Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach

Abstract

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multitenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profiling tasks' execution data using basic statistical description (e.g., mean, standard deviation) or batch offline regression techniques to estimate the runtime may not be suitable for such environments. In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions, we harness fine-grained resources monitoring data in the form of time-series records of CPU utilization, memory usage, and I/O activities that are reflecting the unique characteristics of a task's execution. We compare our solution to a state-of-the-art approach that exploits the resources monitoring data based on regression machine learning technique. From our experiments, the proposed strategy improves the performance, in terms of the error, up to 29.89%, compared to the state-of-the-art solutions.

Delay-Optimal Scheduling of VMs in a Queueing Cloud Computing System with Heterogeneous Workloads

“…The job length S is determined by the application-level workloads and the requesting service (e.g., VM types) [29]- [31]. Although S cannot be obtained precisely before job completion, it can be predicted via estimation technologies [31]- [35]. Particularly, the prediction method using machine learning in reference [32] has achieved a best-case estimation error of 1.6%.…”

Multiple Workflows Scheduling in Multi-tenant Distributed Systems

“…e latest work by Sahoo et al (Sahoo et al 2019) a empts to make OMKR, an online and incremental machine learning approach, scalable to large time-series datasets in a near real-time process. While this approach is still intensively being studied for scienti c work ow area, the preliminary work on such an approach has been presented by Hilman et al (Hilman et al 2018) for future work ow as a service platform.…”

CLUTCH: A Clustering-Driven Runtime Estimation Scheme for Scientific Simulations

“…Table 1 summarizes major similarities and differences between our work and the existing studies. First, the examined papers concern various application domains: load sharing facility (LSF) [8], parallel program [9], [11], [25], cloud [10], [29], HPC [2], [14], [15], [19], location-based services [20]- [23], databases [26]- [28], big data applications [29], [30], and scientific workloads [12], [13], [16]- [19]. The runtime estimation problem addressed in this paper applies to the scientific workloads domain.…”

“…Many of the existing studies [20]- [24], [28] build neural-net based learning models and utilize them for deriving estimated time; many others [2], [10], [16]- [19], [25], [30] use tree and linear regression based machine learning models. Some other works [11], [12], [14], [15] use hybrid methods combining these tools-analytical model, machine learning, and deep learning. In this article, we use what we consider the two most relevant of these works [16], [17] for performance comparison.…”

“…Fourth, the availability of hardware-specific parameter data differs from the existing works. The algorithms proposed in some of the studies [2], [12]- [14], [16], [17], [24], [29] require hardware information that may enhance the quality of the runtime estimation, while those proposed in some other studies [15], [18] require no such information.…”