Hybrid Intellectual Scheme for Scheduling of Heterogeneous Workflows based on Evolutionary Approach and Reinforcement Learning

Mikhail Melnik, Ivan Dolgov, Denis Nasonov


Scheduling of workload in distributed computing systems is a well-known optimization proble. A workload may include single independent software packages. However, the computational process in scientific and industrial fields is often organized as composite applications or workflows which are represented by collection of interconnected computing packages that solve a common problem. We identified three common computing modes: batch, stream and iterative. The batch mode is a classic way for one-time execution of software packages with an initially specified set of input data. Stream mode corresponds to launch of a software package for further continuous processing of active data in real time. Iterative mode is a launching of a distributed application with global synchronization at each iteration. Each computing mode has its own specifics for organization of computation process. But at the moment, there are new problems that require organization of interaction between computing modes (batch, stream, iterative) and to develop optimization algorithms for this complex computations that leads to formation of heterogeneous workflows. In this work, we present a novel developed hybrid intellectual scheme for organizing and scheduling of heterogeneous workflows based on evolutionary computing and reinforcement learning methods.


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