Analytical Modelling and Evaluation of Load Balance Mechanism in Cloud Computing

Yıl 2021, , 279 - 286, 31.07.2021

Yonal Kırsal , Ersin Caglar

https://doi.org/10.17671/gazibtd.723411

Cited By: 1

Öz

Recently, rapid improvement in the field of information technology has led to the development and deployment of new computing paradigms. Cloud computing is just one of these innovations. Cloud computing is an internet-based computing technology that shares hardware and software resources over the internet with other cloud users. With the rapid development of cloud computing and internet technology, there has been a significant increase in the number of users and become most popular technology all around the world. As rapidly increasing number of cloud users, many challenges have arisen. Load balancing mechaisms is one of the key challenges in cloud computing encironment. Thus, in this paper, analytical models and evaluation of dynamic and static load balancing mechanisms are presented in order to obtain better performance results. Mean queue length, mean response time and blocking probability results are presented for performance evaluation. Simulation is also created in order to show the efficiency and accuracy of the proposed models. The obtained results show good agreement with the simulation results. In addition, it is clearly seen from the obtained results that dynamic load balancing outperforms to static load balancing mechanism in cloud computing especially in a loaded system.

Anahtar Kelimeler

cloud computing, load balancing, performance evaluation, queueing theory

Bulut Bilişimde Yük Dengeleme Mekanizmasının Analitik Modellemesi ve Performans Değerlendirmesi

Öz

Günümüzde, bilişim teknolojileri alanındaki hızlı gelişim, yeni bilgi işlem dizilerinin/paradigmalarının geliştirilmesine ve yayılmasına öncülük etmektedir. Bulut bilişim teknolojisi bu yeniliklerden sadece bir tanesidir. Bulut bilişim, internet tabanlı bir teknoloji olup diğer bulut kullanıcılarına donanım ve yazılım paylaşımı sağlayan bir teknolojidir. Bulut bilişim ve internet teknolojisinin hızlı gelişmesi ile tüm dünyada bulut bilişimin kullanıcılarında önemli bir artış olmuştur. Bu hızlı kullanıcı artışı, birçok sorunu da beraberinde getirmiştir. Bu sorunların en önemlilerinden bir tanesi yük dağılımı ve dengeleme mekanizmasıdır. Bu nedenle, bu çalışmada daha iyi sistem performans sonuçlarının elde edilebilmesi amacıyla analitik modeller farklı yük dengeleme mekanizmalarına uygulanmıştır. Performans değerlendirmesi için ortalama yük sayısı, ortalama tepki süresi ve engelleme olasılığı sonuçları sunulmuştur. Önerilen modellerin verimliliğini ve doğruluğunu göstermek için ise simülasyon oluşturulmuştur. Elde edilen sonuçların simülasyon sonuçları ile eşleştiği görülmektedir. Ek olarak, elde edilen sonuçlardan dinamik yük dengelemenin özellikle sistemin yoğun olduğu zamanlarda bulut bilişimdeki statik yük dengeleme mekanizmasından daha iyi performans gösterdiği açıkça görülmektedir

