Makespan and Energy Based Virtual Machine Scheduling in Cloud Systems

Yıl 2024, Cilt: 39 Sayı: 3, 1661 - 1672, 20.05.2024

Alper Kızıl , Korhan Karabulut

https://doi.org/10.17341/gazimmfd.1202336

Öz

Cloud computing is one of the newest computing paradigms that emerged after worldwide development of Internet infrastructure. It is a technology that saves both large companies and small and medium scale companies as well as independent developers from the cost of keeping infrastructure hardware up to date, and operational while also providing flexibility on resource use as well providing additional opportunity to minimize data losses. While in the future, it is evident that demand for cloud computing will be on the rise. These kinds of datacenters, due to their nature, consume large amount of energy and even the savings on smallest scales will enable these gigantic centers to save a significant amount of energy in total. If we have a look at the literature, we can see green computing is gaining immense popularity over the years. The Cloud Scheduling problem is a proven problem to be NP-Hard, aiming to find the best solution for a limited number of cloud resources, which could theoretically be serving an unlimited number of user demands. In this study, firstly, an experimental workload / power consumption model is proposed for a server computer, and then two genetic algorithms optimizing makespan and energy consumption are compared on these metrics at different server loads. As a result, it has been seen that these two criteria are closely related to each other, and it has been determined that optimizing the energy criterion has a more positive effect between 10% and 13% compared to the time criterion optimization at full or near full server loads. In this way, it has been shown that significant energy savings can be achieved by using energy optimization as an objective function at high server loads.

Anahtar Kelimeler

Energy Consumption, Makespan, Evolutionary algorithm, Virtual machine scheduling

Bulut sistemlerinde toplam tamamlanma ve enerji tabanlı sanal makine çizelgelemesi

Öz

Bulut bilişim, internet altyapısının son yıllarda dünya çapında gelişmesiyle önem kazanmış yeni bilişim paradigmalardandır. Hem büyük şirketlere hem de gelişme aşamasındaki küçük ve orta ölçekli şirketlere hem de bağımsız geliştiricilere kendi donanım altyapılarını güncel ve işler tutma maliyetlerinden kurtaran, kaynak kullanımında esneklik sağlayan, veri kayıplarını minimize edebilmeleri için ek olanak sağlayan bir teknolojidir. Gelecekte de bulut bilişime olan talebin artacağı ortadadır. Bu tür veri merkezleri yapıları gereği oldukça yüklü miktarda enerji tüketimi yapmaktadırlar, bu tüketimde yapılacak en küçük tasarruflar bile bu devasa merkezlerin çok önemli miktarda enerji tasarrufu yapmalarını sağlayacaktır. Literatüre baktığımızda da Yeşil bilişim (Green Computing) yıldan yıla gitgide önem kazanmaktadır. Bulut Çizelgeleme problemi, kısıtlı sayıdaki bulut kaynağının teoride sınırsız sayıda olabilecek kullanıcı talebine en uygun, en iyi çözümün bulunmasını amaçlayan NP- Zor olduğu kanıtlanmış bir problemdir. Bu çalışmada, öncelikle bir sunucu bilgisayarı için deneye dayalı bir iş yükü / güç tüketimi modeli önerilmiş, sonra da toplam bitiş süresi ve enerji tüketimi eniyileme yapan iki genetik algoritma, farklı sunucu yüklerinde bu ölçütler üzerinden kıyaslanmıştır. Sonuçta bu iki kriterin birbirleriyle yakın ilişkide olduğu görülmüş, ayrıca enerji kriterini eniyilemenin tam ya da tama yakın sunucu yüklerinde, zaman kriteri eniyilemeye göre %10 – %13 arasında daha olumlu bir etkisi olduğu saptanmıştır. Bu sayede, yüksek sunucu yüklerinde, enerji eniyilemenin amaç fonksiyonu olarak kullanılmasını ile ciddi oranda enerji tasarrufunun mümkün olabileceği gösterilmiştir.

