پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 610 |
| تعداد مقالات | 9,026 |
| تعداد مشاهده مقاله | 67,082,717 |
| تعداد دریافت فایل اصل مقاله | 7,656,151 |
Adaptive optimization of multi-objective query in heterogeneous cloud database environment using multi-level cache | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 8، دوره 16، شماره 6، شهریور 2025، صفحه 81-91 اصل مقاله (2.38 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.32328.4801 | ||
| نویسندگان | ||
| Seyyed Kamal Vaezpour1؛ Mohammadreza Mollahoseini Ardakani* 2؛ Kamal Mirzaie1 | ||
| 1Department of Computer Engineering, Maybod Branch,Islamic Azad University, Maybod, Iran | ||
| 2Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran | ||
| تاریخ دریافت: 21 مهر 1402، تاریخ بازنگری: 21 آذر 1402، تاریخ پذیرش: 28 آذر 1402 | ||
| چکیده | ||
| Mobile Cloud Computing is one of the most prominent future mobile technology infrastructures, because it accumulates the benefits of mobile computing and cloud computing, and provides optimized services for users. In a distributed database system in the cloud, the connections necessary for a query layout may be stored in multiple sites, increasing the number of equivalent designs possible in the search for an optimal query implementation plan. However, a thorough search of all possible designs in such a large search space is not computationally rational. In this study, our goal is to identify a cost model consisting of a multi-objective function with variable (and possibly conflicting) QoS parameters to solve the query optimization problem in inhomogeneous cloud databases (in terms of pricing models) and mobile. Then, we propose a new strategy for the optimization of queries in these environments using the Learning Based Optimization (TLBO) algorithm. Finally, the results are evaluated in the CloudSim environment and compared with genetic optimization and ant colony optimization (ACO). | ||
| کلیدواژه‌ها | ||
| : cloud computing؛ distributed database؛ education-learning based optimization؛ query optimization Introduction | ||
| مراجع | ||
|
[1] A.E.J. Akbari, Z. Fathi, and M. Minouei, Identifying the effective components on promoting tax capacity in e-commerce and providing tools based on identified categories, Tobacco Regul. Sci. 8 (2022), no. 1, 1114–1142. [2] T. Alyas, A. Alzahrani, Y. Alsaawy, K. Alissa, Q. Abbas, and N. Tabassum, Query optimization framework for graph database in cloud dew environment, Comput. Mater. Contin. 74 (2023), no. 1, 2317–2330. [3] J.H. Christensen, Using RESTful web-services and cloud computing to create next-generation mobile applications, Proc. 24th ACM SIGPLAN Conf. Compan. Object Oriented Program. Syst. Lang. Appl., 2009, pp. 627–634. [4] H.T. Dinh, C. Lee, D. Niyato, and P. Wang, A survey of mobile cloud computing: architecture, applications, and approaches, Wireless Commun. Mobile Comput. 13 (2013), no. 18, 1587–1611. [5] D.B. Gordon and S.L. Mayo, Branch-and-terminate: A combinatorial optimization algorithm for protein design, Structure 7 (1999), no. 9, 1089–1098. [6] S. Gros, Distributed query optimization, Master’s thesis, https://api.semanticscholar.org/CorpusID:211528213, 2020. [7] F. Helff, L. Gruenwald, and L. d’Orazio, Weighted sum model for multi-objective query optimization for mobilecloud database environments, EDBT/ICDT Workshops, 2016. [8] L. Liu, R. Moulic and D. Shea, Cloud service portal for mobile device management, IEEE 7th Int. Conf. E-Bus. Engin., IEEE, 2010, pp. 474–478. [9] P. Michiardi, D. Carra, and S. Migliorini, Cache-based multi-query optimization for data-intensive scalable computing frameworks, Inf. Syst. Front. 23 (2021), 35–51. [10] V. Mishra and V. Singh, Generating optimal query plans for distributed query processing using teacher-learner based optimization, Proc. Comput. Sci. 54 (2015), 281–290. [11] T. Niknam, A. Kavousi, and A. Baziar, Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants, Energy 42 (2012), no. 1, 563–573. [12] A. Oludele and O. Oluwabukola, A survey of mobile cloud computing applications: Perspectives and challenges, 7th Int. Multi-Conf. Complex. Inf. Cybern. IMCIC 2016 7th Int. Conf. Soc. Inf. Technol. ICSIT 2016 Proc., 2016, pp. 238–243. [13] M.T. Ozsu and P. Valduriez, Principles of Distributed Database Systems, Prentice Hall, Englewood Cliffs, 1999. [14] M. Perrin, J. Mullen, F. Helff, L. Gruenwald, and L. d’Orazio, Time-, energy-, and monetary cost-aware cache design for a mobile-cloud database system, Biomedical Data Management and Graph Online Querying: VLDB 2015 Workshops, Big-O (Q) and DMAH, Waikoloa, HI, USA, Springer International Publishing, 2016, pp. 71–85. [15] R.V. Rao, V.J. Savsani, and D.P. Vakharia, Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems, CAD Comput. Aided Des. 43 (2011), no. 3, 303–315. [16] M. Satyanarayanan, Mobile computing: the next decade, Proc. 1st ACM Workshop on Mobile Cloud Comput. Serv.: Soc. Networks and Beyond (MCS), 2010, no 5. [17] M. Ul Hassan, A.A. Al-Awady, A. Ali, M.M. Iqbal, M. Akram, J. Khan, and A.A. AbuOdeh, An efficient dynamic decision-based task optimization and scheduling approach for microservice-based cost management in mobile cloud computing applications, Pervasive Mob. Comput. 92 (2023), 101785 | ||
|
آمار تعداد مشاهده مقاله: 77 تعداد دریافت فایل اصل مقاله: 72 |
||