Multivariate Time Series ELM for Cloud Data Centre Workload Prediction

Ismaeel, Salam; Miri, Ali

doi:10.1007/978-3-319-39510-4_52

Cited by 16 publications

(18 citation statements)

References 15 publications

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“…All these approaches possess a prediction module in common which provides estimations to determine respectively whether or not to add more resources, rearrange the order of query execution, and admit or reject a new incoming query [89]. Prediction of the future resource behavior is a crucial process for efficient resource utilization in dynamic cloud computing environment because workload forecasting for short or long periods will be necessary to real-time control, resource allocation, capacity planning and data centre energy saving in cloud computing [90].…”

Section: Workload Prediction Subsystemmentioning

confidence: 99%

“…Thus, this review subsystem will not cover the most practical and recent published prediction algorithms but will include clustering literature, most useful prediction window size recommendation, and even literature discussed VM and user behaviors. We classify workload prediction subsystem into four functional areas, each will review publications in a separate subsection, namely [86,90]:…”

Section: Workload Prediction Subsystemmentioning

confidence: 99%

“…General clustering system used in VM clustering with user behaviors proposed by our previous work [86,90]. General clustering system consists of the following components (see Fig.…”

Section: Clustering Processmentioning

confidence: 99%

“…The virtualization ability not only enables service providers to charge their clients based on their usage in a pay as-you-go scheme, but also it provides clients the ability to scale up or scale down resource utilization, as their needs vary. These advantageous partially stem from the fact that virtualization technology enables multiple virtual servers to run on the same PM, resulting in better resource utilization and reduction of aggregate power consumption [68,89,90,102].…”

Section: Introductionmentioning

confidence: 99%

“…The change may be made because of the performance, maintenance, power or load issues, or SLA violations. In proactive, monitoring, historical data and prediction algorithms are used to change the VM's initial placement before the system reaches a certain condition [90,121]. Proactive provisioning uses prediction based approaches that help prepare ahead of changes in the workload and system usage [158].…”

Section: Introductionmentioning

confidence: 99%

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Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

2018

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Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.

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Section: Workload Prediction Subsystemmentioning

confidence: 99%

Section: Workload Prediction Subsystemmentioning

confidence: 99%

“…General clustering system used in VM clustering with user behaviors proposed by our previous work [86,90]. General clustering system consists of the following components (see Fig.…”

Section: Clustering Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

2018

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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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Cloud Management: An Autoencoder-Based Clustering of Virtual Machines for Data Centre Workload

Sathiya

Prakash

2021

Advances in Automation, Signal Processing, Instrumentation, and Control

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Multivariate Time Series ELM for Cloud Data Centre Workload Prediction

Cited by 16 publications

References 15 publications

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

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

Cloud Management: An Autoencoder-Based Clustering of Virtual Machines for Data Centre Workload

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