Energy-Constrained Provisioning for Scientific Workflow Ensembles

Pietri, Ilia; Malawski, Maciej; Juve, Gideon; Deelman, Ewa; Nabrzyski, Jarek; Sakellariou, Rizos

doi:10.1109/cgc.2013.14

Cited by 41 publications

(36 citation statements)

References 18 publications

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“…Pietri et al [46] proposed two energy-aware algorithms SPSS-ED and SPSS-EB for resource provisioning to schedule workflow ensembles on basis of SPSS. The first algorithm called SPSS-ED focuses on meeting energy and deadline constraints while another one called SPSS-EB focuses on meeting energy and budget constraints.…”

Section: Spss-ed and Spss-ebmentioning

confidence: 99%

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

Chakravarthi

Vijayakumar

2018

IJECE

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In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers.

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Section: Spss-ed and Spss-ebmentioning

confidence: 99%

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

Chakravarthi

Vijayakumar

2018

IJECE

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“…In those cases, we can only use traditional software for scheduling task on resources. For instance in [7], the authors propose an energy-constrained provisioning for scientific workflow ensembles, focused on the provisioning of resources for scientific workflow ensembles and address the problem of meeting energy constraints along with either budget or deadline constraints. The application model is focused on workflow based applications modeled as a DAG and do not take into account the time for data transfers.…”

Section: Related Workmentioning

confidence: 99%

“…Traditionally, scheduling techniques have tried to minimize the total execution time of an application(makespan-C max ) [7] without worrying about the energy consumed. However, there is a trade-off between energy consumed and the execution time, and sometimes increasing the performance for a faster execution implies a higher energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Juarez

Ejarque

Badía

2018

Future Generation Computer Systems

180

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h i g h l i g h t s• Energy-aware run-time scheduler for task-based applications.• Model for estimating the application Energy consumption.• Methodology to automatically generate the required power consumption profile.• Multi-heuristic resource allocation algorithm to get solutions in polynomial time.• Energy saving/performance trade-off evaluation for different scenarios. a r t i c l e i n f o b s t r a c tGreen Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently taskbased applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling.

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“…Pietri et al [19] consider the scheduling of scientific workflows onto homogeneous compute resources subject to energy constraints in the presence of budget or deadline constraints.…”

Section: B Energy Considerations In Workflowsmentioning

confidence: 99%

Energy-Aware Simulation of Workflow Execution in High Throughput Computing Systems

McGough

Forshaw

2015

2015 IEEE/ACM 19th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-Workflows offer a great potential for enacting corelated jobs in an automated manner. This is especially desirable when workflows are large or there is a desire to run a workflow multiple times. Much research has been conducted in reducing the makespan of running workflows and maximising the utilisation of the resources they run on, with some existing research investigates how to reduce the energy consumption of workflows on dedicated resources. We extend the HTC-Sim simulation framework to support workflows allowing us to evaluate different scheduling strategies on the overheads and energy consumption of workflows run on non-dedicated systems. We evaluate a number of scheduling strategies from the literature in an environment where (workflow) jobs can be evicted by higher priority users.

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Energy-Constrained Provisioning for Scientific Workflow Ensembles

Cited by 41 publications

References 18 publications

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Energy-Aware Simulation of Workflow Execution in High Throughput Computing Systems

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