Predictive Models for Energy-Efficient Clouds: An Analysis on Real-Life and Synthetic Data

Altomare, Albino; Cesario, Eugenio

doi:10.1109/cit/iucc/dasc/picom.2015.231

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Decision tree is one of the supervised classification techniques, and it was used in [76] for energy-aware allocation of virtual machines. In this case, classification models have been learnt by separating the training set in 5 and 10 bins representing different ranges of CPU and RAM usages.…”

Section: Supervised and Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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Section: Supervised and Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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“…This section presents a preliminary evaluation of the regressive function, modeled by Equation 2, performed over synthetic data. In particular, we used an ad-hoc data generator [3] to produce PUE data series for three geographically distributed DCs, each one adhering to a predefined pattern (introducing a 5% noise). Then, the whole dataset (of each DC) has been split in training set and test set: the first one has been used to infer the regressive function, while the second one has been exploited as test bed to validate the approach.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Efficient workload management in geographically distributed data centers leveraging autoregressive models

Altomare

Cesario

Mastroianni

2016

AIP Conference Proceedings

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The opportunity of using Cloud resources on a pay-as-you-go basis and the availability of powerful data centers and high bandwidth connections are speeding up the success and popularity of Cloud systems, which is making on-demand computing a common practice for enterprises and scientific communities. The reasons for this success include natural business distribution, the need for high availability and disaster tolerance, the sheer size of their computational infrastructure, and/or the desire to provide uniform access times to the infrastructure from widely distributed client sites. Nevertheless, the expansion of large data centers is resulting in a huge rise of electrical power consumed by hardware facilities and cooling systems. The geographical distribution of data centers is becoming an opportunity: the variability of electricity prices, environmental conditions and client requests, both from site to site and with time, makes it possible to intelligently and dynamically (re)distribute the computational workload and achieve as diverse business goals as: the reduction of costs, energy consumption and carbon emissions, the satisfaction of performance constraints, the adherence to Service Level Agreement established with users, etc. This paper proposes an approach that helps to achieve the business goals established by the data center administrators. The workload distribution is driven by a fitness function, evaluated for each data center, which weighs some key parameters related to business objectives, among which, the price of electricity, the carbon emission rate, the balance of load among the data centers etc. For example, the energy costs can be reduced by using a "follow the moon" approach, e.g. by migrating the workload to data centers where the price of electricity is lower at that time. Our approach uses data about historical usage of the data centers and data about environmental conditions to predict, with the help of regressive models, the values of the parameters of the fitness function, and then to appropriately tune the weights assigned to the parameters in accordance to the business goals. Preliminary experimental results, presented in this paper, show encouraging benefits.

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Predictive Models for Energy-Efficient Clouds: An Analysis on Real-Life and Synthetic Data

Cited by 2 publications

References 11 publications

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

Efficient workload management in geographically distributed data centers leveraging autoregressive models

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