## 1 Introduction  
Online video streaming, especially video-on-demand (VoD), has been the main driving force for the recent escalation in the overall Internet traffic. To cope with traffic growth, operators are continuously exploring network architectural solutions which provide users with more capacity and improved quality of service (QoS) while keeping network-resource occupation low and avoiding excessive costs. A promising solution consists of enhancing nodes at the edge of the network with storage and computing capabilities [1], allowing edge nodes to deliver services from locations close to end users. In particular, VoD content caching enables storing video content, preferably the most popular, in storage elements (i.e., caches) and delivering them from proximity to end-users, thereby offloading from the network, a substantial amount of traffic [2]. Caching also grants further benefits, such as improved quality of experience (QoE) for the end-users [3] and reduced overall network energy consumption [4]. However, a cache deployment consisting of a high number of large-capacity caches in edge nodes requires a very large economic investment. For instance, a content-centric router with a cache size of 10 TB using flash-based solid-state drives (SSDs) is estimated to cost around $300,000 and consume 500 Watts of power [5]. Thus, it is decisive that network operators, for a given investment, or in other words, under a limited storage capacity, choose the cache deployment strategy which significantly minimizes network resource occupation. _With cache deployment strategy, we refer here to choosing the number of caches, their locations (where in the network) and their sizes (how much storage capacity)_. An effective deployment strategy must take into consideration network topology, users' requirements and characteristics of the service (e.g., size and the popularity distribution of the video content catalog). In this work, we aim to find the optimal cache deployment that minimizes network resource-occupation; i.e., the amount of capacity occupied in the network to perform VoD content delivery, for a given investment in terms of storage capacity (i.e., under limited storage capacity available). Here, we note that recent debates about network-neutral-caching [6] have also addressed a similar problem: regulations of network neutrality force content providers to utilize a specific amount of storage capacity to perform caching [7; 8]. In that case, however, network operators aim to minimize network resource occupation, subject to constraints given by network neutrality regulations, instead of by a limited monetary investment. We focus our analysis on hierarchical optical metro-area networks. Current metro networks feature several hierarchical layers, and, when deploying caches, it is not a trivial task to decide the number of caches to be deployed; where they should be located; and how to distribute the available storage capacity among the caches of the different network layers, specifically if constrained by storage capacity. In our previous work [9], we addressed the problem through developing a discrete event-based simulator for dynamic VoD provisioning. The simulator generates VoD content requests according to VoD-content popularity model, and based on network status (e.g., available bandwidth on links) and cache deployment strategy, provisions the VoD content requests. As an output, the simulator provides the overall network resource (i.e., capacity) occupation and the blocking probability, allowing one to measure the efficiency of a cache deployment strategy. Results demonstrated that, given a budget in terms of storage capacity, the cache deployment that minimizes the overall network capacity occupation is not achieved by deploying all the available storage capacity in the nearest cache locations but by deploying part of the storage capacity in caches at higher network levels.