Budget-Aware Scheduling Algorithms for Scientific Workflows with Stochastic Task Weights on Heterogeneous IaaS Cloud Platforms

Caniou, Yves; Caron, Eddy; Chang, Aurélie Kong Win; Robert, Yves

doi:10.1109/ipdpsw.2018.00014

Cited by 14 publications

(17 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These two phases are repeated until all tasks are scheduled. These algorithms have been designed to plan independent task sets; therefore, they are not well suited for workflow and workflow planning, as indicated in [5].…”

Section: Related Workmentioning

confidence: 99%

“…In [5] an algorithm based on HEFT is presented. This algorithm divides a client's budget by the number of workflows to schedule in a public Cloud environment.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, this paper also extends Min-Min and shows that Min-Min is less effective than HEFT in minimizing makespan. The objective considered in [5] is to respect a user budget. As for [6], [16], a public Cloud environment is considered and the assumption of infinite resources is made.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Workflow Scheduling Deadline-Based Heuristic for Energy Optimization in Cloud

Cadorel

Coullon

Menaud

2019

2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE C

View full text Add to dashboard Cite

This article addresses the scheduling of heterogeneous scientific workflows while minimizing the energy consumption of the cloud provider, by introducing a deadline sensitive algorithm. Scheduling in a cloud environment is a difficult optimization problem. Usually, work around the scheduling of scientific workflows focuses on public clouds where infrastructure management is an unknown black box. Thus, many works offer scheduling algorithms designed to select the best set of virtual machines over time, so that the cost to the end user is minimized. This article presents a new HEFTbased algorithm that takes into account users deadlines to minimize the number of machines used by the cloud provider. The results show the real benefits of using our algorithm for reducing the energy consumption of the cloud provider.

show abstract

Section: Related Workmentioning

confidence: 99%

“…In [5] an algorithm based on HEFT is presented. This algorithm divides a client's budget by the number of workflows to schedule in a public Cloud environment.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Workflow Scheduling Deadline-Based Heuristic for Energy Optimization in Cloud

Cadorel

Coullon

Menaud

2019

2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE C

View full text Add to dashboard Cite

show abstract

“…They are thus dealing with uncertainties rather than a true non-clairvoyant setting. The work in [8] targets stochastic tasks but is limited to taking static decisions (no task interruption).…”

Section: Cloud Computingmentioning

confidence: 99%

Scheduling independent stochastic tasks on heterogeneous cloud platforms

Gao

Canon

Robert

et al. 2019

2019 IEEE International Conference on Cluster Computing (CLUSTER)

Self Cite

View full text Add to dashboard Cite

This work introduces scheduling strategies to maximize the expected number of independent tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The cloud platform is composed of several types of virtual machines (VMs), where each type has a unit execution cost that depends upon its characteristics. The amount of budget spent during the execution of a task on a given VM is the product of its execution length by the unit execution cost of that VM. The execution lengths of tasks follow a variety of standard probability distributions (exponential, uniform, half-normal, lognormal, gamma, inverse-gamma and Weibull) whose mean and standard deviation both depend upon the VM type. Finally, there is a global available budget and a deadline constraint, and the goal is to successfully execute as many tasks as possible before the deadline is reached or the budget is exhausted (whichever comes first). On each VM, the scheduler can decide at any instant to interrupt the execution of a (long) running task and to launch a new one, but the budget already spent for the interrupted task is lost. The main questions are which VMs to enroll, and whether and when to interrupt tasks that have been executing for some time. We assess the complexity of the problem by showing its NP-completeness and providing a 2-approximation for the asymptotic case where budget and deadline both tend to infinity. Then we introduce several heuristics and compare their performance by running an extensive set of simulations.

show abstract

“…They are thus dealing with uncertainties rather than a true non-clairvoyant setting. The work in [5] targets stochastic tasks but is limited to taking static decisions (no task interruption). Some works are limited to a particular type of application like MapReduce [12], [24].…”

Section: Related Workmentioning

confidence: 99%

Scheduling Independent Stochastic Tasks Under Deadline and Budget Constraints

Canon

Chang

Robert

et al. 2018

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

Self Cite

View full text Add to dashboard Cite

This paper discusses scheduling strategies for the problem of maximizing the expected number of tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The execution times of tasks follow IID probability laws. The main questions are how many processors to enroll and whether and when to interrupt tasks that have been executing for some time. We provide complexity results and an asymptotically optimal strategy for the problem instance with discrete probability distributions and without deadline. We extend the latter strategy for the general case with continuous distributions and a deadline and we design an efficient heuristic which is shown to outperform standard approaches when running simulations for a variety of useful distribution laws.

show abstract

Budget-Aware Scheduling Algorithms for Scientific Workflows with Stochastic Task Weights on Heterogeneous IaaS Cloud Platforms

Cited by 14 publications

References 23 publications

A Workflow Scheduling Deadline-Based Heuristic for Energy Optimization in Cloud

A Workflow Scheduling Deadline-Based Heuristic for Energy Optimization in Cloud

Scheduling independent stochastic tasks on heterogeneous cloud platforms

Scheduling Independent Stochastic Tasks Under Deadline and Budget Constraints

Contact Info

Product

Resources

About