housed within a cloud data center node with associated dynamic storage resources acquired from the cloud node as needed. With the offline centralized version of the optimal cache allocation problem being NP-hard, we develop a distributed adaptive algorithm that provides an approximate solution within a constant factor from the optimal. Performance evaluation of the algorithm is carried out through extensive simulations involving a variety of network topologies, establishing experimentally that our proposal significantly outperforms existing cache allocation algorithms.
In this paper, we investigate a cache updating system with a server containing $N$ files, $K$ relays and $M$ users. The server keeps the freshes
We propose a new optimal consumption model in which the degree of addictiveness of habit formation is directly controlled through a consumption constraint. In particular, we assume that the individual is unwilling to consume at a rate below a certain
This article investigates the cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA). The delivery of a large volume of multimedia contents for ground users is
We consider a cache updating system with a source, a cache with limited storage capacity and a user. There are $n$ files. The source keeps the freshes
How to strategically allocate the available vaccines is a crucial issue for pandemic control. In this work, we propose a mathematical framework for optimal stabilizing vaccine allocation, where our goal is to send the infections to zero as soon as po