Toward Cost-Efficient Content Placement in Media Cloud: Modeling and Analysis

Abstract

Cloud-centric media network (CCMN) was previously proposed to provide cost-effective content distribution services for user-generated contents (UGCs) based on media cloud. CCMN service providers orchestrate cloud resources to deliver UGCs in a pay-per-use style, with an objective to minimize the operational monetary cost. The monetary cost depends on the actual usage of cloud resources (e.g., computing, storage, and bandwidth), which in turn, is affected by the content placement strategy. In this paper, we investigate this cost-optimal content placement problem. Specifically, it is formulated into a constrained optimization problem, in which the objective is to minimize the total monetary cost, with respect to the resource capacity. We tackle this problem via a two-step strategy. The first step focuses on the placement for a single content, which is mapped into a $k$ -center problem. Using a graph-theoretic approach, we derive and verify a logarithmic model between the optimal mean hop distance from viewers to contents, and the optimal number of content replicas. The second step leverages this analytical result to solve the cost optimization problem, via a feasible direction method. The analysis is substantiated via numerical simulations, using a set of data traces from a top content website. This investigation suggests that the optimal number of content replica for each title follows a power-law distribution in respect to its popularity rank. Moreover, it reveals a fundamental tradeoff between the storage and bandwidth cost. Finally, compared to existing heuristics, our proposed algorithm is able to obtain the optimal placement strategy, with lower computational complexity.