Energy saving slot allocation-based multicast routing in cloud wireless mesh network

被引:0
作者
Jeyakarthic M. [1 ]
Subalakshmi N. [2 ]
机构
[1] Department of Computer and Information Science, Annamalai University, Tamil Nadu, Annamalai Nagar
[2] Computer Science and Engineering Wing, Directorate of Distance Education, Annamalai University, Tamil Nadu, Annamalai Nagar
关键词
cloud computing; cloud services; data centre; distributed system; load balancing; parallel processing; scheduling; virtual machine;
D O I
10.1504/IJCC.2023.130895
中图分类号
学科分类号
摘要
Many techniques of cloud computing are affected by surge load and time issues. This proposed method for virtual cloud planning utilise simple protocols in a single or multiple data centres. A new load balanced amortised multi-scheduling algorithm (LBAM) is proposed for assigning the task to cloud based on active load in the cloud environment. The proposed system calculates the multiple attribute weight for each workplace and introduces an application source that changes in application virtualisation environment. The system calculates the cloud data weight based upon the allocation of data and its consequence based on the processing efficiency of the cloud machine. This approach works effectively in most configurations, also virtual machines hold the ability to process load with high pace and efficient memory management. A detailed comparative analysis is made with the existing methods namely SLA, FPGGA and DJS algorithms. The obtained results indicated that the LBAM model is superior to compared methods under several aspects. Copyright © 2023 Inderscience Enterprises Ltd.
引用
收藏
页码:148 / 162
页数:14
相关论文
共 18 条
[1]  
Agarwal A., Manisha G., Milind R.N., Shylaja S.S., Performance analysis of cloud based load balancing techniques, 2014 International Conference on Parallel, Distributed and Grid Computing, pp. 49-52, (2014)
[2]  
Al-Rayis E., Kurdi H., Performance analysis of load balancing architectures in cloud computing, 2013 European Modelling Symposium, pp. 520-524, (2013)
[3]  
Azimzadeh F., Biabani F., Multi-objective job scheduling algorithm in cloud computing based on reliability and time, 2017 3th International Conference on Web Research (ICWR), pp. 96-101, (2017)
[4]  
Babu K.R., Joy A.A., Samuel P., Load balancing of tasks in cloud computing environment based on bee colony algorithm, 2015 Fifth International Conference on Advances in Computing and Communications (ICACC), pp. 89-93, (2015)
[5]  
Behal V., Kumar A., Cloud computing: performance analysis of load balancing algorithms in cloud heterogeneous environment, 2014 5th International Conference-Confluence The Next Generation Information Technology Summit (Confluence), pp. 200-205, (2014)
[6]  
Beniwal P., Garg A., A comparative study of static and dynamic load balancing algorithms, International Journal of Advance Research in Computer Science and Management Studies, 2, 12, pp. 1-7, (2014)
[7]  
Duan R., Prodan R., Li X., Multi-objective game theoretic scheduling of bag-of-tasks workflows on hybrid clouds, IEEE Transactions on Cloud Computing, 2, 1, pp. 29-42, (2014)
[8]  
Geetha P., Robin C.R., A comparative-study of load-cloud balancing algorithms in cloud environments, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 806-810, (2017)
[9]  
Indukuri R.K.R., Penmasta S.V., Sundari M.R., Moses G.J., Performance evaluation of deadline aware multi-stage scheduling in cloud computing, 2016 IEEE 6th International Conference on Advanced Computing (IACC), pp. 229-234, (2016)
[10]  
Indukuri R.K.R., Varma P.S., Moses G.J., Performance measure of multi stage scheduling algorithm in cloud computing, 2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM), pp. 8-11, (2012)