A Dynamic Hybrid Resource Provisioning Approach for Running Large-Scale Computational Applications on Cloud Spot and On-Demand Instances

Lu, Sifei; Li, Xiaorong; Wang, Long; Kasim, Henry; Palit, Henry Novianus; Hung, Terence; Legara, Erika Fille; Lee, Gary

doi:10.1109/icpads.2013.117

Cited by 14 publications

(19 citation statements)

References 25 publications

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“…In [23], after analyzing the characteristic of the spot price and the effect of spot instances disturbance, the authors proposed a dynamic approach for running the applications with the aim of reducing cost, increasing the reliability and reducing the complexity of fault tolerance without affecting the overall performance and scalability. However, the main difference between the works [8,23] and ours is that we focus on a budget-constrained autoscaling problem while the efforts mentioned focus on solving scheduling problems subject to task deadline constraints, thus they are useful in different scenarios. Another important distinction is that we are also focused in reducing the probability of failures -to avoid heavily affecting workflow application results-and without relying on spot price predictors.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

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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“…Some works take into account Amazon spot VMs instance features. In [18], Lu et al use hybrid instances, including both on-demand instances for high priority tasks and backup, and spot instances for normal computational tasks. Authors of [19] propose to switch to on-demand resources when there is no spot instance available to ensure the desired performance.…”

Section: Related Workmentioning

confidence: 99%

A Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds

Teylo

Arantes²,

Sens³

et al. 2019

2019 31st International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Cloud platforms have emerged as a prominent environment to execute high performance computing (HPC) applications providing on-demand resources as well as scalability. They usually offer different classes of Virtual Machines (VMs) which ensure different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in Amazon EC2 cloud, the user pays per hour for on-demand VMs while spot VMs are unused instances available for lower price. Despite the monetary advantages, a spot VM can be terminated, stopped, or hibernated by EC2 at any moment.Using both hibernation-prone spot VMs (for cost sake) and on-demand VMs, we propose in this paper a static scheduling for HPC applications which are composed by independent tasks (bag-of-task) with deadline constraints. However, if a spot VM hibernates and it does not resume within a time which guarantees the application's deadline, a temporal failure takes place. Our scheduling, thus, aims at minimizing monetary costs of bagof-tasks applications in EC2 cloud, respecting its deadline and avoiding temporal failures. To this end, our algorithm statically creates two scheduling maps: (i) the first one contains, for each task, its starting time and on which VM (i.e., an available spot or on-demand VM with the current lowest price) the task should execute; (ii) the second one contains, for each task allocated on a VM spot in the first map, its starting time and on which on-demand VM it should be executed to meet the application deadline in order to avoid temporal failures. The latter will be used whenever the hibernation period of a spot VM exceeds a time limit.Performance results from simulation with task execution traces, configuration of Amazon EC2 VM classes, and VMs market history confirm the effectiveness of our scheduling and that it tolerates temporal failures.

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“…Lu et al [19] used spot resources for executing BoT jobs. The authors focus on the robustness of the system by using on-demand VMs, which are usually more expensive.…”

Section: Spot Vmsmentioning

confidence: 99%

“…Finally, the majority of the existing work treats resource selection and workload allocation as an interrelated process [19,22,23,24,25,26,27,28,29,30,32,34,35,37,38,39,41,46]. In other words, the decision-making process must consider not only a number of required resources but also the allocation of the workload onto those resources.…”

Section: Literature Analysismentioning

confidence: 99%

A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

Thai

Varghese

Barker

2018

Future Generation Computer Systems

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Cloud computing has been widely adopted due to the flexibility in resource provisioning and on-demand pricing models. Entire clusters of Virtual Machines (VMs) can be dynamically provisioned to meet the computational demands of users. However, from a user's perspective, it is still challenging to utilise cloud resources efficiently. This is because an overwhelmingly wide variety of resource types with different prices and significant performance variations are available. This paper presents a survey and taxonomy of existing research in optimising the execution of Bag-of-Task applications on cloud resources. A BoT application consists of multiple independent tasks, each of which can be executed by a VM in any order; these applications are widely used by both the scientific communities and commercial organisations. The objectives of this survey are as follows: (i) to provide the reader with a concise understanding of existing research on optimising the execution of BoT applications on the cloud, (ii) to define a taxonomy that categorises current frameworks to compare and contrast them, and (iii) to present current trends and future research directions in the area.

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A Dynamic Hybrid Resource Provisioning Approach for Running Large-Scale Computational Applications on Cloud Spot and On-Demand Instances

Cited by 14 publications

References 25 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 Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds

A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

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