VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing

Śnieżyński, Bartłomiej; Nawrocki, Piotr; Wilk, Michał; Jarzab, Marcin; Zieliński, Krzysztof

doi:10.1007/s10723-019-09487-x

Cited by 29 publications

(14 citation statements)

References 25 publications

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“…Very few works incorporate anomaly detection mechanisms into resource usage prediction. An example is [25], in which the authors propose a novel method used to build a long-term virtual machine reservation plan in cloud computing using machine learning algorithms. The solution suggested enables autonomous plan adaptation and verification through analyzing data on system usage.…”

Section: Usage Prediction With Anomaly Detectionmentioning

confidence: 99%

Anomaly detection in the context of long-term cloud resource usage planning

Nawrocki

Sus

2022

Knowl Inf Syst

Self Cite

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This paper describes a new approach to automatic long-term cloud resource usage planning with a novel hybrid anomaly detection mechanism. It analyzes existing anomaly detection solutions, possible improvements and the impact on the accuracy of resource usage planning. The proposed anomaly detection solution is an important part of the research, since it allows greater accuracy to be achieved in the long term. The proposed approach dynamically adjusts reservation plans in order to reduce the unnecessary load on resources and prevent the cloud from running out of them. The predictions are based on cloud analysis conducted using machine learning algorithms, which made it possible to reduce costs by about 50%. The solution was evaluated on real-life data from over 1700 virtual machines.

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Section: Usage Prediction With Anomaly Detectionmentioning

confidence: 99%

Anomaly detection in the context of long-term cloud resource usage planning

Nawrocki

Sus

2022

Knowl Inf Syst

Self Cite

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“…Antonescu et al [13] dynamically adjusted resources to meet predicted shortterm workload so as to minimize the cost, while avoiding the service level agreement (SLA) violations. Sniezynski et al [14] used linear regression, neural networks, etc., to learn resource usage patterns from the historical records so as to predict and update resource capacity periodically.…”

Section: Dynamic Resource Provisioningmentioning

confidence: 99%

“…Estimations. Some studies directly used the historical cycle's workloads as an estimation of the current cycle's workloads [14,15,17]. Similarly, we used the workloads from 2014 to 2017 as the estimations of the workloads in the following years, namely, 2015 to 2018, respectively.…”

Section: Historical-workload-basedmentioning

confidence: 99%

“…In these studies, the uncertain nature of the user's demands is neglected by assuming the demand as a deterministic value. To address the demand uncertainties, in [11][12][13][14], some dynamic resource provisioning schemes are proposed. ese schemes are more flexible and provision resources dynamically to meet fluctuating workload.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, planning resource for the future business cycle of a web application requires an estimation/prediction of the future workloads. Some studies have employed the simulated workloads [6,9,11]; meanwhile, some studies directly take the workloads in the historical cycle as an estimation of the workloads in the future cycle [14,15,17]. But, the two approaches generally could not obtain a good accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing the Procurement of IaaS Reservation Contracts via Workload Predicting and Integer Programming

Zhu

Luo

Deng

2020

Mathematical Problems in Engineering

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Cloud-based web applications are proliferating fast. Owing to the elastic capacity and diverse pricing schemes, cloud Infrastructure-as-a-Service (IaaS) offers great opportunity for web application providers to optimize resource cost. However, such optimization activities are confronting the challenges posed by the uncertainty of future demand and the increasing reservation contracts. This work investigates the problem of how to minimize IaaS rental cost associated with hosting web applications, while meeting the demand in the future business cycle. First, an integer liner program model is developed to optimize reservation-contract procurement, in which reserved and on-demand resources are planned for multiple provisioning stages as well as a long-term plan, e.g., twelve stages in an annual plan. Then, a Long Short-Term Memory (LSTM) based algorithm is designed to predict the workload in the future business cycle. In addition, the approaches for determining virtual instance capacity and the baseline workload of planning time slot are also presented. Finally, the experimental prediction results show the LSTM-based algorithm gains an advantage over several popular models, such as the Holter–Winters, the Seasonal Autoregressive Integrated Moving Average (SARIMA), and the Support Vector Regression (SVR). The simulations of resource planning show that the provisioning scheme based on our reservation-optimization model obtains significant cost savings than other typical provisioning schemes, while satisfying the demands.

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A hierarchical decentralized architecture to enable adaptive scalable virtual machine migration

Hummaida

Paton

Sakellariou

2022

Concurrency and Computation

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Cloud computing is an established paradigm for end users to access resources. Cloud infrastructure providers seek to maximize accepted requests, meet Service Level Agreements (SLAs), and reduce operational costs by dynamically allocating Virtual Machines (VMs) to physical nodes. Many solutions have been presented to manage cloud infrastructure, however, these tend to be centralized and suffer in their ability to maintain Quality of Service (QOS) and support data centers with thousands of nodes.Decentralized approaches, with no central management, can manage large data centers. However, these tend to reduce the ability to obtain an optimal resource allocation across the data center. To address this, we propose a hybrid hierarchical decentralized architecture that achieves lower SLA violations and lowers network traffic. We used simulation to evaluate our proposal in practice with a variety of existing VM placement policies.

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VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing

Cited by 29 publications

References 25 publications

Anomaly detection in the context of long-term cloud resource usage planning

Anomaly detection in the context of long-term cloud resource usage planning

Optimizing the Procurement of IaaS Reservation Contracts via Workload Predicting and Integer Programming

A hierarchical decentralized architecture to enable adaptive scalable virtual machine migration

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