The general objective of our work is to reduce the
energy usage of computing and at the same time, to reduce the
total cost of ownership of running a computing infrastructure.
The focus of this paper is on an energy-efficient storage system
that distributes data across multiple data centers. While our
approach is generally applicable, the motivation for our work
is driven by the challenge on how to set up energy-efficient and
cost-effective edge clouds. Edge clouds are, for example, a central
part of the vision for the 5th generation of cellular communication
[1]: an edge cloud enables service providers to move the data and
computing closer to end users.
This work extends our energy-conserving implementation of
a consistent hashing based key-value store, called PowerCass
[2]. PowerCass is based on Apache’s Cassandra and divides
storage nodes into three groups: active, dormant, and sleepy.
Nodes in the active group store cover all the data and are running
continuously. Dormant nodes are only powered during peak
activity time and for replica synchronization. Sleepy nodes are
offline almost all the time except for replica synchronization and
exceptional peak loads. With this simple and elegant approach,
we are able to reduce the energy consumption by up to 66%
compared to the unmodified key-value store Cassandra. In this
paper, we sketch how we plan to extend PowerCass to permit a
better load distribution between clouds