Linear Scheduling of Big Data Streams on Multiprocessor Sets in the Cloud

Nowadays, there is an accelerating need to efficiently and timely handle large amounts of data that arrives continuously. Streams of big data led to the emergence of Distributed Stream Processing Systems (DSPS) that assign processing tasks to the available resources (dynamically or not) and route streaming data between them. Efficient scheduling of processing tasks of data flows can reduce application latencies and eliminate network congestion. In this work, we propose a linear complexity scheme for the task allocation and scheduling problem to improve system’s performance, load balancing and memory efficiency, in applications where there is need for heavy communication (all-to-all) between the tasks assigned to pairs of components.