An empirical evaluation of energy-aware load balancing technique for cloud data center

We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.

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Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Center

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.325-326.1730 ◽

2013 ◽

Vol 325-326 ◽

pp. 1730-1733 ◽

Cited By ~ 1

Author(s):

Si Yuan Jing ◽

Shahzad Ali ◽

Kun She

Keyword(s):

Energy Consumption ◽

Data Center ◽

Network Flow ◽

Resource Provisioning ◽

Approximate Algorithm ◽

Vm Placement ◽

Energy Aware ◽

Cloud Data Center ◽

Cloud Data ◽

Placement Change

Numerous part of the energy-aware resource provision research for cloud data center just considers how to maximize the resource utilization, i.e. minimize the required servers, without considering the overhead of a virtual machine (abbreviated as a VM) placement change. In this work, we propose a new method to minimize the energy consumption and VM placement change at the same time, moreover we also design a network-flow-theory based approximate algorithm to solve it. The simulation results show that, compared to existing work, the proposed method can slightly decrease the energy consumption but greatly decrease the number of VM placement change

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SBPSO-LB: SB-PSO based Secure Load Balancing Approach for Cloud Data Center with RSA

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) ◽

10.1109/upcon.2018.8597011 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sweta Dey

Keyword(s):

Load Balancing ◽

Data Center ◽

Cloud Data Center ◽

Cloud Data

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Enhancing the dynamic load balancing technique for cloud computing using HBATAABC algorithm

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962320500415 ◽

2020 ◽

Vol 11 (05) ◽

pp. 2050041

Author(s):

Arif Ullah ◽

Nazri Mohd Nawi

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Local Search ◽

Data Center ◽

Bat Algorithm ◽

Network Stability ◽

Cloud Data Center ◽

Abc Algorithm ◽

Cloud Data ◽

Novel Technique

Cloud computing brings incipient transmutations in different fields of life and consists of different characteristics and virtualization is one of them. Virtual machine (VM) is one of the main elements of virtualization. VM is a process in which physical server changes into the virtual machine and works as a physical server. When a user sends data or request for data in cloud data center, a situation can occur that may cause the virtual machines to underload data or overload data. The aforementioned situation can lead to failure of the system or delay the user task. Therefore, appropriate load balancing techniques are required to surmount the above two mentioned problems. Load balancing is a technique utilized in cloud computing for management of the resource by a condition such that a maximum throughput is achieved with slightest reaction time and additionally dividing the traffic between different servers or VM so that it can get data without any delay. For the amelioration of load balancing technique in this study, a novel technique is used which is coalescence of BAT and ABC algorithms both of which are nature-inspired algorithms. When the ABC algorithm local search section changes with BAT algorithm local search section, a second modification takes place in the fitness function of BAT algorithm. The proposed technique is known as HBATAABC algorithm. The novel technique implemented by utilizing transfer strategy policy in VM improves the performance of data allocation system of VM in the cloud data center. To check the performance of the proposed algorithm, three main parameters are used which are network average time, network stability and throughput. The performance of the proposed novel technique is verified and tested with the help of cloudsim simulator. The result shows that the suggested modified algorithm increases performance by 1.30% of network average time, network stability and throughput as compared with BAT algorithm, ABC algorithm and RRA algorithm. Nevertheless, the proposed algorithm is more precise and expeditious as compared with the three models.

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Energy-aware Multi-dimensional Resource Allocation Algorithm in Cloud Data Center

KSII Transactions on Internet and Information Systems ◽

10.3837/tiis.2017.09.008 ◽

2017 ◽

Vol 11 (9) ◽

Cited By ~ 1

Keyword(s):

Resource Allocation ◽

Data Center ◽

Energy Aware ◽

Cloud Data Center ◽

Resource Allocation Algorithm ◽

Allocation Algorithm ◽

Cloud Data

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Enhanced Load Balancing Approach for Cloud Data Center Load Optimization

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2021.9399 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1270-1274

Author(s):

J. Prassanna ◽

V. Neelanarayanan

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Data Center ◽

Resource Control ◽

Resource Optimization ◽

Computing Technology ◽

Cloud Data Center ◽

Cloud Data ◽

Load Optimization ◽

Related Data

Cloud computing is a most popular technology that has huge response in markets. Cloud computing has the potential to access applications and their related data via the Internet anywhere. Most companies already pay for the use of cloud resources for storage purposes and ultimately reduce the costs of infrastructure spending. They can make use of this technology for accessing to company applications like pay-as-you-go approach. One of the major obstacles associated with cloud computing technology is to better optimization of resource allocation. Assigning of workloads to the servers using load balancing techniques is used to achieve less response time and better resource optimization across the server. Resource control and balance of load are the major conflicts in the cloud environment, which is why there are different load balancing algorithms, each with its own advantages and disadvantage. In order to achieve a better economy and mutual benefit, efficient algorithms can be derived simultaneously by optimizing servers, green computing and better utilization of resources. The objective of this paper is to analyze and enhance existing load balancing algorithms.

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