Energy consumption in big data environments – a systematic mapping study

Big Data is a term that describes a large volume of structured and unstructured data. Big Data must be acquired, stored, analyzed and visualized by means of non-conventional methods requiring normally a big set of resources, which includes energy consumption. Although Big Data is not new as a phenomenom, its explosion of the interest in literature is recent and its study in new scenarios presents several gaps. On the other hand, Green IT is also a growing field in computing, given the increasing role of IT in energy consumption in the world. Green IT is aimed to reduce IT-related energy consumption and overall IT environmental impact. In order to investigate the reported initiatives regarding the Big Data and Green IT with a focus of energy consumption, the authors conducted a systematic mapping on the topic. The search strategy which was used resulted in 28 relevant studies which were relevant to the topic. We found that a majority of the studies performed present algorithms designed to reduce the energy consumption in data centres. The rest of the studies present benchmarks and energy measurements, reviews, proposals of hardware-based solutions, as well as studies which give an overview of one or more aspects on Big Data.publishedVersio

Energy-aware adaptation in managed Cassandra datacenters

Today, Apache Cassandra, an highly scalable and available NoSql datastore, is largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra datacenters. As for all complex services, human assisted management of a multi-tenant cassandra datacenter is unrealistic. Rather, there is a growing demand for autonomic management solutions. In this paper, we present an optimal energy-aware adaptation model for managed Cassandra datacenters that modify the system configuration orchestrating three different actions: horizontal scaling, vertical scaling and energy aware placement. The model is built from a real case based on real application data from Ericsson AB. We compare the performance of the optimal adaptation with two heuristics that avoid system perturbations due to re-configuration actions triggered by subscription of new tenants and/or changes in the SLA. One of the heuristic is local optimisation and the second is a best fit decreasing algorithm selected as reference point because representative of a wide range of research and practical solutions. The main finding is that heuristic's performance depends on the scenario and workload and no one dominates in all the cases. Besides, in high load scenarios, the suboptimal system configuration obtained with an heuristic adaptation policy introduce a penalty in electric energy consumption in the range [+25%, +50%] if compared with the energy consumed by an optimal system configuration