Research Area:  Cloud Computing

Abstract:

With the enormous growth of wireless technology, and location acquisition techniques, a huge amount of spatio-temporal traces are being accumulated. This dataset facilitates varied location-aware services and helps to take real-life decisions. Efficiently handling and processing spatio-temporal queries are necessary to respond in real-time. Processing the vast spatio-temporal data requires scalable computing infrastructure. In this regard, an efficient query resolution system can be deployed if we predict the infrastructure requirement of the user query apriori along with the identification of the geospatial service chain. In this work, we propose a framework, namely LYRIC (deadLine and budget aware spatio-temporal querY pRocessing In Cloud), where the spatio-temporal queries are resolved efficiently considering user-defined deadline and budget constraint. Our framework shows high deadline completion accuracy in the range of 1.0 - 0.937, which is more accurate than SparkGIS, GeoSpark, GeoMesa, and JUST. LYRIC reduces the resource prediction error by 11%, considering the geospatial service chain than without it. The cost of the spatio-temporal query is reduced by 23% in LYRIC, further, the simulation study (using CloudSim) illustrates the efficacy and scalability of LYRIC compared to four baseline approaches.

Keywords:  

Author(s) Name:  Jaydeep Das; Shreya Ghosh; Soumya K. Ghosh; Rajkumar Buyya

Publisher name:  IEEE

DOI:  10.1109/TSC.2021.3073006

Volume Information:   Page(s): 1 - 1

Paper Link:   https://ieeexplore.ieee.org/document/9403960