PROJECT TITLE :

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 suppliers orchestrate cloud resources to deliver UGCs during a pay-per-use vogue, with an objective to attenuate the operational financial cost. The monetary price depends on the actual usage of cloud resources (e.g., computing, storage, and bandwidth), which in turn, is suffering from the content placement strategy. During this paper, we have a tendency to investigate this price-optimal content placement problem. Specifically, it is formulated into a constrained optimization drawback, in that the target is to attenuate the full monetary cost, with respect to the resource capability. We have a tendency to tackle this downside via a two-step strategy. The first step focuses on the position for a single content, that is mapped into a -center downside. Using a graph-theoretic approach, we tend to derive and verify a logarithmic model between the optimal mean hop distance from viewers to contents, and the optimal range of content replicas. The second step leverages this analytical result to unravel the price optimization problem, via a possible direction method. The analysis is substantiated via numerical simulations, employing a set of information traces from a prime content web site. This investigation suggests that the optimal number of content replica for every title follows a power-law distribution in respect to its popularity rank. Moreover, it reveals a basic tradeoff between the storage and bandwidth value. Finally, compared to existing heuristics, our proposed algorithm is able to obtain the optimal placement strategy, with lower computational complexity.