A Markov-based Prediction Model for Host Load Detection in Live VM Migration

被引：21
|
作者：
Melhem, Suhib Bani [1 ]
Agarwal, Anjali [1 ]
Goel, Nishith [2 ]
Zaman, Marzia [2 ]
机构：
[1] Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ, Canada
[2] Cistech Ltd, Ottawa, ON, Canada
来源：
2017 IEEE 5TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD (FICLOUD 2017) | 2017年
基金：
加拿大自然科学与工程研究理事会;
关键词：
Live Migration; Host underload/overload detection; Virtual machine consolidation; Data center; CLOUD DATA CENTERS; VIRTUAL MACHINES; CONSOLIDATION; ALGORITHMS; ENERGY; PERFORMANCE; HEURISTICS;
D O I：
10.1109/FiCloud.2017.37
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Host load detection algorithm determines if a given host is overload or underloaded then the decision can be made to migrate VMs to achieve host/server consolidation and load balancing in cloud data centers while satisfying the QoS constraints. Presently, host load detection is a challenging problem in the cloud data center management specially with high dynamic environment for the host load. In this paper, we propose a novel Markov-based prediction algorithm to forecast the future load state of the host. The experimental results demonstrate that the proposed algorithm has better performance than the other competitive algorithms. The results for different types of PlanetLab real and random workloads show significant reduction of the SLA violation and the number of VM migrations.
引用
收藏
页码：32 / 38
页数：7
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