This work considers the impact of different contexts when attempting to exploit parallelization approaches for processing continuous spatio-temporal queries. More specifically, we are interested in various trade-off aspects that may arise due to differences of the computing environments like, for example, multicore vs. cloud. Algorithmic solutions for parallel processing of spatio-temporal queries cater to splitting the load among units - be it based on the data or the query (or both) - relying to a bigger or lesser degree on a certain set of features of a given environment. We postulate that incorporating the service-features should be coupled with the algorithms/heuristics for processing particular queries, in addition to the volume of the data. We present the current version of the implementation of our P2EST system and analyze the execution of different heuristics for parallel processing of spatio-temporal range queries.