More and more, users store their data in the cloud. While the content is then retrieved, the retrieval has to respect quality of service (QoS) constraints. In order to reduce transfer latency, data is replicated. The idea is make data close to users and to take advantage of providers home storage. However to minimize the cost of their platform, cloud providers need to limit the amount of storage usage. This is still more crucial for big contents.
This problem is hard, the distribution of the popularity among the stored pieces of data is highly non-uniform: several pieces of data will never be accessed while others may be retrieved thousands of times. Thus, the trade-off between storage usage and QoS of data retrieval has to take into account the data popularity.
This paper presents our architecture gathering several storage domains composed of small-sized datacenters and edge devices; and it shows the importance of adapting the replication degree to data popularity.
Our simulations, using realistic workloads, show that a simple cache mechanism provides a eight-fold decrease in the number of SLA violations, requires up to 10 times less of storage capacity for replicas, and reduces aggregate bandwidth and number of flows by half.