scholarly journals An Approach to Reduce Energy Consumption in Cloud data centers using Harmony Search Algorithm

2016 ◽  
Vol 6 (4) ◽  
pp. 01-10
Author(s):  
Masoumeh Najafi ◽  
Keyvan Mohebbi
Keyword(s):  
Energy Consumption ◽  
Data Centers ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Reduce Energy Consumption

scholarly journals Hybrid and adaptive harmony search algorithm for optimizing energy efficiency in VMP problem in cloud environment

2022 ◽  
Vol 11 (2) ◽  
pp. 113-126
Author(s):  
Amol C. Adamuthe ◽  
Smita M. Kagwade
Keyword(s):  
Energy Consumption ◽  
Data Centers ◽  
Search Strategy ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Energy Usage ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Local Search Strategy

Data Center energy usage has risen dramatically because of the rapid growth and demand for cloud computing. This excessive energy usage is a challenge from an economic and environmental point. Virtual Machine Placement (VMP) along with virtualization technologies is widely used to manage power utilization in data centers. The assignment of virtual machines to physical machines affects energy consumption. VMP is a process of mapping VMs onto a set of PMs in a data center to minimize total power consumption and maximize resource utilization. The VMP is an NP-hard problem due to its constraints and huge combinations. In this paper, we formulated the problem as a single objective optimization problem in which the objective is to minimize the energy consumption in cloud data centers. The main contribution of this paper is hybrid and adaptive harmony search algorithm for optimal placements of VMs to PMs. HSA with adaptive PAR settings, simulated annealing and local search strategy aims at minimizing energy consumption in cloud data centers with satisfying given constraints. Experiments are conducted to validate the performance of these variations. Results show that these hybrid HSA variations produce better results than basic HSA and adaptive HSA. Hybrid HS with simulated annealing, and local search strategy gives better results than other variants for 80 percent datasets.

scholarly journals Green Cloud and reduction of energy consumption

2015 ◽  
Vol 4 (1) ◽  
pp. 51-60
Author(s):  
Kamyab Khajehei
Keyword(s):  
Energy Consumption ◽  
Data Centers ◽  
Cloud Data ◽  
Active Devices ◽  
High Efficient ◽  
Efficient Data ◽  
Cloud Data Centers ◽  
Reduce Energy Consumption ◽  
Green Cloud ◽  
Reduction Of Energy Consumption

By using global application environments, cloud computing based data centers growing every day and this exponentially grows definitely effect on our environment. Researchers that have a commitment to their environment and others which was concerned about the electricity bills came up with a solution which called “Green Cloud”. Green cloud data centers based on how consume energy are known as high efficient data centers. In green cloud we try to reduce number of active devices and consume less electricity energy. In green data centers toke an advantage of VM and ability of copying, deleting and moving VMs over the data center and reduce energy consumption. This paper focused on which parts of data centers may change and how researchers found the suitable solution for each component of data centers. Also with all these problems why still the cloud data centers are the best technology for IT businesses.

scholarly journals Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine Placement

2018 ◽  
Vol 8 (5) ◽  
pp. 3890
Author(s):  
A. R. Mohazabiyeh ◽  
K. H. Amirizadeh
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
High Load ◽  
Cloud Services ◽  
Heavy Load ◽  
Cloud Data ◽  
Light Load ◽  
Cloud Data Centers ◽  
Reduce Energy Consumption

With the increasing expansion of cloud data centers and the demand for cloud services, one of the major problems facing these data centers is the “increasing growth in energy consumption ". In this paper, we propose a method to balance the burden of virtual machine resources in order to reduce energy consumption. The proposed technique is based on a four-adaptive threshold model to reduce energy consumption in physical servers and minimize SLA violation in cloud data centers. Based on the proposed technique, hosts will be grouped into five clusters: hosts with low load, hosts with a light load, hosts with a middle load, hosts with high load and finally, hosts with a heavy load. Virtual machines are transferred from the host with high load and heavy load to the hosts with light load. Also, the VMs on low hosts will be migrated to the hosts with middle load, while the host with a light load and hosts with middle load remain unchanged. The values of the thresholds are obtained on the basis of the mathematical modeling approach and the 𝐾-Means Clustering Algorithm is used for clustering of hosts. Experimental results show that applying the proposed technique will improve the load balancing and reduce the number of VM migration and reduce energy consumption.

