Simplified Workflow Simulation on Clouds based on Computation and Communication Noisiness

Abstract

Many researchers rely on simulations to analyze and validate their researched methods on Cloud infrastructures. However, determining relevant simulation parameters and correctly instantiating them to match the real Cloud performance is a difficult and costly operation, as minor configuration changes can easily generate an unreliable inaccurate simulation result. Using legacy values experimentally determined by other researchers can reduce the configuration costs, but is still inaccurate as the underlying public Clouds and the number of active tenants are highly different and dynamic in time. To overcome these deficiencies, we propose a novel model that simulates the dynamic Cloud performance by introducing noise in the computation and communication tasks, determined by a small set of runtime execution data. Although the estimating method is apparently costly, a comprehensive sensitivity analysis shows that the configuration parameters determined for a certain simulation setup can be used for other simulations too, thereby reducing the tuning cost by up to 82.46 percent, while declining the simulation accuracy by only 1.98 percent on average. Extensive evaluation also shows that our novel model outperforms other state-of-the-art dynamic Cloud simulation models, leading up to 22 percent lower makespan inaccuracy.

Multi-Objective Optimization of Deadline and Budget-Aware Workflow Scheduling in Uncertain Clouds

“…Similarly, to minimize uncertainty propagation in scheduling workflows, Chen et al [20] model task execution time and data transfer time by means of normal distributions. Mathà et al [33] include performance instability in cloud simulation environments by introducing the concept of noise in computation and communication tasks. Performance data monitored in real cloud environments are the basis for extracting the noise.…”

Data‐aware and simulation‐driven planning of scientific workflows on IaaS clouds

“…In Reference 10, the authors showed that the bandwidth depends on the file sizes, number of files, instance types, and so forth. But our simulations assume a good QoS and the respect of the performance characteristics for the resources allocated by the cloud provider.…”