Anahtar Kelimeler

bulut bilişim, yük dengeleme, performans analizi, kuyruk teorisi

Kaynakça

• E. Çağlar ve Y. Kirsal, "Analytical Modelling and Performance Evaluation of Security Issues For Cloud Computing", 27th Signal Processing and Communications Applications Conference (SIU), IEEE, 2019.
• C. Paşaoğlu, ve E. Cevheroğlu, “Bulut Bilişim Sistemleri Kapsamında Kişisel Verilerin Şifreleme Yöntemleri ile Korunması”, Bilişim Teknolojileri Dergisi, 13(2), 183-195, 2020.
• H. Özcan, ve B. G. Emiroğlu, “Bulut Tabanlı Öğrenme Yönetim Sistemi Seçiminde Bulanık Çok Kriterli Karar Analizi Yaklaşımı”, Bilişim Teknolojileri Dergisi, 13(1), 97-111, 2020.
• Ç. Çetin, N. Yaman, L. Sabah, E. Ayday, “Bulut Bilişim (Cloud Computing) Teknolojisinin Uzaktan Algılama ve Coğrafi Bilgi Sistemlerinde Uygulama Olanakları”, Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği VII. Teknik Sempozyumu, 23-25, 2013.
• D. Goutam, A. Verma, N. Agrawal, “The Performance Evaluation Of Proactive Fault Tolerant Scheme Over Cloud Using Cloudsim Simulator”, Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), 171-176, IEEE, 2014.
• M. R. Mesbahi, M. Hashemi, A. M. Rahmani, “Performance evaluation and analysis of load balancing algorithms in cloud computing environments”, 2016 Second International Conference on Web Research (ICWR), 145-151, IEEE, 2016.
• E. N. Desyatirikova, O. V. Kuripta, Y. S. Stroganova, ve I. P. Abrosimov, “Quality Management in IT Service Management Based On Statistical Aggregation And Decomposition Approach”, In 2017 International Conference Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), 500-505, IEEE, 2017.
• S. K. Mishra, B. Sahoo, ve P. P. Parida, “Load balancing in cloud computing: a big picture”, Journal of King Saud University-Computer and Information Sciences, 2018.
• V. N. Volkova, L. V. Chemenkaya, E. N. Desyatirikova, M. Hajali, A. Khodar, ve A. Osama, “Load balancing in cloud computing”, 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 387-390, IEEE, 2018.
• K. Hashizume, D. G. Rosado, E. FernandezMedina, ve E. B. Fernandez, “An Analysis of Security Issues For Cloud Computing”, Journal of Internet Services and Applications, 4-5, 2013.
• A. S. Hanamakkanavar, ve V. S. Handur, “Load Balancing In Distributed Systems: A survey”, Proceedings of International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE), 14(2), 2015.
• H. Siar, K. Kiani, A. T. Chronopoulos, “A Combination Of Game Theory And Genetic Algorithm For Load Balancing In Distributed Computer Systems”, International Journal of Advanced Intelligence Paradigms, 9(1), 82-95, 2017.
• H. Khazaei, J. Misic, V. B. Misic, “Performance of an iaas cloud with live migration of virtual machines”, In 2013 IEEE Global Communications Conference (GLOBECOM), 2289–2293, 2013.
• L. Guo, T. Yan, S. Zhao, C. Jiang, “Dynamic performance opti-mization for cloud computing using m/m/m queueing system”, Journal of Applied Mathematics, 2014.
• R. Ghosh, F. Longo, V. K. Naik, K. S. Trivedi, “Modeling andperformance analysis of large scale iaas clouds,” Future Generation Computer Systems, 29(5), 1216–1234, 2013.
• K. Salah, K. Elbadawi, R. Boutaba, “An analytical model for estimating cloud resources of elastic services,” Journal of Network and Systems Management, 24(2), 285–308, 2016.
• J. Vilaplana, F. Solsona, I. Teixido, J. Mateo, F. Abella, and J. Rius, “A queuing theory model for cloud computing”, The Journal of Supercomputing, 69(1), 492–507, 2014.
• N. Joshi, K. Kotecha, D. B. Choksi, S. Pandya, “Implementation of Novel Load Balancing Technique in Cloud Computing Environment”, 2018 International Conference on Computer Communication and Informatics (ICCCI), 1-5, IEEE, 2018.
• K. Pathak, G. Vahinde, “Comparison of Particle Swarm Optimization And Genetic Algorithm For Load Balancing In Cloud Computing Environment”, International Journal of Research in Computer & Information Technology (IJRCIT), 1(1), 2015.
• A. Yadav, “Comparative Analysis Of Load Balancing Algorithms In Cloud Computing”, International Journal of Enhanced Research in Management & Computer Applications, 4(9), 2015.
• A. Nair, S. Anand, S. A. Sinha, “Performance Booster For Load Balancing In Cloud Computing With My Load Balancer Techinique”, International Journal of Recent Technology and Engineering, 8(1), 2019.
• G. Megharaj, K. G. Mohan “A Survey On Load Balancing Techniques In Cloud Computing”, IOSR Journal of Computer Engineering (IOSR-JCE), 18(2), 55-61, 2016.
• R. Gao, J. Wu, “Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization”, Future Internet, 7, 465-483, 2015.
• Y. Zhu, D. Zhao, W. Wang, H. He, “A Novel Load Balancing Algorithm Based on Improved Particle Swarm Optimization in Cloud Computing Environment”, International Conference on Human Centered Computing, Springer, 634–645, 2016.
• V. S. Handur, S. Belkar, S. Deshpande, P. R. Marakumbi, "Study of load balancing algorithms for Cloud Computing", 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), Bangalore, India, 173-176, 2018.
• Y. Kırsal, "Performability evaluation and optimization analysis of repairmen for large-scale networks", 25th Signal Processing and Communications Applications Conference (SIU), 1-4, 2017.
• Y. Kırsal, "Analytical modelling and optimization analysis of large-scale communication systems and networks with repairmen policy”, Computing, 100(5), 503-527, 2018.
• S. Grıshechkın, Queueing Theory, Probability Theory and Mathematical Statistics, 1, 455, 2020.
• J. Cao, Z. Ma, S. Guo, X. Yu, “Performance analysis of non-exhaustive wireless sensor networks based on queueing theory”, International Journal of Communication Networks and Distributed Systems, 24(2), 186-213, 2020.
• S. K. Majhi, S. S. Pal, S. Bhuyan, S. K. Dhal, “Queuing Analysis of Cloud Load Balancing Algorithms”, International Journal of Knowledge-Based Organizations (IJKBO), 8(1), 50-67, 2018.
• A. Pourghaffari, M. Barari,, S. Sedighian Kashi, “An efficient method for allocating resources in a cloud computing environment with a load balancing approach”, Concurrency and Computation: Practice and Experience, 31(17), e5285, 2019.
• F. Ebadifard, S. M. Babamir, “Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment”, Cluster Computing, 1-27, 2020.