Microscheduling and Randomization Strategies in Distributed and Cloud Computing

In this work, we study heuristic and randomized approaches for parallel jobs execution in high performance computing environments with heterogeneous resources. Traditional process of a job-flow scheduling implies many requirements for jobs execution criteria and priority and, thus, usually cannot be changed. However, special microscheduling heuristics may be used in such scenarios as a secondary optimization based on the primary scheduling procedure. Coordinated and randomized microscheduling approaches simulate different strategies for the whole job queue scheduling in order to select the most resource-efficient scenarios. Based on a conservative backfilling scheduling procedure we study how different resources allocation heuristics affect integral job-flow scheduling characteristics in a dedicated simulation environment.