Energy Aware Next Fit Allocation Approach for Placement of VMs in Cloud Computing Environment

2020 ◽  
pp. 436-453
Author(s):  
Jyotsna Sengupta ◽  
Pardeep Singh ◽  
P. K. Suri
Keyword(s):  
Cloud Computing ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Allocation Approach

scholarly journals Minimizing Energy Consumption by Task Consolidation in Cloud Centers with Optimized Resource Utilization

2016 ◽  
Vol 6 (6) ◽  
pp. 3283 ◽  
Author(s):  
Mahendra Kumar Gourisaria ◽  
S. S. Patra ◽  
P. M. Khilar
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Carbon Dioxide Emission ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Cpu Utilization

<p>Cloud computing is an emerging field of computation. As the data centers consume large amount of power, it increases the system overheads as well as the carbon dioxide emission increases drastically. The main aim is to maximize the resource utilization by minimizing the power consumption. However, the greatest usages of resources does not mean that there has been a right use of energy.  Various resources which are idle, also consumes a significant amount of energy. So we have to keep minimum resources idle. Current studies have shown that the power consumption due to unused computing resources is nearly 1 to 20%. So, the unused resources have been assigned with some of the tasks to utilize the unused period. In the present paper, it has been suggested that the energy saving with task consolidation which has been saved the energy by minimizing the number of idle resources in a cloud computing environment. It has been achieved far-reaching experiments to quantify the performance of the proposed algorithm. The same has also been compared with the FCFSMaxUtil and Energy aware Task Consolidation (ETC) algorithm. The outcomes have shown that the suggested algorithm surpass the FCFSMaxUtil and ETC algorithm in terms of the CPU utilization and energy consumption.</p>

Adapting Market-Oriented Policies for Scheduling Divisible Loads on Clouds

2020 ◽  
Vol 11 (2) ◽  
pp. 45-55
Author(s):  
Mimi Liza Abdul Majid ◽  
Suriayati Chuprat
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Cloud Service ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Divisible Loads ◽  
Workload Allocation ◽  
The Cost ◽  
Baseline Approach ◽  
Allocation Approach

Cloud computing has become an important alternative for solving big data processing. Nowadays, cloud service providers usually offer users a virtual machine with various combinations of prices. As each user has different circumstances, the problem of choosing the cost-minimized combination under a deadline constraint as well as user's preference is becoming more complex. This article is concerned with the investigation of adapting a user's preference policies for scheduling real-time divisible loads in a cloud computing environment. The workload allocation approach used in this research is using Divisible Load Theory. The proposed algorithm aggregates resources into groups and optimally distributes the fractions of load to the available resources according to user's preference. The proposed algorithm was evaluated by simulation experiments and compared with the baseline approach. The result obtained from the proposed algorithm reveals that a significant reduction in computation cost can be attained when the user's preferences are low priority.

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