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The recent explosion of data of all kinds (persistent and short-lived) have imposed processing speed constraints on big data processing systems (BDPSs). One such constraint on running these systems in Cloud computing environments is to utilize as many parallel processors as required to process data fast. Consequently, the nodes in a Cloud environment encounter highly crowded clusters of computational units. To properly cater for high degree of parallelism to process data fast, efficient task and resource allocation schemes are required. These schemes must distribute tasks on the nodes in a way to yield highest resource utilization as possible. Such scheduling has proved even more complex in the case of processing of short-lived data. Task scheduling is vital not only to handle big data but also to provide fast processing of data to satisfy modern time data processing constraints. To this end, this paper reviews the most recently published (2020-2021) task scheduling schemes and their deployed algorithms from the fast data processing perspective .
Jalalian et al. (Wed,) studied this question.