Intelligent process migration in heterogeneous distributed systems

Terecio Diosnel  Marecos Brizuela; David Luis  La Red Martínez; Federico  Agostini; Jorge Tomás  Fornerón Martínez

doi:10.59400/cai2018

Intelligent process migration in heterogeneous distributed systems

Terecio Diosnel Marecos Brizuela Faculty of Applied Sciences, National University of Pilar, Pilar 120101, Paraguay
David Luis La Red Martínez Faculty of Exact and Natural Sciences and Surveying, Northeastern National University, Corrientes W3400, Argentine
Federico Agostini Faculty of Exact and Natural Sciences and Surveying, Northeastern National University, Corrientes W3400, Argentine
Jorge Tomás Fornerón Martínez Faculty of Applied Sciences, National University of Pilar, Pilar 120101, Paraguay

Article ID: 2018

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DOI: https://doi.org/10.59400/cai2018

Keywords: migration; load balancing; synchronization; distributed systems; mutual exclusion

Abstract

In distributed processing environments, multiple groups of processes are found sharing resources and competing for access. These processes may or may not require synchronization and it is essential to reach a consensus to manage access to resources in a way that establishes a strict order, thus ensuring mutual exclusion. The proposal presented is an innovative and effective solution for the management of shared resources in distributed systems, which allows solving problems related to overload and workload balancing. The evaluation of the state of computational loads and the final comparison using standard deviation are useful tools to detect and correct imbalances in the system. In addition, the possibility of establishing initial configurations of the algorithm for each particular situation allows adapting the solution to the specific needs of each system.

References

[1]La Red Martínez DL. Aggregation Operator for Assignment of Resources in Distributed Systems. International Journal of Advanced Computer Science and Applications. 2017; 8(10). doi: 10.14569/ijacsa.2017.081053

[2]Ricart G, Agrawala AK. An optimal algorithm for mutual exclusion in computer networks. Communications of the ACM. 1981; 24(1): 9-17. doi: 10.1145/358527.358537

[3]Guohong C, Singhal M. A delay-optimal quorum-based mutual exclusion algorithm for distributed systems. IEEE Transactions on Parallel and Distributed Systems. 2001; 12(12): 1256-1268. doi: 10.1109/71.970560

[4]Lodha S, Kshemkalyani A. A fair distributed mutual exclusion algorithm. IEEE Transactions on Parallel and Distributed Systems. 2000; 11(6): 537-549. doi: 10.1109/71.862205

[5]la Red Martínez DL, Agostini F, Acosta JC, et al. Simulator for the evaluation of algorithms for the management of shared resources in distributed systems (Spanish). Revista de Investigación en Tecnologías de la Información. 2022; 10(20): 62-79. doi: 10.36825/riti.10.20.006

[6]La Red Martínez DL, Acosta JC, Agostini F. Assignment of Resources in Distributed Systems. Proceedings of IMCIC 2018-9th International Multi-Conference on Complexity, Informatics and Cybernetics.

[7]Agostini F, La Red Martínez DL. Allocation of shared resources. In: Proceedings of 2019 14th Iberian Conference on Information Systems and Technologies (CISTI); 19-22 June 2019; Coimbra, Portugal.

[8]Agostini F, la Red Martínez DL, Acosta JC. Assignment of Resources in Distributed Systems with Strict Consensus Requirements. Proceedings of the IMCIC 2019-10th International Multi-Conference on Complexity, Informatics and Cybernetics.

[9]Agostini F. New Proposal for the Management of Resources and Processes in Distributed Systems (Spanish). Universidad Nacional del Nordeste; 2019.

[10]Beiruti MA, Ganjali Y. Load Migration in Distributed SDN Controllers. NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium. 2020; 1-9. doi: 10.1109/noms47738.2020.9110292

[11]Upadhyay A, Lakkadwala P. Migration of over loaded process and schedule for resource utilization in Cloud Computing. In: Proceedings of 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions); 02-04 September 2015; Noida, India.

[12]Deshmukh SC, Deshmukh SS. Improved load balancing for distributed file system using self acting and adaptive loading data migration process. In: Proceedings of 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions); 2-4 September 2015; Noida, India.

[13]Junaidi J, Wibowo P, Yuniasri D, et al. Applied machine learning in load balancing. JUTI: Jurnal Ilmiah Teknologi Informasi. 2020; 18(2): 76. doi: 10.12962/j24068535.v18i2.a940

[14]Liu H, Jin H, Xu CZ, et al. Performance and energy modeling for live migration of virtual machines. Cluster Computing. 2011; 16(2): 249-264. doi: 10.1007/s10586-011-0194-3

[15]Asadi AN, Azgomi MA, Entezari-Maleki R. Analytical evaluation of resource allocation algorithms and process migration methods in virtualized systems. Sustainable Computing: Informatics and Systems. 2020; 25: 100370. doi: 10.1016/j.suscom.2019.100370

[16]Chiclana F, Herrera F. & Herrera-Viedma E. Integrating Multiplicative Preference Relations in a Multipurpose Decision-Making Model Based on Fuzzy Preference Relations. Fuzzy Sets and Systems. 2001; 122(2). doi: 10.1016/S0165-0114(00)00004-X

