TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Quantitative Approach to Minimize Energy Consumption in Cloud Data Centres using VM Consolidation Algorithm |
| 영문제목(English Title) |
A Quantitative Approach to Minimize Energy Consumption in Cloud Data Centres using VM Consolidation Algorithm |
| 저자(Author) |
M. Hema
S. KanagaSubaRaja
|
| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0312 ~ 0334 (2023. 02) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms’ efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms. |
| 키워드(Keyword) |
Energy consumption
Data Centres
Virtual Machine
Physical Machine
SLA
Regression
VM migration
VM selection
VM Placement
Host Utilization
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| 파일첨부 |
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