Markov Prediction Model for Host Load Detection and VM Placement in Live Migration

Komputasi awan atau cloud computing merupakan lingkungan yang heterogen dan terdistribusi, tersusun atas gugusan jaringan server dengan berbagai kapasitas sumber daya komputasi yang berbeda-beda guna menopang model layanan yang ada di atasnya. Virtual machine (VM) dijadikan sebagai representasi dari ketersediaan sumber daya komputasi dinamis yang dapat dialokasikan dan direalokasikan sesuai dengan permintaan. Mekanisme live migration VM di antara server fisik yang terdapat di dalam data center cloud digunakan untuk mencapai konsolidasi dan memaksimalkan utilisasi VM. Pada prosedur konsoidasi vm, pemilihan dan penempatan VM sering kali menggunakan kriteria tunggal dan statis. Dalam penelitian ini diusulkan pemilihan dan penempatan VM menggunakan multi-criteria decision making (MCDM) pada prosedur konsolidasi VM dinamis di lingkungan cloud data center guna meningkatkan layanan cloud computing. Pendekatan praktis digunakan dalam mengembangkan lingkungan cloud computing berbasis OpenStack Cloud dengan mengintegrasikan VM selection dan VM Placement pada prosedur konsolidasi VM menggunakan OpenStack-Neat. Hasil penelitian menunjukkan bahwa metode pemilihan dan penempatan VM melalui live migration mampu menggantikan kerugian yang disebabkan oleh down-times sebesar 11,994 detik dari waktu responnya. Peningkatan response times terjadi sebesar 6 ms ketika terjadi proses live migration VM dari host asal ke host tujuan. Response times rata-rata setiap vm yang tersebar pada compute node setelah terjadi proses live migration sebesar 67 ms yang menunjukkan keseimbangan beban pada sistem cloud computing.

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A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing

International Journal of Green Computing ◽

10.4018/ijgc.2017070102 ◽

2017 ◽

Vol 8 (2) ◽

pp. 20-36

Author(s):

Yu Cai

Keyword(s):

Energy Efficiency ◽

Energy Efficient ◽

Virtual Machines ◽

Live Migration ◽

Vm Placement ◽

Server Cluster ◽

Simulated Environment ◽

Resource Requirements ◽

Placement Algorithm

Energy efficient virtual machines (VM) management and distribution on cloud platforms is an important research subject. Mapping VMs into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It should also take into accounts of VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. In this article, the authors propose an energy efficient statistical live VM placement scheme in a heterogeneous server cluster. Their scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption and is ready to be extended with more considerations on user demands. The authors conducted extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of the proposed scheme.

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VM placement with effective energy management in cloud using optimal VM allocation framework (OVAF)

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i3.pp1531-1538 ◽

2020 ◽

Vol 18 (3) ◽

pp. 1531

Author(s):

Susmita J A Nair ◽

T R Gopalakrishnan Nair

Keyword(s):

Energy Management ◽

Data Centers ◽

Live Migration ◽

Vm Placement ◽

Utilization Factor ◽

Performance Loss ◽

Service Interruption ◽

Vm Allocation ◽

Novel Approach ◽

Selection Of

<p class="CAbstract"><span>The performance of servers at the data centers is affected when the servers are overloaded. To overcome this problem, the workload at the overloaded servers has to be redistributed to other servers which is possible with live VM migration. Live migration plays a crucial role in handling the overload at the data centers without service interruption. However, live migration also incurs some performance loss and energy overhead. The energy consumption at the data centers is a matter of utmost concern both in terms of economy and ecology. In this paper we are proposing a novel approach to find the most suitable server for VM placement. We have introduced an Optimal VM Allocation Framework (OVAF) in which the hosts at the source requests the destination for their available slots. Based on the response from the available servers, the utilization factor is calculated and the selection of appropriate destination for VM placement is done. Simulations carried out have shown 10% improvement in energy saving.</span></p>

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