[17]Dong Y, Zhang H, Herrera-Viedma E. Consensus reaching model in the complex and dynamic MAGDM problem. Knowledge-Based Systems. 2016; 106: 206-219. doi: 10.1016/j.knosys.2016.05.046

[18]Sohrabi Z, Mousavi Khaneghah E. Challenges of using live process migration in distributed exascale systems. Azerbaijan Journal of High Performance Computing. 2020; 3(2): 151-163. doi: 10.32010/26166127.2020.3.2.151.163

[19]Rathore N, Chana I. Load Balancing and Job Migration Techniques in Grid: A Survey of Recent Trends. Wireless Personal Communications. 2014; 79(3): 2089-2125. doi: 10.1007/s11277-014-1975-9

[20]Chang C, Hadachi A, Srirama SN. Adaptive Edge Process Migration for IoT in Heterogeneous Fog and Edge Computing Environments. International Journal of Mobile Computing and Multimedia Communications. 2020; 11(3): 1-21. doi: 10.4018/ijmcmc.2020070101

[21]Marecos TD, Agostini, F, La Red Martínez D. Controlled migration of processes in distributed systems (Spanish). Proceedings of Memorias del Encuentro Argentino de Ingeniería, 6º Congreso Argentino de Ingeniería y 12º Congreso Argentino de Enseñanza de Ingeniería.

[22]Fornerón Martínez JT, Agostini F, La Red Martínez DL. Resource and Process Management With a Decision Model Based on Fuzzy Logic. International Journal of Interactive Multimedia and Artificial Intelligence. 2023; 8(2): 134. doi: 10.9781/ijimai.2023.02.009

[23]Bishop M, Valence M, Winiewski LF. Process migration for heterogeneous distributed systems. Dartmouth; 1995.

[24]Cao J, Yu Z, Xue B. Research on collaborative edge network service migration strategy based on crowd clustering. Scientific Reports. 2024; 14(1). doi: 10.1038/s41598-024-58048-0

[25]Kommisetty PDNK, Abhireddy N. Cloud Migration Strategies: Ensuring Seamless Integration and Scalability in Dynamic Business Environments. International Journal of Engineering and Computer Science. 2024; 13(04): 26146-26156. doi: 10.18535/ijecs/v13i04.4812

[26]Nama P, Pattanayak S, Meka HS. AI-driven innovations in cloud computing: Transforming scalability, resource management, and predictive analytics in distributed systems. International Research Journal of Modernization in Engineering Technology and Science. 2023; 5(12): 4165-4174.

[27]Devan M, Shanmugam L, Tomar M. AI-powered data migration strategies for cloud environments: Techniques, frameworks, and real-world applications. Australian Journal of Machine Learning Research & Applications. 2021; 1(2): 79-111.

[28]Rathore N, Chana I. A cognitive analysis of load balancing and job migration technique in Grid. 2011 World Congress on Information and Communication Technologies. 2011; 77-82. doi: 10.1109/wict.2011.6141221

[29]Hung LH, Wu CH, Tsai CH, et al. Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods. IEEE Access. 2021; 9: 49760-49773. doi: 10.1109/access.2021.3065170

[30]Negi S, Rauthan MMS, Vaisla KS, et al. CMODLB: an efficient load balancing approach in cloud computing environment. The Journal of Supercomputing. 2021; 77(8): 8787-8839. doi: 10.1007/s11227-020-03601-7

[31]Grosu D, Chronopoulos AT. A game-theoretic model and algorithm for load balancing in distributed systems. In: Proceedings of 16th International Parallel and Distributed Processing Symposium; 15-19 April 2002; Ft. Lauderdale, FL, USA.

[32]Siar H, Kiani K, Chronopoulos AT. A combination of game theory and genetic algorithm for load balancing in distributed computer systems. International Journal of Advanced Intelligence Paradigms. 2017; 9(1): 82. doi: 10.1504/ijaip.2017.081181

[33]Tan T, Chen X, Li C, et al. Load balancing-oriented fault-tolerant NoC design. 2024 IEEE International Test Conference in Asia (ITC-Asia). 2024; 1-6. doi: 10.1109/itc-asia62534.2024.10661350

[34]He Q, Chen Y, Dong Y, et al. Efficient Load Balance Algorithm for Network-on-Chip Mapping. In: Proceedings of 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC); 22-24 April 2022; Suzhou, China.

[35]Romanov A, Myachin N, Sukhov A. Fault-Tolerant Routing in Networks-on-Chip Using Self-Organizing Routing Algorithms. IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. 2021; 1-6. doi: 10.1109/iecon48115.2021.9589829

[36]Gogoi A, Ghoshal B, Manna K. Fault-aware routing approach for mesh-based Network-on-Chip architecture. Integration. 2023; 93: 102043. doi: 10.1016/j.vlsi.2023.05.007

[37]Yager R. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics. 1988; 18(1): 183-190. doi: 10.1109/21.87068

[38]Yager R. Families of OWA operators. Fuzzy Sets and Systems. 1993; 59: 125-148. doi: 10.1016/0165-0114(93)90194-M

Published

2024-12-18

How to Cite

Marecos Brizuela, T. D., La Red Martínez, D. L., Agostini, F., & Fornerón Martínez, J. T. (2024). Intelligent process migration in heterogeneous distributed systems. Computing and Artificial Intelligence, 3(1), 2018. https://doi.org/10.59400/cai2018

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