Cost-efficient task scheduling for executing large programs in the cloud

Sen, Sandip; Li, Jian; Huang, Qilin; Xh, Huang; Shuang, Kai; Jie, Wei

doi:10.1016/j.parco.2013.03.002

Cited by 146 publications

(76 citation statements)

References 24 publications

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“…A similar approach is proposed in [22], where the authors present a cost-efficient task scheduling for executing large programs in the cloud, using two heuristic strategies. The first strategy dynamically maps tasks to the most cost-efficient VMs, based on the concept of Pareto dominance.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

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

Juarez

Ejarque

Badía

2018

Future Generation Computer Systems

180

View full text Add to dashboard Cite

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“…Execution time and cost optimization is the focus in many studies [14,25,27]. The Heterogeneous Earliest Finish Time (HEFT) algorithm in [27] is a well known heuristic that deals with workflow scheduling on heterogeneous systems.…”

Section: Related Workmentioning

confidence: 99%

“…In that way, cost-efficient schedules with an acceptable penalty on execution time are developed. The work in [25] considers different combinations of Virtual Machine (VM) capacities and prices to reduce the monetary cost, while achieving good performance in terms of makespan. Their algorithm initially allocates tasks to the cheapest VMs and then reassigns non-critical tasks (tasks that are not in the critical path) to less expensive VMs stretching their execution time without impacting overall makespan.…”

Section: Related Workmentioning

confidence: 99%

Cost-Efficient CPU Provisioning for Scientific Workflows on Clouds

Pietri

Sakellariou

2016

Economics of Grids, Clouds, Systems, and Services

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Abstract. Cloud providers now offer resources as combinations of CPU frequencies and prices, with faster resources (which operate at higher frequencies) charged at a higher monetary cost. With the emergence of this new pricing scheme, the problem of choosing cost-efficient configurations is becoming even more challenging for users. The frequencies required to achieve cost-efficient configurations may vary in different scenarios, depending on both the provider's pricing model and the application characteristics. In this paper, two cost-aware algorithms that select low-cost CPU frequencies for each resource to complete a scientific workflow application within a deadline and at a minimum cost are presented. The proposed approaches are evaluated and compared through simulation using different pricing models that charge resource provisioning also based on the CPU frequency.

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“…Maximum scheduling rate with CRB is 80%. Sen Su et.al [13] proposed a method to schedule large problem in cloud. Large program is divided into dependent task.…”

Section: Related Workmentioning

confidence: 99%