scholarly journals Roulette Wheel Selection Model based on Virtual Machine Weight for Load Balancing in Cloud Computing

2014 ◽  
Vol 16 (5) ◽  
pp. 65-70
Author(s):  
Rashed Al-Marhabi ◽  
◽  
Mohamed Haggag ◽  
Amal Elsayed Aboutabl
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Selection Model ◽  
Roulette Wheel Selection ◽  
Model Based ◽  
Roulette Wheel

scholarly journals Virtual Machine Consolidation using Roulette wheel selection Strategy

2022 ◽  
Vol 06 (01) ◽  
Author(s):  
Raghi K.R K R
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Carbon Dioxide Emissions ◽  
Data Centers ◽  
High Energy ◽  
Selection Strategy ◽  
Cloud Data ◽  
Roulette Wheel Selection ◽  
Roulette Wheel ◽  
Vm Consolidation

Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. Virtual Machine [VM] consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/under load detection, VM selection and VM placement. In Our Proposed Model We are going to use Roulette-Wheel Selection Strategy, Where the VM selects the Instance type and Physical Machine [PM] using Roulette-Wheel Selection Mechanism Keywords—searchable encryption, dynamic update, cloud computing

Efficient task scheduling on virtual machine in cloud computing environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahfooz Alam ◽  
Mahak ◽  
Raza Abbas Haidri ◽  
Dileep Kumar Yadav
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machine ◽  
Task Allocation ◽  
Completion Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Content Type ◽  
Minimum Completion Time

Purpose Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization. Design/methodology/approach Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing. Findings To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time. Originality/value The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

A dynamic cloud service selection model based on trust and SLA in cloud computing

2019 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Bamei Tao ◽  
Quanwang Wu ◽  
Lei Guo ◽  
Junhao Wen ◽  
Yubiao Wang
Keyword(s):  
Cloud Computing ◽  
Service Selection ◽  
Cloud Service ◽  
Selection Model ◽  
Model Based

scholarly journals LOAD BALANCING IN CLOUD ENVIRONMENT: A REVIEW

2016 ◽  
Vol 15 (8) ◽  
pp. 6986-6990
Author(s):  
Sheenam Kamboj ◽  
Mr. Navtej Singh Ghumman
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Computing Platform ◽  
Side Load ◽  
Virtualization Technology ◽  
Cloud Computing Platform ◽  
Demand Changes

An essential role of cloud computing platform is to dynamically balance the load among the different servers in order to improve resource utilization and to avoid hotspots. Load balancing (LB) is done on both sides i.e. on provider as well as on consumer side. On provider side, load balancing is the problem of allocating virtual machines to servers at runtime. Virtual Machine need to be reassigned so that servers do not get overloaded as demand changes. On consumer side application load can be balanced which provides efficiency to the consumers. On cloud computing platform, load balancing of the entire system can be dynamically handled by  using virtualization technology through which itÂbecomes possible to remap virtual machine and physical resources according to  the change in load. However, in order to improve performance, the virtual machines have to fully utilize its resources and services by adapting to computing environment dynamically. The load balancing with proper allocation of resources must be guaranteed in order to improve resource utility.

Virtual Machine Migration Based on Trust Measurement of Computer Node

2014 ◽  
Vol 536-537 ◽  
pp. 678-682
Author(s):  
Zhi Hong Liang ◽  
Zhi Qiang Liang ◽  
Yi Ming Tan ◽  
Xue Cheng Lv
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Virtual Machine Migration ◽  
Performance Constraints ◽  
Computing Platform ◽  
Physical Machine ◽  
Cloud Computing Platform ◽  
Trust Measurement ◽  
Computer Node

Currently, the study of virtual machine migration in cloud computing platform which usually did not consider the trustworthiness of target physical machine. For this, the paper proposes a trusted virtual machine migration with performance constraints algorithm (TVM2PC). The trustworthiness of target physical machine includes direct trustworthiness and indirect trustworthiness. By this method, a virtual machine will be migrated to a trusted physical machine. A large of experiment shows that the proposed method can give a better result than the existing method in load balancing and trustworthiness.

scholarly journals A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Jia Zhao ◽  
Yan Ding ◽  
Gaochao Xu ◽  
Liang Hu ◽  
Yushuang Dong ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Power Saving ◽  
Genetic Operators ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Vm Migration ◽  
Location Selection ◽  
Cloud Data

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

scholarly journals A Detailed Analysis on Several Load Balancing Algorithm in Cloud Computing

10.29007/rnvj ◽  
2018 ◽  
Author(s):  
Shubhra Saxena ◽  
Navneet Sharma ◽  
Akash Saxena ◽  
Jayanti Goyal
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Load Balancing ◽  
Detailed Analysis ◽  
Virtual Machine ◽  
Bumble Bee ◽  
Cloud Framework ◽  
Execution Unit ◽  
Broad Variety ◽  
Load Balancing Algorithm

Cloud computing (CC) is rising rapidly; an expansive number of clients are pulled in towards cloud administrations for more fulfillments. Distributed computing is most recent developing innovation for expansive scale dispersed processing and parallel registering. CC gives vast pool of shared assets, program bundle, data, stockpile and a broad variety of uses according to client requests at any example of time. Adjusting the heap has turned out to be all the more intriguing examination zone in this field. Better load adjusting calculation in cloud framework builds the execution and assets use by progressively dispersing work stack among different hubs in the framework. Virtual machine (VM) is an execution unit that goes about as an establishment for distributed computing innovation. Bumble bee conduct propelled stack adjusting enhances the general throughput of handling and need construct adjusting centers with respect to decreasing the measure of time an errand needs to look out for a line of the VM.

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