Context : Big Data and Cloud Computing nowadays require large amounts of storage that are accessible by many servers. The Energy consumed by these servers as well as that consumed by hosts providing the storage has been growing rapidly over the recent years. There are various approaches to save energy both at the hardware and software level, respectively. In the context of software, this challenge requires identification of new development methodologies that can help reduce the energy footprint of the Distributed Storage System. Until recently, reducing the energy footprint of Distributed Storage Systems is a challenge because there is no new methodology implemented to reduce the energy footprint of the Distributed Storage Systems. To tackle this challenge, we evaluate the energy consumption of Distributed Storage Systems by using a Power Application Programming Interface (PowerAPI) that monitors, in real-time, the energy consumed at the granularity of a system process.

Objectives : In this study we investigate the Energy Consumption of distributed storage system. We also attempt to understand the effect on energy consumption for various patters of video streams. Also we have observed different measurement approaches for energy performance.

Methods : The method is to use a power measuring software library while a synthetic load generator generates the load i.e., video data streams. The Tool which generates the workload is Standard Performance Evaluation Corporation Solution File Server (SPECsfs 2014) and PowerAPI is the software power monitoring library to evaluate the energy consumption of distributed storage systems of GlusterFS and Compuverde.

Results : The mean and median values of power samples in mill watts for Compuverde higher than Gluster. For Compuverde the mean and median values until the load increment of three streams was around a 400 milliwatt value. The values of mean and median for the Gluster system were gradually increasing.

Conclusions : The results show Compuverde having a higher consumption of energy than Gluster as it has a higher number of running processes that implement additional features that do not exist in Gluster. Also we have concluded that the conpuverde performed better for higher values of Load i.e., video data streams.