A Machine Learning Framework for Optimising File Distribution Across Multiple Cloud Storage Services

Storing data using a single cloud storage service may lead to several potential problems
for the data owner. Such issues include service continuity, availability, performance,
security, and the risk of vendor lock-in. A promising solution is to distribute the data
across multiple cloud storage services , similarly to the manner in which data are distributed
across multiple physical disk drives to achieve fault tolerance and to improve
performance . However, the distinguishing characteristics of different cloud providers,
in term of pricing schemes and service performance, make optimising the cost and performance
across many cloud storage services at once a challenge. This research proposes
a framework for automatically tuning the data distribution policies across multiple cloud
storage services from the client side, based on file access patterns. The aim of this work
is to explore the optimisation of both the average cost per gigabyte and the average service
performance (mainly latency time) on multiple cloud storage services . To achieve
these aims, two machine learning algorithms were used:
1. supervised learning to predict file access patterns.
2. reinforcement learning to learn the ideal file distribution parameters.
File distribution over several cloud storage services . The framework was tested in a
cloud storage services emulator, which emulated a real multiple-cloud storage services
setting (such as Google Cloud Storage, Amazon S3, Microsoft Azure Storage, and Rack-
Space file cloud) in terms of service performance and cost. In addition, the framework
was tested in various settings of several cloud storage services. The results of testing
the framework showed that the multiple cloud approach achieved an improvement of
about 42% for cost and 76% for performance. These findings indicate that storing data
in multiple clouds is a superior approach, compared with the commonly used uniform
file distribution and compared with a heuristic distribution method.