scholarly journals Energy aware dynamic virtual machine consolidation in cloud data centers

2018 ◽  
Vol 7 (2.8) ◽  
pp. 550 ◽  
Author(s):  
G Anusha ◽  
P Supraja
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Energy Aware ◽  
Large Power ◽  
Complex Tasks ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Virtual Machine Consolidation ◽  
Cloud Data Centers

Cloud computing is a growing technology now-a-days, which provides various resources to perform complex tasks. These complex tasks can be performed with the help of datacenters. Data centers helps the incoming tasks by providing various resources like CPU, storage, network, bandwidth and memory, which has resulted in the increase of the total number of datacenters in the world. These data centers consume large volume of energy for performing the operations and which leads to high operation costs. Resources are the key cause for the power consumption in data centers along with the air and cooling systems. Energy consumption in data centers is comparative to the resource usage. Excessive amount of energy consumption by datacenters falls out in large power bills. There is a necessity to increase the energy efficiency of such data centers. We have proposed an Energy aware dynamic virtual machine consolidation (EADVMC) model which focuses on pm selection, vm selection, vm placement phases, which results in the reduced energy consumption and the Quality of service (QoS) to a considerable level.

Energy and Quality Aware Multi-Objective Resource Allocation Algorithm in Cloud

2021 ◽  
pp. 2150052
Author(s):  
Koné Kigninman Désiré ◽  
Eya Dhib ◽  
Nabil Tabbane ◽  
Olivier Asseu
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Search Algorithm ◽  
Fitness Function ◽  
Video Quality ◽  
Minimum Energy ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Energy Aware ◽  
Resource Allocation Algorithm

Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massive resources for video representation and its streaming when several simultaneous players reach a particular point. Alternatively, various players may have separate necessities on Quality-of Experience, like low delay, high-video quality, etc. The challenging task is providing better service by the fixed cloud resource. Hence, there is a necessity for an energy-aware multi-resource allocation in the cloud. This paper devises a Fractional Rider-Harmony search algorithm (Fractional Rider-HSA) for resource allocation in the cloud. The Fractional Rider-HSA combines fractional calculus, Rider Optimization algorithm (ROA), and HSA. Moreover, the fitness function, like mean opinion score (MOS), gaming experience loss, fairness, energy consumption, and network parameters, is considered to determine the optimal resource allocation. The proposed model produces the maximal MOS of 0.8961, maximal gaming experience loss (QE) of 0.998, maximal fairness of 0.9991, the minimum energy consumption of 0.3109, and minimal delay 0.2266, respectively.

Energy Efficient Scheduling for Multiple Workflows in Cloud Environment

2018 ◽  
Vol 13 (3) ◽  
pp. 14-34
Author(s):  
Ritu Garg ◽  
Neha Shukla
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Cloud Environment ◽  
Cloud Data ◽  
Available Energy ◽  
Cloud Data Centers ◽  
Reduce Energy Consumption ◽  
Scheduling Heuristic ◽  
Communication Stage ◽  
Energy Efficient Scheduling

Cloud computing makes utility computing possible with pay as you go model. It virtualizes the systems by polling and sharing the resources, thus we need to handle more than one workflow at the same time. Workflow is the standard to represent compute intensive applications in scientific and engineering domain. Hence, in this article, the authors presented the scheduling heuristic for multiple workflows running parallel in the cloud environment with the aim to reduce the energy consumption as it is one of the major concerns of cloud data centers along with the execution performance. In the proposed approach, first clustering is performed to minimize the energy consumption and execution time during communication corresponding to precedence constraint tasks. Then cluster are scheduled is on the best available energy efficient resources. Finally, DVFS is applied in order to reduce energy consumption further when the nodes are in the idle and communication stage. The simulation has been performed on CloudSim and the results show the reduction in energy consumption by up to 42%.

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