Optimizing a Cloud Contract Portfolio Using Genetic Programming-Based Load Models

Stijven, Sean; Bossche, Ruben Van den; Vladislavleva, Ekaterina; Vanmechelen, Kurt; Broeckhove, J.; Kotanchek, Mark

doi:10.1007/978-1-4939-0375-7_3

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…90 days) reserved contract procurement suggestions are made based on these predictions. The purchase suggestion algorithm in [6] only calculates the load distribution levels once, and is unable to take into account previous purchases, which differs from this contribution.…”

Section: Related Workmentioning

confidence: 93%

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IaaS reserved contract procurement optimisation with load prediction

Bossche

Vanmechelen

Broeckhove

2015

Future Generation Computer Systems

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Section: Related Workmentioning

confidence: 93%

“…Stijven et al [6] create load models from four web server traces using Genetic Programming. The load predictions span an interval of 90 days, and short-term (i.e.…”

Section: Related Workmentioning

confidence: 99%

IaaS reserved contract procurement optimisation with load prediction

Bossche

Vanmechelen

Broeckhove

2015

Future Generation Computer Systems

Self Cite

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“…90 days) reserved contract procurement suggestions are made based on these predictions. The purchase suggestion algorithm in [18] only calculates the load distribution levels once, and is unable to take into account previous purchases, which differs from this contribution.…”

Section: Related Workmentioning

confidence: 99%

“…Stijven et al [18] use Genetic Programming to generate load models from four web server traces. Their predictions span an interval of 90 days, and short-term (i.e.…”

Section: Related Workmentioning

confidence: 99%

Optimizing IaaS Reserved Contract Procurement Using Load Prediction

Bossche

Vanmechelen

Broeckhove

2014

2014 IEEE 7th International Conference on Cloud Computing

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With the increased adoption of cloud computing, new challenges have emerged related to the cost-effective management of cloud resources. The proliferation of resource properties and pricing plans has made the selection, procurement and management of cloud resources a time-consuming and complex task, which stands to benefit from automation. This contribution focuses on the procurement decision of reserved contracts in the context of Infrastructure-as-a-Service (IaaS) providers such as Amazon EC2. Such reserved contracts complement pay-by-the-hour pricing models, and offer a significant reduction in price (up to 70%) for a particular period in return for an upfront payment. Thus, customers can reduce costs by predicting and analyzing their future needs in terms of the number and type of server instances. We present an algorithm that uses load prediction with automated time series forecasting based on a Double-seasonal Holt-Winters model, in order to make cost-efficient purchasing decisions among a wide range of contract types while taking into account an organization's current contract portfolio. We analyze its cost effectiveness through simulation of real-world web traffic traces. Our analysis investigates the impact of different prediction techniques on cost compared to a clairvoyant predictor and compares the algorithm's performance with a stationary contract renewal approach. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved contract procurement. Moreover, the algorithm's computational cost makes it applicable to largescale real-world settings.

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“…e hybrid resource provisioning uses deterministic reserved resources to deal with long-term stable workload and uses dynamic on-demand resources to deal with short-term sudden workload. Stijven et al [39] proposed a scheme to plan reserved resources based on short-term workload prediction but only one kind of contract could be used. Candeia et al [15] designed the algorithms to select IaaS reservation markets and determine the numbers of instances as well as their lifespans, without considering multiple kinds of contracts simultaneously.…”

Section: Dynamic Resource Provisioningmentioning

confidence: 99%

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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Optimizing a Cloud Contract Portfolio Using Genetic Programming-Based Load Models

Cited by 4 publications

References 15 publications

IaaS reserved contract procurement optimisation with load prediction

IaaS reserved contract procurement optimisation with load prediction

Optimizing IaaS Reserved Contract Procurement Using Load Prediction

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

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