Exploiting user metadata for energy-aware node allocation in a cloud storage system

• Mechanisms to reduce cloud storage energy consumption by around 60%.
• Dynamic and static storage allocation methods based on user metadata.
• A mathematical model to estimate energy consumption, latency and load balance.
• Theoretical and experimental evaluations of the proposed methods.

Cloud computing has gained popularity in recent years delivering various services as cost-effective platforms. However, the increasing energy consumption needs to be addressed in order to preserve the cost-effectiveness of these systems. In this work, we target the storage infrastructure in a cloud system and introduce several energy efficient storage node allocation methods by exploiting the metadata heterogeneity of cloud users. Our proposed methods preserve load balance on demand and switch inactive nodes into low-energy modes to save energy. We provide a mathematical model to estimate the outcome of proposed methods and conduct theoretical and simulative analyses using real-world workloads.