Robust Deadline-Constrained Resource Provisioning and Workflow Scheduling Algorithm for Handling Performance Uncertainty in IaaS Clouds
Bilkisu Larai Muhammad-Bello, Masayoshi Aritsugi · 2017
Scheduling the execution of scientific applications expressed as workflows on Infrastructure as a Service (IaaS) Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Clouds. Our proposed model addresses the uncertainties related to: the estimation of task execution times, and the delay in provisioning computational Cloud resources. The workflow scheduling problem is considered as a cost-optimized, deadline-constrained optimization problem. In our model, we consider knowledge of the interval of uncertainty for modeling the execution time rather than using a known probability distribution function or precise estimations which are very sensitive to variations. Simulation results from experiments with synthetic workflows show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty.