A Prediction Approach to Define Checkpoint Intervals in Spot Instances

Neto, José Pergentino Araújo; Pianto, Donald M.; Ralha, Célia Ghedini

doi:10.1007/978-3-319-94295-7_6

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…In this case, if the distance between x[axis] and node[axis] is less than the maximum distance between k nearest nodes set and X , we should search neatest node in node 's right subtree (line [17][18][19]. This is a similar case when x[axis] is more than node[axis] (line [21][22][23][24][25][26][27]. Finally, this algorithm will return the set of k nearest nodes.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Neto et al [26] proposed a heuristic model that uses checkpoint and restore techniques, and takes price change traces of spot instances as input in a machine learning and statistical model to predict time to revocation. By using a bid strategy and the observed price variation history, their model can be able to predict revocation time with high levels of accuracy.…”

Section: Price Prediction Models Based On Machine Learningmentioning

confidence: 99%

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Cloud spot instance price prediction using kNN regression

Liu

Wang

Meng

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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With the amount of data rapidly increasing, many applications need higher-performance hardware to support their running. However, for most individuals and organizations, these hardware are too expensive, and their budgets are limited. Consequently, the cloud computing price scheme based on pay-as-you-go is a very suitable choice for users to migrate their applications to the cloud, and using cloud can reduce the cost of purchasing and maintaining hardware. There are three major service models for cloud computing: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) [1]. Amazon Elastic Compute Cloud (EC2) [2] is one of the representatives of IaaS, which can provide users with basic hardware resources, such as CPU, network, memory and storage. Amazon EC2 provides users with many purchase options. They can be divided into three categories: reserved instances, on-demand instances and spot instances [2]. Reserved instances allow users to purchase a long-term right to use instances at a lower price, which are cost-effective for long-term applications. On-demand instances cost more than reserved instances, however users can purchase it according to the actual

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Price Prediction Models Based On Machine Learningmentioning

confidence: 99%

Cloud spot instance price prediction using kNN regression

Liu

Wang

Meng

et al. 2020

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…It is calculated based on two factors, that is, recall and precision. Neto et al 137 proposed an approach using spot instances to address the problem of executing a distributed application like bag‐of‐tasks. Authors in Reference 138 developed a cloud architecture to tolerate failure before or after its occurrence efficiently.…”

Section: Taxonomy Of Fault Tolerance Approachesmentioning

confidence: 99%

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

Kirti,

Maurya,

Yadav

2024

Concurrency and Computation

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Fault tolerance is crucial in ensuring smooth working of distributed and cloud computing. It is challenging to implement because of the constantly changing infrastructure and complex configurations in distributed and cloud computing. Implementation of various fault tolerance methods require domain‐specific knowledge as well as in‐depth understanding of the existing techniques and approaches. Recent surveys on fault tolerance in cloud and distributed environments exist, but they have limitations. This article systematically reviews fault tolerance approaches in distributed and cloud computing and discusses their taxonomy. Based on the taxonomy provided, fault‐tolerance approaches are divided into four types, that is, reactive approaches, proactive approaches, adaptive approaches, and hybrid approaches. Reactive approaches provide a preventive measure after the occurrence of faults in the system. Proactive approaches prevent the system or minimize failure effects by predicting in advance. The adaptive approaches predict, learn, and adapt the changes to deal with new faults in the system. The hybrid approaches combine reactive, proactive, and adaptive approaches. The objective of this article is to give a better understanding of handling faults using suitable approaches and further compare them on various parameters. The paper also presents a promising research direction based on the challenges and issues in multiple approaches.

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“…Balances the best overfitting and underfitting hyperparameter. [2,3,4] The number of hidden layers 64 [ 8,16,32,64,128] The number of neurons in each layer Adam [Adam, SGD, RMSprop] Optimization algorithm…”

Section: Generating a List Containing Combinations Of Hyperparametersmentioning

confidence: 99%

“…Infrastructure as a Service (IaaS) is a unique service model that sits at the very bottom of the cloud computing stack, near the hardware. IaaS provides users with virtual machine-based infrastructure resources, allowing them to elastically lease VMs with various capacities and functionalities [4].…”

Section: Introductionmentioning

confidence: 99%

Optimal prediction of cloud spot instance price utilizing deep learning

2022

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Cloud platforms frequently provide a variety of virtual machine models (VMs) with varying types and capacities, allowing users to select instances that best suit their needs. Cloud providers have devised a system for maximizing the utilization of redundant computing resources, whose costs fluctuate dynamically depending on supply and demand. "Spot pricing" is a common term for this, the user must make a suitable offer that is higher than the spot price to use this instance. Accurate spot price prediction enables users to prepare ahead of time for the bid price and run time to increase the method's reliability. To solve this critical challenge of predicting future pricing, we utilize a consistent and robust deep learning model. When compared to other sophisticated methods, the test results reveal that our proposed method performs better and more accurately.

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A Prediction Approach to Define Checkpoint Intervals in Spot Instances

Cited by 7 publications

References 19 publications

Cloud spot instance price prediction using kNN regression

Cloud spot instance price prediction using kNN regression

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

Optimal prediction of cloud spot instance price utilizing deep learning

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