Today a huge amount of diverse data is produced very fast by various sources like sensors, social networks, stock markets, electronic businesses every second. This gives rise to the era of big data. Almost all data have valuable information that can be earned by processing of data. Some data have short lifetime and lose their value fast. Besides, processing of large volumes of data is a big challenge. One solution for handling of such data is distributed and fast parallel processing. A set of clusters consisting of heterogeneous compute nodes can be used for distributed parallel processing. In such a big system, allocating suitable big data and tasks to the compute nodes play an important role. Since different tasks for processing of various data might have different requirements, the main goal of an autonomous task scheduler is to allocate tasks to appropriate compute nodes for fast big data processing. In this paper, we propose a novel autonomous task scheduler for fast processing of big data on a given cluster with heterogeneous compute nodes. We use Spark as our data processing framework alongside Mesos as a cluster manager that provides efficient resource isolation and sharing across distributed applications and frameworks. We discuss how our scheduler tackles the notable challenges in the way of fast processing of big data on the configured heterogeneous cluster.
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