Dynamic Voltage and Frequency Scaling Approach for Processing Spatio-Temporal Queries in Mobile Environment

Abstract

AbstractModern information and communication technology along with the broad adaptation of computing paradigms, namely cloud computing has significant impact on environment due to huge amount of CO 2 emission. In the recent decade, a significant growth in the research domain of “green” and low power consumption network has been observed. With the recent advancements of compute intensive machine learning frameworks like deep learning, the energy consumption by GPUs is a challenging issue. In all of these cases, Dynamic Voltage Frequency Scaling (DVFS) has gained significant research interests and shown encouraging outcome. Dynamic Voltage and Frequency Scaling (DVFS) is utilized to reduce the energy consumption in data centers without impacting the Quality of Service. On the other side, spatio-temporal dataset is huge in size and requires compute and time intensive algorithms and techniques for different spatio-temporal query processing. In all aspects, the correlations among energy, DVFS, consolidation, and performance are the key-enablers of energy-efficient management. In this chapter, we explore different existing DVFS techniques and challenges to analyse and process spatio-temporal query in cloud based paradigm. We have considered movement datasets for demonstrating energy consumption and utility of dynamic voltage and frequency scaling.KeywordsDynamic voltage and frequency scalingSpatio-temporal QueryCloud datacenterScheduling algorithmPowerEnergy