Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres
Salam Ismaeel,Raed Karim,Ali Miri +2 more
- Vol. 7, Iss: 1, pp 10
TLDR
This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption and presents a general framework that can be used on multiple phases of a complete consolidation process.Abstract:
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.read more
Citations
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Energy-aware VM placement algorithms for the OpenStack Neat consolidation framework
TL;DR: This work proposes VM placement algorithms based on both bin-packing heuristics and servers’ power efficiency and introduces a new bin- packing heuristic called a Medium-Fit (MF) to reduce SLA violation.
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A Neuro-fuzzy approach for user behaviour classification and prediction
Atta-ur-Rahman,Sujata Dash,Ashish Kumar Luhach,Naveen Chilamkurti,Seungmin Baek,Yunyoung Nam +5 more
TL;DR: A neuro-fuzzy approach for the classification and prediction of user behaviour is proposed and the scheme is found to be promising in terms of classification as well as prediction accuracy.
Journal ArticleDOI
A survey of data center consolidation in cloud computing systems
Leila Helali,Mohamed Nazih Omri +1 more
TL;DR: In this article, the authors present an overview of virtualized data centers and consolidation solutions from the literature and present a brief thematic taxonomy and an illustration of some consolidation solutions.
Journal ArticleDOI
Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers
Seyedhamid Mashhadi Moghaddam,Michael O'Sullivan,Cameron Walker,Sareh Fotuhi Piraghaj,Charles P. Unsworth +4 more
TL;DR: This paper proposes an energy aware VM consolidation algorithm that minimizes SLAVs and develops different fine-tuned Machine Learning prediction models for individual VMs to predict the best time to trigger migrations from hosts.
Journal ArticleDOI
Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters
TL;DR: This work introduces a multi-objective approach to compute optimal placement strategies considering different goals, such as the impact of hardware outages, the power required by the datacenter, and the performance perceived by users.
References
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VM consolidation approach based on heuristics, fuzzy logic, and migration control
TL;DR: This research explores fuzzy logic and heuristic based virtual machine consolidation approach to achieve energy-QoS balance and proposes a Fuzzy VM selection method that is most energy efficient compared to others.
Proceedings ArticleDOI
A Hybrid Approach to Live Migration of Virtual Machines
Shashank Sahni,Vasudeva Varma +1 more
TL;DR: A hybrid approach of live migrating a virtual machine across hosts in a Gigabit LAN that takes the best of both the traditional methods of live migration - pre and post-copy and a prototype design on KVM/Qemu is proposed.
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Proceedings ArticleDOI
A Time-Series Based Precopy Approach for Live Migration of Virtual Machines
TL;DR: Compared with the traditional approach of Xen, the proposed improved time-series based precopy approach can effectively improve the performance of virtual machine migration, including less number of iterations, less down time and migration time, and fewer pages transferred.
Proceedings ArticleDOI
Energy Aware Consolidation Algorithm Based on K-Nearest Neighbor Regression for Cloud Data Centers
TL;DR: Experimental results on the real workload traces from more than a thousand Planet Lab virtual machines show that the proposed technique minimizes energy consumption and maintains required performance levels.
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