Dynamic auto-scaling and scheduling of deadline constrained service workloads on IaaS clouds

Coninck, Elias De; Verbelen, Tim; Vankeirsbilck, Bert; Bohez, Steven; Simoens, Pieter; Dhoedt, Bart

doi:10.1016/j.jss.2016.05.011

Cited by 30 publications

(17 citation statements)

References 5 publications

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“…The constraint in Eq. (7) forces that autoscaling solutions must consider at least one instance. Finally, the n constraints in Eq.…”

Section: Mathematical Formulation Of Subproblemsmentioning

confidence: 99%

“…There are also some works particularly addressing workflow autoscaling [7,24,25,10] with deadline or budget constraints. The problem was first addressed by [25] where the authors proposed an autoscaling strategy for the efficient execution of multiple workflow applications subject to deadline constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Later, the same authors moved to the problem of workflow autoscaling but considering budget constraints [10]. Another work is the one presented in [7], where the main characteristics of the tasks in the workflow structure are learned over time, i.e., the autoscaler dynamically adapts the number of allocated resources in order to meet the deadlines of all tasks without knowing the workflow structure itself and without any information of the execution time. The goal of this work was to minimize the makespan.…”

Section: Related Workmentioning

confidence: 99%

“…It is worth noting that, from the related works found, most of them have been proposed for workflows considering task deadlines or budget constraint but without considering spot instances. Besides, another crucial distinction is that in none of the surveyed works the authors have considered to reduce the impact of OOB errors as part of the optimization process when using spot instances, and only in three works [7,26,27] the authors have proposed to minimize the makespan, rendering difficult their applicability to execute scientific workflows in clouds infrastructures to support rapid domain-specific "in vitro" experimentation. Concretely, in the present paper the objectives are to minimize the makespan, monetary cost and the failure probability when different types of spot instances and bid prices are considered.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

CMI: An online multi-objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines

Monge

Pacini

Mateos

et al. 2020

Journal of Network and Computer Applications

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Cloud Computing is becoming the leading paradigm for executing scientific and engineering workflows. The large-scale nature of the experiments they model and their variable workloads make clouds the ideal execution environment due to prompt and elastic access to huge amounts of computing resources. Autoscalers are middleware-level software components that allow scaling up and down the computing platform by acquiring or terminating virtual machines (VM) at the time that workflow's tasks are being scheduled. In this work we propose a novel online multi-objective autoscaler for workflows denominated Cloud Multi-objective Intelligence (CMI), that aims at the minimization of makespan, monetary cost and the potential impact of errors derived from unreliable VMs. In addition, this problem is subject to monetary budget constraints. CMI is responsible for periodically solving the autoscaling problems encountered along the execution of a workflow. Simulation experiments on four well-known workflows exhibit that CMI significantly outperforms a state-of-the-art autoscaler of similar characteristics called Spot Instances Aware Autoscaling (SIAA). These results convey a solid base for deepening in the study of other meta-heuristic methods for autoscaling workflow applications using cheap but unreliable infrastructures.

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“…The constraint in Eq. (7) forces that autoscaling solutions must consider at least one instance. Finally, the n constraints in Eq.…”

Section: Mathematical Formulation Of Subproblemsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

CMI: An online multi-objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines

Monge

Pacini

Mateos

et al. 2020

Journal of Network and Computer Applications

View full text Add to dashboard Cite

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“…Reactive mechanisms includes; dynamic schedul-ing, self-adaptation and rescheduling which are most suitable when the resource in the target environment fluctuates considerably. The online heuristics proposed by De Coninck et al [10] and Cai et al [11] exemplifies dynamic scheduling mechanism which is also know as just-in-time scheduling because tasks are scheduled as they become ready. Rescheduling dynamically reallocates tasks to resources during execution which results to additional communication and synchronization overheads making the technique costly.…”

Section: Related Workmentioning

confidence: 99%

A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment

Muhammad-Bello

Aritsugi

2018

IEICE Trans. Inf. & Syst.

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The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a costoptimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty. key words:handling resource performance uncertainty, deadlineconstrained workflow scheduling, IaaS cloud environment, robust scheduling, uncertain processing time

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An NSGA-III-Based Multi-objective Intelligent Autoscaler for Executing Engineering Applications in Cloud Infrastructures

Yannibelli

Pacini

Monge

et al. 2020

Advances in Soft Computing

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Dynamic auto-scaling and scheduling of deadline constrained service workloads on IaaS clouds

Cited by 30 publications

References 5 publications

CMI: An online multi-objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines

CMI: An online multi-objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines

A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment

An NSGA-III-Based Multi-objective Intelligent Autoscaler for Executing Engineering Applications in Cloud Infrastructures

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