Power consumption model based on feature selection and deep learning in cloud computing scenarios

Yang, Liang; Hu, Zhigang; Li, Keqin

doi:10.1049/iet-com.2019.0717

Cited by 22 publications

(11 citation statements)

References 39 publications

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“…Initially, the power models of data centers were static. 13,14 However, these models are not practical for power management such as workflow scheduling and VM migrating, 15,16 because they cannot capture power fluctuation in the runtime.…”

Section: Power Consumption Predictionmentioning

confidence: 99%

Data characteristics aware prediction model for power consumption of data center servers

Shen

Zhou

Zhang

et al. 2022

Concurrency and Computation

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Summary Due to the rapid increase in the number and scale of data centers, the information and communication technology (ICT) equipment in data centers consumes an enormous amount of power. A power prediction model is therefore essential for decision‐making optimization and power management of ICT equipment. However, it is difficult to predict the power consumption of data centers accurately due to the complex power patterns and nonlinear interdependencies among components. Existing methods either rely on standard formulas, or simply treat it as time series, both leading to poor power prediction accuracy. To overcome those limitations, in this article, we present a systematic power prediction framework called characteristic aware attention‐augmented deep learning‐based prediction method. In particular, we first analyze the different power consumption series to illustrate their different temporal characteristics. Second, we perform different data processing for the corresponding characteristics of power series samples. Third, we propose an accurate and efficient neural network model to predict future power consumption with the pretreated data. The experimental results show that the proposed model is able to achieve superior prediction accuracy.

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Section: Power Consumption Predictionmentioning

confidence: 99%

Data characteristics aware prediction model for power consumption of data center servers

Shen

Zhou

Zhang

et al. 2022

Concurrency and Computation

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“…42 Like Chaoqiang et al we believe that to build energy or power consumption models, linear regression or polynomial regressions are apt. 43 Energy-aware scheduling 44 and power consumption model of cloud data centers based on feature selection, deep learning, 45 and ANN 46 is quite rigorous in the field of power consumption modeling of VMs.…”

Section: Modeling Resource Utilization For Virtual Machinesmentioning

confidence: 99%

Performance modeling for I/O‐intensive applications on virtual machines

Bhattacharya

Peng

Mao

et al. 2022

Concurrency and Computation

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Models for virtual machines running on cloud computing systems. Modeling system behaviors of clouds is a grand challenge because the resource utilization in VMs is heterogeneous due to variability in workload conditions. We address this challenging issue by uniquely (1) objectifying the usage prediction of virtualized resources and(2) predicting the performance trends of programs running on clouds. At the heart of the modeling system, we pay particular attention to CPU cores, disk size, main memory space, and input data volume, which serve as important factors for the developed prediction module. We devise two resource-utilization prediction algorithms driven by two distinctive sets of I/O and CPU intensive benchmarks, where one algorithm deals with execution time and the other one revolves around input data size. We investigate the correlation between CPU/disk utilization and VM live migrations. Our system aims at not only providing performance optimization for virtualized resources but also ensuring service level agreement (SLA) and Quality of Service (QoS). The model fits the curve quite well, thereby advocating for the efficiency of the algorithm. The case studies conducted in this project draw the comparisons between the performance of striped and monolithic disks as well as bringing forth the problem of cache coherence that causes hindrance to the experiment. We also deal with the cache-coherence problem to improve the accuracy of our prediction algorithms K E Y W O R D S cache coherence, I/O intensive, linear model, monolithic and split disk, resource utilization INTRODUCTIONThis article is motivated by the rapid growth in demand for computation resources in data centers and the widespread of cloud computing. Modeling system behaviors of clouds is a grand challenge because the resource utilization in VMs is heterogeneous due to variability in workload conditions.We address this challenging issue by uniquely (1) objectifying the usage prediction of virtualized resources and (2) predicting the performance trends of programs running on clouds.This research is inspired by the following four motivations.Motivation 1: virtualization. Cloud computing has changed the landscape of computing resources delivery models. Large-scale virtualized data centers gain popularity thanks to the rapid growth in demand for computational power-driven by modern service applications combined with the shift to the cloud computing model. 1 The virtualization technique emulates physical computers with a mirrored operating system, virtual disks, virtual CPUs, and virtual memory resources. The emergence of virtual machines promises to streamline the on-demand provisioning of software,

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“…This category includes several energy models applied on CDCs such as [26]- [29]. An energy-aware algorithm is closely related to the energy consumption model, and any energy-saving algorithm is dependent on a certain power model.…”

Section: Energy Consumption Model On Cdcmentioning

confidence: 99%

“…The power model's accuracy directly reflects the pros and cons of an energy-saving algorithm. Precisely, in [26], the authors design a novel energy consumption model by leveraging the deep learning technology. Their model takes 12 energy-related features into account and leverages deep neural network architecture to build an energy consumption model.…”

Section: Energy Consumption Model On Cdcmentioning

confidence: 99%

AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

Zhou

Shojafar

Alazab

et al. 2021

IEEE Trans. on Green Commun. Netw.

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Cloud Data Centers (CDCs) have become a vital computing infrastructure for enterprises. However, CDCs consume substantial energy due to the increased demand for computing power, especially for the Internet of Things (IoT) applications. Although a great deal of research in green resource allocation algorithms have been proposed to reduce the energy consumption of the CDCs, existing approaches mostly focus on minimizing the number of active Physical Machines (PMs) and rarely address the issue of load fluctuation and energy efficiency of the Virtual Machine (VM) provisions jointly. Moreover, existing approaches lack mechanisms to consider and redirect the incoming traffics to appropriate resources to optimize the Quality of Services (QoSs) provided by the CDCs. We propose a novel adaptive energy-aware VM allocation and deployment mechanism called AFED-EF for IoT applications to handle these problems. The proposed algorithm can efficiently handle the fluctuation of load and has good performance during the VM allocation and placement. We carried out extensive experimental analysis using a real-world workload based on more than a thousand PlanetLab VMs. The experimental results illustrate that AFED-EF outperforms other energy-aware algorithms in energy consumption, Service Level Agreements (SLA) violation, and energy efficiency.

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Power consumption model based on feature selection and deep learning in cloud computing scenarios

Cited by 22 publications

References 39 publications

Data characteristics aware prediction model for power consumption of data center servers

Data characteristics aware prediction model for power consumption of data center servers

Performance modeling for I/O‐intensive applications on virtual machines

AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

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