6,339 research outputs found
Predicting Workflow Task Execution Time in the Cloud using A Two-Stage Machine Learning Approach
Many techniques such as scheduling and resource provisioning rely on performance prediction of workflow tasks for varying input data. However, such estimates are difficult to generate in the cloud. This paper introduces a novel two-stage machine learning approach for predicting workflow task execution times for varying input data in the cloud. In order to achieve high accuracy predictions, our approach relies on parameters reflecting runtime information and two stages of predictions. Empirical results for four real world workflow applications and several commercial cloud providers demonstrate that our approach outperforms existing prediction methods. In our experiments, our approach respectively achieves a best-case and worst-case estimation error of 1.6% and 12.2%, while existing methods achieved errors beyond 20% (for some cases even over 50%) in more than 75% of the evaluated workflow tasks. In addition, we show that the models predicted by our approach for a specific cloud can be ported with low effort to new clouds with low errors by requiring only a small number of executions
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
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 multi-tenant 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.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM
International Conference on Utility and Cloud Computin
Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud
With the advent of cloud computing, organizations are nowadays able to react
rapidly to changing demands for computational resources. Not only individual
applications can be hosted on virtual cloud infrastructures, but also complete
business processes. This allows the realization of so-called elastic processes,
i.e., processes which are carried out using elastic cloud resources. Despite
the manifold benefits of elastic processes, there is still a lack of solutions
supporting them.
In this paper, we identify the state of the art of elastic Business Process
Management with a focus on infrastructural challenges. We conceptualize an
architecture for an elastic Business Process Management System and discuss
existing work on scheduling, resource allocation, monitoring, decentralized
coordination, and state management for elastic processes. Furthermore, we
present two representative elastic Business Process Management Systems which
are intended to counter these challenges. Based on our findings, we identify
open issues and outline possible research directions for the realization of
elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and
P. Hoenisch (2015). Elastic Business Process Management: State of the Art and
Open Challenges for BPM in the Cloud. Future Generation Computer Systems,
Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00
Lotaru: Locally Predicting Workflow Task Runtimes for Resource Management on Heterogeneous Infrastructures
Many resource management techniques for task scheduling, energy and carbon
efficiency, and cost optimization in workflows rely on a-priori task runtime
knowledge. Building runtime prediction models on historical data is often not
feasible in practice as workflows, their input data, and the cluster
infrastructure change. Online methods, on the other hand, which estimate task
runtimes on specific machines while the workflow is running, have to cope with
a lack of measurements during start-up. Frequently, scientific workflows are
executed on heterogeneous infrastructures consisting of machines with different
CPU, I/O, and memory configurations, further complicating predicting runtimes
due to different task runtimes on different machine types.
This paper presents Lotaru, a method for locally predicting the runtimes of
scientific workflow tasks before they are executed on heterogeneous compute
clusters. Crucially, our approach does not rely on historical data and copes
with a lack of training data during the start-up. To this end, we use
microbenchmarks, reduce the input data to quickly profile the workflow locally,
and predict a task's runtime with a Bayesian linear regression based on the
gathered data points from the local workflow execution and the microbenchmarks.
Due to its Bayesian approach, Lotaru provides uncertainty estimates that can be
used for advanced scheduling methods on distributed cluster infrastructures.
In our evaluation with five real-world scientific workflows, our method
outperforms two state-of-the-art runtime prediction baselines and decreases the
absolute prediction error by more than 12.5%. In a second set of experiments,
the prediction performance of our method, using the predicted runtimes for
state-of-the-art scheduling, carbon reduction, and cost prediction, enables
results close to those achieved with perfect prior knowledge of runtimes
Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop
Although information workers may complain about meetings, they are an
essential part of their work life. Consequently, busy people spend a
significant amount of time scheduling meetings. We present Calendar.help, a
system that provides fast, efficient scheduling through structured workflows.
Users interact with the system via email, delegating their scheduling needs to
the system as if it were a human personal assistant. Common scheduling
scenarios are broken down using well-defined workflows and completed as a
series of microtasks that are automated when possible and executed by a human
otherwise. Unusual scenarios fall back to a trained human assistant who
executes them as unstructured macrotasks. We describe the iterative approach we
used to develop Calendar.help, and share the lessons learned from scheduling
thousands of meetings during a year of real-world deployments. Our findings
provide insight into how complex information tasks can be broken down into
repeatable components that can be executed efficiently to improve productivity.Comment: 10 page
- …