A Survey of Machine Learning Applications for Energy-Efficient Resource Management in Cloud Computing Environments

Demirci, Mehmet

doi:10.1109/icmla.2015.205

Cited by 49 publications

(32 citation statements)

References 34 publications

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“…This proactive approach outperforms the static modeling approach in terms of adaptability and flexibility, but it may incur high computational overheads, affecting overall system performance. In addition to demand prediction, machine learning techniques can also be applied to schedule virtual resources and workloads (Demirci, ). Specifically, reinforcement learning, with its capabilities in making sequential decisions under uncertainty, has been applied successfully in cloud resource allocation and scaling (Barrett et al, ; Xu, Rao, & Bu, ; Jamshidi, Sharifloo, Pahl, Metzger, & Estrada, ).…”

Section: Related Workmentioning

confidence: 99%

Automatic cloud resource management for interactive remote geovisualization

2018

Transactions in GIS

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Section: Related Workmentioning

confidence: 99%

Automatic cloud resource management for interactive remote geovisualization

2018

Transactions in GIS

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“…To appreciate the interest in this hot topic relating to ML applications for energy efficient management in cloud computing environment, one may refer to the survey papers by Demirci [5], Tantar [6] and Zhan [9]. Evolutionary NN as a ML approach to modeling and predicting non-linear dynamic system such as the cloud data center is a powerful and promising approach.…”

Section: Evolutionary Neural Networkmentioning

confidence: 99%

Machine Learning with Sensitivity Analysis to Determine Key Factors Contributing to Energy Consumption in Cloud Data Centers

Foo

Goh

2016

2016 International Conference on Cloud Computing Research and Innovations (ICCCRI)

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Abstract-Machine learning (ML) approach to modeling and predicting real-world dynamic system behaviours has received widespread research interest. While ML capability in approximating any nonlinear or complex system is promising, it is often a black-box approach, which lacks the physical meanings of the actual system structure and its parameters, as well as their impacts on the system. This paper establishes a model to provide explanation on how system parameters affect its output(s), as such knowledge would lead to potential useful, interesting and novel information. The paper builds on our previous work in ML, and also combines an evolutionary artificial neural networks with sensitivity analysis to extract and validate key factors affecting the cloud data center energy performance. This provides an opportunity for software analysts to design and develop energyaware applications and for Hadoop administrator to optimize the Hadoop infrastructure by having Big Data partitioned in bigger chunks and shortening the time to complete MapReduce jobs.

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“…The use of machine learning techniques to solve networking challenges has been extensively studied. The network optimisation aspects of the traditional network management, like traffic classification [26] and energy efficient resource management [27], as well as the dynamic resource management [28], have been thoroughly investigated. A traffic engineering framework with machine learning was also studied in SDN [29].…”

Section: Related Workmentioning

confidence: 99%

“…and reinforcement learning (Markov decision processes etc.) [26], [27], [29]. Overall, the research community highlighted that a broad range of networking challenges can be met by employing a wide range of machine learning and data mining techniques.…”

Section: Related Workmentioning

confidence: 99%

Seer: Empowering Software Defined Networking with Data Analytics

Sideris¹,

Nejabati²,

Simeonidou³

2016

2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace An

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Network complexity is increasing, making network control and orchestration a challenging task. The proliferation of network information and tools for data analytics can provide an important insight into resource provisioning and optimisation. The network knowledge incorporated in software defined networking can facilitate the knowledge driven control, leveraging the network programmability. We present Seer: a flexible, highly configurable data analytics platform for network intelligence based on software defined networking and big data principles. Seer combines a computational engine with a distributed messaging system to provide a scalable, fault tolerant and real-time platform for knowledge extraction. Our first prototype uses Apache Spark for streaming analytics and open network operating system (ONOS) controller to program a network in real-time. The first application we developed aims to predict the mobility pattern of mobile devices inside a smart city environment.

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A Survey of Machine Learning Applications for Energy-Efficient Resource Management in Cloud Computing Environments

Cited by 49 publications

References 34 publications

Automatic cloud resource management for interactive remote geovisualization

Automatic cloud resource management for interactive remote geovisualization

Machine Learning with Sensitivity Analysis to Determine Key Factors Contributing to Energy Consumption in Cloud Data Centers

Seer: Empowering Software Defined Networking with Data Analytics

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