Virtual Machine Learning: Thinking like a Computer Architect

International Symposium on Code Generation and Optimization ◽

10.1109/cgo.2005.37 ◽

2005 ◽

Author(s):

M. Hind

Keyword(s):

Machine Learning ◽

Virtual Machine

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A power and thermal-aware virtual machine management framework based on machine learning

Cluster Computing ◽

10.1007/s10586-020-03228-6 ◽

2021 ◽

Author(s):

Peng Xiao ◽

Zhenyu Ni ◽

Dongbo Liu ◽

Zhigang Hu

Keyword(s):

Machine Learning ◽

Virtual Machine ◽

Management Framework

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Machine-Learning-Based Malware Detection for Virtual Machine by Analyzing Opcode Sequence

Advances in Brain Inspired Cognitive Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-030-00563-4_70 ◽

2018 ◽

pp. 717-726

Author(s):

Xiao Wang ◽

Jianbiao Zhang ◽

Ai Zhang

Keyword(s):

Machine Learning ◽

Virtual Machine ◽

Malware Detection ◽

Opcode Sequence

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A multi-objective load balancing algorithm for virtual machine placement in cloud data centers based on machine learning

Computing ◽

10.1007/s00607-020-00813-w ◽

2020 ◽

Vol 102 (9) ◽

pp. 2049-2072 ◽

Cited By ~ 1

Author(s):

Arezoo Ghasemi ◽

Abolfazl Toroghi Haghighat

Keyword(s):

Machine Learning ◽

Load Balancing ◽

Virtual Machine ◽

Data Centers ◽

Virtual Machine Placement ◽

Cloud Data ◽

Multi Objective ◽

Cloud Data Centers ◽

Load Balancing Algorithm

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Virtual machine scheduling strategy based on machine learning algorithms for load balancing

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1454-9 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 2

Author(s):

Xin Sui ◽

Dan Liu ◽

Li Li ◽

Huan Wang ◽

Hongwei Yang

Keyword(s):

Machine Learning ◽

Load Balancing ◽

Virtual Machine ◽

Learning Algorithms ◽

Machine Scheduling ◽

Machine Learning Algorithms ◽

Scheduling Strategy ◽

Virtual Machine Scheduling

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Performance Analysis of Java Virtual Machine for Machine Learning Workloads using Apache Spark

Proceedings of the International Conference on Informatics and Analytics - ICIA-16 ◽

10.1145/2980258.2982117 ◽

2016 ◽

Author(s):

N. Hema ◽

K. G. Srinivasa ◽

Saravanan Chidambaram ◽

Sandeep Saraswat ◽

Sujoy Saraswati ◽

...

Keyword(s):

Machine Learning ◽

Performance Analysis ◽

Virtual Machine ◽

Apache Spark ◽

Java Virtual Machine

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An Efficient Machine Learning Approach for Virtual Machine Resource Demand Prediction

International Journal of Advanced Science and Technology ◽

10.33832/ijast.2019.123.03 ◽

2019 ◽

Vol 123 ◽

pp. 21-30 ◽

Cited By ~ 5

Author(s):

Jitendra Kumar ◽

Ashutosh Kumar Singh

Keyword(s):

Machine Learning ◽

Virtual Machine ◽

Learning Approach ◽

Resource Demand ◽

Demand Prediction ◽

Machine Learning Approach ◽

Efficient Machine

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Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/tcad.2017.2760517 ◽

2018 ◽

Vol 37 (8) ◽

pp. 1667-1680 ◽

Cited By ~ 6

Author(s):

Ali Pahlevan ◽

Xiaoyu Qu ◽

Marina Zapater ◽

David Atienza

Keyword(s):

Machine Learning ◽

Virtual Machine ◽

Data Centers ◽

Learning Methods ◽

Machine Learning Methods ◽

Machine Allocation ◽

Virtual Machine Allocation

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Machine learning based optimized live virtual machine migration over WAN links

Telecommunication Systems ◽

10.1007/s11235-016-0173-3 ◽

2016 ◽

Vol 64 (2) ◽

pp. 245-257 ◽

Cited By ~ 6

Author(s):

Moiz Arif ◽

Adnan K. Kiani ◽

Junaid Qadir

Keyword(s):

Machine Learning ◽

Virtual Machine ◽

Virtual Machine Migration

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The Practicality of using Virtual Machine Introspection Technique with Machine Learning Algorithms for the Detection of Intrusions in Cloud

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India ◽

10.4108/eai.16-5-2020.2303939 ◽

2021 ◽

Author(s):

Alfred Melvin ◽

G Jaspher Kathrine ◽

J Immanuel Johnraja

Keyword(s):

Machine Learning ◽

Virtual Machine ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Virtual Machine Introspection

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SLA Management For Virtual Machine Live Migration Using Machine Learning with Modified Kernel and Statistical Approach

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.1692 ◽

2018 ◽

Vol 8 (1) ◽

pp. 2459-2463 ◽

Cited By ~ 1

Author(s):

M. K. Hassan ◽

A. Babiker ◽

M. Baker ◽

M. Hamad

Keyword(s):

Machine Learning ◽

Cloud Computing ◽

Virtual Machine ◽

Service Level ◽

Live Migration ◽

Core Technology ◽

Acceptable Quality ◽

Resource Requirements ◽

Current Resource ◽

Virtual Machine Live Migration

Application of cloud computing is rising substantially due to its capability to deliver scalable computational power. System attempts to allocate a maximum number of resources in a manner that ensures that all the service level agreements (SLAs) are maintained. Virtualization is considered as a core technology of cloud computing. Virtual machine (VM) instances allow cloud providers to utilize datacenter resources more efficiently. Moreover, by using dynamic VM consolidation using live migration, VMs can be placed according to their current resource requirements on the minimal number of physical nodes and consequently maintaining SLAs. Accordingly, non optimized and inefficient VMs consolidation may lead to performance degradation. Therefore, to ensure acceptable quality of service (QoS) and SLA, a machine learning technique with modified kernel for VMs live migrations based on adaptive prediction of utilization thresholds is presented. The efficiency of the proposed technique is validated with different workload patterns from Planet Lab servers.

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