Constructing energy-efficient database systems to reduce economic costs and
environmental impact has been studied for 10 years. With the emergence of the big data …

Graph analysis is a powerful method in data analysis. Although several frameworks have
been proposed for processing large graph instances in distributed environments, their …

MapReduce, originally developed by Google for search applications, has recently become a
popular programming framework for parallel and distributed environments. This paper …

The Internet of Things (IoT) combines large data centers with (mobile, networked) edge
devices that are constrained both in compute power and energy budget. Modern edge …

ARM processors, with their low power consumption and heat dissipation, have been highly
successful in embedded systems. In the recent past, there have been attempts to adopt …

Traditional database systems result in high energy consumption and low energy efficiency
due to the lack of consideration of energy issues and environmental adaptation in the design …

The ever-increasing demand for scalable database systems is limited by their energy
consumption, which is one of the major challenges in research today. While existing …

Abstract In the Big Data Era, the management of energy consumption by servers and data
centers has become a challenging issue for companies, institutions, and countries. In data …

Physical and thermal restrictions hinder commensurate performance gains from the ever
increasing transistor density. While multi-core scaling helped alleviate dimmed or dark …

Data centers are well known to consume a large amount of energy. As databases are one of
the major applications in a data center, building energy-aware database systems has …