LYRIC Deadline and Budget Aware Spatio-Temporal Query Processing in Cloud

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. This also 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 in terms of optimal budget usage and execution time compared to four baseline approaches

 EXISTING SYSTEM :

 ? Existing continuous query processors for spatio-temporal databases assume explicitly that all incoming data can be stored in secondary storage. ? A wide variety of spatio-temporal access methods (e.g., see (Mokbel et al., 2003b) for a survey) has been introduced to deal with massive sizes of spatio-temporal data. ? Most of the existing techniques in spatio-temporal databases abstract the continuous query into a series of snapshot queries executed at regular interval times. ? Instead, we furnish existing data stream management systems by a set of primitive spatio-temporal pipeline operators (e.g., the INSIDE and kNN operators).

 DISADVANTAGE :

 ? In the context of the spatio-temporal query, we define our problem space into two broad aspects: (i) static spatial objects (these are “fixed location assets”, such as buildings, road-segments, lakes, mountains, etc.) where location information does not change with time; and (ii) moving objects in the two-dimensional space (moving agents, say, trajectory traces of people, vehicles, etc.). ? It may be noted, that temporal information is crucial in the latter case, since the location changes with time-instances, along with the data size.

 PROPOSED SYSTEM :

 • In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware environments). • We implemented the proposed continuous query processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable locationaware database server developed at Purdue University. • The PLACE server extends data streaming management systems to support location-aware environments. • If the trajectory information changes, then the query needs to be reevaluated.

 ADVANTAGE :

 ? This learning achieved query scheduling with proper cost and performance management, which met the service level agreement(SLA) of user and service provider. ? Many researchers have also analyzed the performance and latency of analytical queries through machine learning techniques. ? They used Granite distributed engine over Graphite ICM platform for experiments. Another graph embedded query performance prediction for concurrent queries has been proposed by . ? They also used the graph update and compaction algorithm to determine the query workload. Chu et al. predicts the query execution time using LSTM in graph database. Encoding the query plan tree, they used a post-order traversal algorithm.