Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing
Distributed object computing is widely envisioned to be the desired distributed software development paradigm due to the higher modularity and the capability of handling machine and operating system heterogeneity. In this paper, we address the issue of judicious load balancing in distributed object...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| التنسيق: | article |
| منشور في: |
2006
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://eprints.kfupm.edu.sa/id/eprint/2642/1/LNCS-Helmy-Abstract.pdf |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513391166488577 |
|---|---|
| author | Helmy, Tarek |
| author2 | Shahab, S.A. unknown |
| author2_role | author author |
| author_facet | Helmy, Tarek Shahab, S.A. unknown |
| author_role | author |
| dc.creator.none.fl_str_mv | Helmy, Tarek Shahab, S.A. unknown |
| dc.date.none.fl_str_mv | 2006 2020 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/2642/1/LNCS-Helmy-Abstract.pdf (2006) Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing. LNCS, 3947. pp. 488-497. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Springer |
| dc.relation.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/2642/ |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Computer |
| dc.title.none.fl_str_mv | Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing |
| dc.type.none.fl_str_mv | Article PeerReviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Distributed object computing is widely envisioned to be the desired distributed software development paradigm due to the higher modularity and the capability of handling machine and operating system heterogeneity. In this paper, we address the issue of judicious load balancing in distributed object computing systems. In order to decrease response time and to utilize services effectively, we have proposed and implemented a new technique based on machine learning for adaptive and flexible load balancing mechanism within the framework of distributed middleware. We have chosen Jini 2.0 to build our experimental middleware platform, on which our proposed approach as well as other related techniques are implemented and compared. Extensive experiments are conducted to investigate the effectiveness of the proposed technique, which is found to be consistently better in comparison with existing techniques. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | KFUPM_81df9bcaed7822004e3d315c364e78ec |
| identifier_str_mv | (2006) Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing. LNCS, 3947. pp. 488-497. |
| language_invalid_str_mv | en |
| network_acronym_str | KFUPM |
| network_name_str | King Fahd University of Petroleum and Minerals |
| oai_identifier_str | oai::2642 |
| publishDate | 2006 |
| publisher.none.fl_str_mv | Springer |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object ComputingHelmy, TarekShahab, S.A.unknownComputerDistributed object computing is widely envisioned to be the desired distributed software development paradigm due to the higher modularity and the capability of handling machine and operating system heterogeneity. In this paper, we address the issue of judicious load balancing in distributed object computing systems. In order to decrease response time and to utilize services effectively, we have proposed and implemented a new technique based on machine learning for adaptive and flexible load balancing mechanism within the framework of distributed middleware. We have chosen Jini 2.0 to build our experimental middleware platform, on which our proposed approach as well as other related techniques are implemented and compared. Extensive experiments are conducted to investigate the effectiveness of the proposed technique, which is found to be consistently better in comparison with existing techniques.Springer20062020ArticlePeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://eprints.kfupm.edu.sa/id/eprint/2642/1/LNCS-Helmy-Abstract.pdf (2006) Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing. LNCS, 3947. pp. 488-497. enhttps://eprints.kfupm.edu.sa/id/eprint/2642/info:eu-repo/semantics/openAccessoai::26422019-11-01T13:45:45Z |
| spellingShingle | Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing Helmy, Tarek Computer |
| status_str | publishedVersion |
| title | Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing |
| title_full | Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing |
| title_fullStr | Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing |
| title_full_unstemmed | Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing |
| title_short | Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing |
| title_sort | Machine Learning-Based Adaptive Load Balancing Framework for Distributed Object Computing |
| topic | Computer |
| url | https://eprints.kfupm.edu.sa/id/eprint/2642/1/LNCS-Helmy-Abstract.pdf |