Anahtar Kelimeler

Enerji tüketimi, Toplam bitiş süresi, Evrimsel Algoritma, Sanal makine çizelgeleme

Kaynakça

• 1. Synergy Research Group. Q2 Cloud Market Grows by 29% Despite Strong Currency Headwinds; Amazon Increases its Share.https://www.srgresearch.com/articles/q2-cloud-market-grows-by-29-despite-strong-currency-headwinds-amazon-increases-its-share. Yayın tarihi Temmuz, 28 2022. Erişim tarihi Kasım, 5, 2022.
• 2. Shehabi A., Smith S., Sartor D., Brown R., Herrlin M., Koomey J., Masanet E., Horner N., Azevedo I. Lintner W., United States Data Center Energy Usage Report, Berkeley National Laboratory, Orlando, 2016.
• 3. Clarivate. Clarivate Web of Science Document Search. https://www.webofscience.com/wos. Erişim tarihi Ağustos, 15 2022.
• 4. Haitao Y., Bi J., Zhou M., Qing L., Ammari A., Biobjective Task Scheduling for Distributed Green Data Centers, IEEE Transactions On Automation Science And Engineering. 18 (2), 731-742, 2021.
• 5. Othman S., Almalki F., Chakraborty C., Sakli H., Privacy-preserving aware data aggregation for IoT-based healthcare with green computing technologies, Computers and Electrical Engineering. 101 (2), 108025, 2022.
• 6. Medeni I., Virtual Private Server Or Micro Pcs: Which Is Better For The Learning Management System Decision?, 3rd International Conference on Education and Social Sciences (INTCESS), Istanbul - Türkiye, 2016.
• 7. Öztürk A., Ümit K., Medeni İ., Üçüncü B., Caylan M., Akba F., Medeni T., Green ICT (Information and Communication Technologies): A Review of Academic and Practitioner, International Journal of e Business and e Government Studies, 3 (1), 1-16, 2011.
• 8. Lannelongue L., Grealey J., Inouye M., Green Algorithms: Quantifying the Carbon Footprint of Computation, Advanced science, 8 (12), 2021.
• 9. Zhan Z.-H., Liu X.-F., Gong Y.-J, Zhang J., Chung H. S.-H, Li Y., Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches," ACM Computing Surveys, 47 (4), 1-33, 2015.
• 10. Chu P. C., Beasley J. E., A Genetic Algorithm for the Multidimensional Knapsack Problem, Journal of Heuristics, 4 (1), 63-86, 1998.
• 11. Prins C., A simple and effective evolutionary algorithm for the vehicle routing problem, Computers & Operations Research, 31 (12), 1985-2002, 2004.
• 12. Pezzella F., Morganti G., Ciaschetti G., A genetic algorithm for the Flexible Job-shop Scheduling Problem, Computers & Operations Research, 35 (10), 3202-3212, 2008.
• 13. Tosun U., Dokeroglu T., Cosar A., A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem, International Journal of Production Research, 51 (14), 4117-4133, 2012.
• 14. Quiroz-Castellanos M., Cruz-Reyes L., Torres-Jimenez J., Gómez C., Huacuja H., Alvim A., A grouping genetic algorithm with controlled gene transmission for the bin packing problem, Computers & Operations Research, 55 (1), 52-64, 2015.
• 15. Ha Q., Deville Y., Pham Q., A hybrid genetic algorithm for the traveling salesman problem with drone, Journal of Heuristics, 26, 219–247, 2020.
• 16. Wang R., Ying G., Lai L., LigBuilder: A Multi-Purpose Program for Structure-Based Drug Design, Molecular modeling annual, 6, 498-516, 2000.
• 17. Olague G., Mohr R., Optimal camera placement for accurate reconstruction, Pattern Recognition, 35 (4), 927-944, 2002.
• 18. Yılmaz A., Güzel M., Bostancı E., Askerzade I., A Novel Action Recognition Framework Based on Deep-Learning and Genetic Algorithms, IEEE Access, 8, 00631-100644, 2020.
• 19. Prasad T., Park N., Multiobjective Genetic Algorithms for Design of Water Distribution Networks, Journal of Water Resources Planning and Management, 130 (1), 73-82, 2004.
