Practical Resource Monitoring for Robust High Throughput Computing

Juve, Gideon; Tovar, Benjamín; Silva, Rafael Ferreira da; Król, Dariusz; Thain, Douglas; Deelman, Ewa; Allcock, William; Livny, Miron

doi:10.1109/cluster.2015.115

Cited by 23 publications

(12 citation statements)

References 18 publications

(12 reference statements)

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“…We profiled these workflows when executed with Pegasus [5], a state-of-the-art workflow management system. Pegasus monitors and logs fine-grained profiling data such as I/O operations, runtime, memory usage, and CPU utilization [13].…”

Section: Workflow Characterizationmentioning

confidence: 99%

Accurately Simulating Energy Consumption of I/O-Intensive Scientific Workflows

Silva¹,

Orgerie

Casanova

et al. 2019

Lecture Notes in Computer Science

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While distributed computing infrastructures can provide infrastructure-level techniques for managing energy consumption, application-level energy consumption models have also been developed to support energy-efficient scheduling and resource provisioning algorithms.In this work, we analyze the accuracy of a widely-used application-level model that have been developed and used in the context of scientific workflow executions. To this end, we profile two production scientific workflows on a distributed platform instrumented with power meters. We then conduct an analysis of power and energy consumption measurements. This analysis shows that power consumption is not linearly related to CPU utilization and that I/O operations significantly impact power, and thus energy, consumption. We then propose a power consumption model that accounts for I/O operations, including the impact of waiting for these operations to complete, and for concurrent task executions on multi-socket, multi-core compute nodes. We implement our proposed model as part of a simulator that allows us to draw direct comparisons between real-world and modeled power and energy consumption. We find that our model has high accuracy when compared to real-world executions. Furthermore, our model improves accuracy by about two orders of magnitude when compared to the traditional models used in the energyefficient workflow scheduling literature.

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Section: Workflow Characterizationmentioning

confidence: 99%

Accurately Simulating Energy Consumption of I/O-Intensive Scientific Workflows

Silva¹,

Orgerie

Casanova

et al. 2019

Lecture Notes in Computer Science

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“…We profiled the 1000 genome sequencing analysis workflow using the Kickstart [42] profiling tool. Kickstart monitors and records task execution in scientific workflows (e.g., process I/O, runtime, memory usage, and CPU utilization).…”

Section: Workflow Characterizationmentioning

confidence: 99%

Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows

Silva

Filgueira

Deelman

et al. 2019

Future Generation Computer Systems

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Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. Although the scientific community has addressed this challenge from both theoretical and practical approaches, failure prediction, detection, and recovery still raise many research questions. In this paper, we propose an approach inspired by the control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach is inspired on the proportionalintegral-derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, where the controller will react to adjust its output to mitigate faults. PID controllers aim to detect the possibility of a non-steady state far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of large scale data-intensive workflows-data storage overload and memory overflow. We developed a simulator, which implements and evaluates simple standalone PID-inspired controllers to autonomously manage data and memory usage of a data-intensive bioinformatics workflow that consumes/produces over 4.4TB of data, and requires over 24TB of memory to run all tasks concurrently. Experimental results obtained via simulation indicate that workflow executions may significantly benefit from the controller-inspired approach, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed method, and faults are detected and mitigated far in advance of their occurrence.

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“…In this section, we characterize execution profiles for the scientific workflows described in the previous section by using the Kickstart [14,15] profiling tool, and the Pegasus Workflow Management System (WMS) [24,25]. Kickstart monitors and records information about the execution of individual workflow tasks.…”

Section: Workflow Characterizationmentioning

confidence: 99%

Online Task Resource Consumption Prediction for Scientific Workflows

Silva

Juve

Rynge

et al. 2015

Parallel Process. Lett.

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Estimates of task runtime, disk space usage, and memory consumption, are commonly used by scheduling and resource provisioning algorithms to support efficient and reliable workflow executions. Such algorithms often assume that accurate estimates are available, but such estimates are difficult to generate in practice. In this work, we first profile five real scientific workflows, collecting fine-grained information such as process I/O, runtime, memory usage, and CPU utilization. We then propose a method to automatically characterize workflow task requirements based on these profiles. Our method estimates task runtime, disk space, and peak memory consumption based on the size of the tasks' input data. It looks for correlations between the parameters of a dataset, and if no correlation is found, the dataset is divided into smaller subsets using a clustering technique. Task estimates are generated based on the ratio parameter/input data size if they are correlated, or based on the probability distribution function of the parameter. We then propose an online estimation process based on the MAPE-K loop, where task executions are monitored and estimates are updated as more information becomes available. Experimental results show that our online estimation process results in much more accurate predictions than an offline approach, where all task requirements are estimated prior to workflow execution.

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Practical Resource Monitoring for Robust High Throughput Computing

Cited by 23 publications

References 18 publications

Accurately Simulating Energy Consumption of I/O-Intensive Scientific Workflows

Accurately Simulating Energy Consumption of I/O-Intensive Scientific Workflows

Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows

Online Task Resource Consumption Prediction for Scientific Workflows

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