Analysis of a Virtual Cloud Storage Service Aware of User Access Patterns

• Samuel de Oliveira Ribeiro UFPI
• Francisco Airton Silva UFPI
• Alex Borges Vieira UFJF
• Glauber Dias Gonçalves UFPI

Abstract

Cloud storage has become an attractive service for users, given its easy use for data backup and sharing. This service can be managed by a virtual provider that uses cloud infrastructure to provide storage for home or business users. In this paper, we investigate a key aspect of a virtual cloud storage service, which is to predict the frequency of data access, thus storing data without access on high latency media, i.e., “cold storage”, thereby reducing costs. We analyze patterns of user data access in Dropbox and propose a framework for predicting the adequate time to move data to cold storage, based on user access history. Our preliminary results show opportunities for a virtual cloud storage service that either benefits users as well as saves up to 23% on storage costs.

Keywords: Cloud data storage, access patterns, data freezing

References

Bocchi, E., Drago, I., and Mellia, M. (2015). Personal Cloud Storage: Usage, Performance and Impact of Terminals. In Proc. of the IEEE CloudNet.

Drago, I., Mellia, M., Munaf`o, M. M., Sperotto, A., Sadre, R., and Pras, A. (2012). Inside Dropbox: Understanding Personal Cloud Storage Services. In Proc. of the ACM IMC.

Gonc¸alves, G., Drago, I., Da Silva, A. P. C., Vieira, A. B., and Almeida, J. M. (2016). The impact of content sharing on cloud storage bandwidth consumption. IEEE Internet Computing, 20(4):26–35.

Gracia-Tinedo, R., Garc´ýa-L´opez, P., G´omez, A., and Illana, A. (2016). Understanding data sharing in private personal clouds. In Proc. of the IEEE CLOUD.

Hsu, Y., Irie, R., Murata, S., and Matsuoka, M. (2018). A novel automated cloud storage tiering system through hot-cold data classification. In Proc. of the IEEE CLOUD.

Irie, R., Murata, S., Hsu, Y., and Matsuoka, M. (2018). A novel automated tiered storage architecture for achieving both cost saving and qoe. In Proc. of the IEEE SC2.

Kaushik, R. T. and Bhandarkar, M. (2010). Greenhdfs: towards an energy-conserving, storage-efficient, hybrid hadoop compute cluster. In Proc. of the USENIX.

Muralidhar, S., Lloyd, W., Roy, S., Hill, C., Lin, E., Liu, W., Pan, S., Shankar, S., Sivakumar, V., Tang, L., et al. (2014). f4: Facebook’s warm blob storage system. In Proc. of the OSDI.