• 20. Zhou Y., Wang Y., Wang K., Kang L., Peng F., Wang L., Pang J., Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors, Applied Energy, 260, 2020.
• 21. Shahid F., Zameer A., Muneeb M., A novel genetic LSTM model for wind power forecast, Energy, 223, 2021.
• 22. Zhu J., Optimal reconfiguration of electrical distribution network using the refined genetic algorithm, Electric Power Systems Research, 62 (1), 37-42, 2002.
• 23. Arabasadi Z., Alizadehsani R., Roshanzamir M., Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm, Computer Methods and Programs in Biomedicine, 141, 19-26, 2017.
• 24. Beyaz S., Açıcı K., Sümer E., Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches, Joint Diseases and Related Surgery, 31 (2), 175-183, 2020.
• 25. Cui H., Li Y., Liu X., Ansari N., Liu Y., Cloud service reliability modelling and optimaltask scheduling, IET Communications, 11 (2), 161-167, 2016.
• 26. Mohammad S., Javanmardi S., Saeid A., Cordeschi N., FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method, Cluster Computing-The Journal of Networks Software Tools and Applications, 18 (2), 829-844, 2015.
• 27. Li H., Wang B., Yuan Y., Zhou M., Fan Y., Xia Y., Scoring and Dynamic Hierarchy-Based NSGA-II for Multiobjective Workflow Scheduling in the Cloud, IEEE Transactions on Automation Science and Engineering, 19 (2), 982-993, 2022.
• 28. Kılınçcı Ö., Assembly line balancing problem with resource and sequence-dependent setup times (ALBPRS), Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (1), 557-570, 2023.
• 29. Sarac T., Ozcelik F., A matheuristic algorithm for multi-objective unrelated parallel machine scheduling problem, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (3), 1953-1966, 2023.
• 30. Intel Corporation. Intel® Z690 Chipset. https://www.intel.com/content/www/us/en/products/sku/218833/intel-z690-chipset/specifications.html. Erişim tarihi Kasım, 5 2022.
• 31. Crucial.How Much Power Does Memory Use? https://www.crucial.com/support/articles-faq-memory/how-much-power-does-memory-use. Erişim tarihi Kasım, 5 2022.
• 32. Western Digital. Product Brief: WD Red Pro NAS HDD. https://documents.westerndigital.com/content/dam/doc-library/en_us/assets/public/western-digital/product/internal-drives/wd-red-pro-hdd/product-brief-western-digital-wd-red-pro-hdd.pdf. Erişim tarihi Kasım, 5 2022.
• 33. Standard Performance Evaluation Corporation. First Quarter 2022 SPECpower_ssj2008 Results. https://www.spec.org/power_ssj2008/results/res2022q1/. Yayın Tarihi Mart, 24 2022. Erişim tarihi Kasım, 5 2022.
• 34. Xia X., Qiu H., Xu X., Zhang Y., Multi-objective workflow scheduling based on genetic algorithm in cloud environment, Information Sciences, 606 (1), 38-59, 2022.
• 35. Liang B., Liu R., Dia D., Design of Virtual Machine Scheduling Algorithm in Cloud Computing Enviroment, Journal of Sensors, 2022, 2022.
• 36. Li J., Zhang R., Zheng Y., QoS-aware and multi-objective virtual machine dynamic scheduling for big data centers in clouds, Soft Computing, 1-14, 2022.
• 37. Aida A. M., Movaghar A., Rahmani A. M., A new reliability-based task scheduling algorithm in cloud computing, International Journal of Communication Systems, 35 (3), 2022.
• 38. Zhu Z., Zhang G., Li M., Liu. X., Evolutionary multi-objective workflow scheduling in cloud, IEEE Transactions on parallel and distributed Systems, 27 (5), 1344-1357, 2015.
• 39. Shojafar M., Javanmardi S., Abolfazli S., Cordeschi N., FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method, Cluster Computing, 18 (2), 829-844, 2015.