Connecting objects have increasingly become popular in recent years, leading to the connection of more than 50 billion objects by the end of 2020. This large number of objects will generate a huge amount of data that is currently being processed and stored in the cloud. Fog Computing presents a promising solution to the problems of high latency and huge network traffic encountered in the cloud. As Fog’s infrastructures are dense, heterogeneous and geo-distributed, managing the data in order to satisfy users demand in such context is very complicated. In this work, we propose a data management strategy called ‘RMS-HaFC’ in which we consider the characteristics of Fog Computing environment. To do so, we proposed a hierarchical multi-layer model, on which we designed a migration and replication strategy based on data popularity. These strategies duplicate files dynamically and store them in different locations to improve the response time of users requests and minimize the system energy consumption without loading network usage. The strategy was evaluated using the iFogSim simulator and the experimental results obtained are very promising.
{"title":"A replication and migration strategy on the hierarchical architecture in the fog computing environment","authors":"Ahmed Berkennou, Ghalem Belalem, Said Limam","doi":"10.3233/mgs-200333","DOIUrl":"https://doi.org/10.3233/mgs-200333","url":null,"abstract":"Connecting objects have increasingly become popular in recent years, leading to the connection of more than 50 billion objects by the end of 2020. This large number of objects will generate a huge amount of data that is currently being processed and stored in the cloud. Fog Computing presents a promising solution to the problems of high latency and huge network traffic encountered in the cloud. As Fog’s infrastructures are dense, heterogeneous and geo-distributed, managing the data in order to satisfy users demand in such context is very complicated. In this work, we propose a data management strategy called ‘RMS-HaFC’ in which we consider the characteristics of Fog Computing environment. To do so, we proposed a hierarchical multi-layer model, on which we designed a migration and replication strategy based on data popularity. These strategies duplicate files dynamically and store them in different locations to improve the response time of users requests and minimize the system energy consumption without loading network usage. The strategy was evaluated using the iFogSim simulator and the experimental results obtained are very promising.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"79 1","pages":"291-307"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81397973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jihed Elouni, Hamdi Ellouzi, Hela Ltifi, Mounir Ben Ayed
{"title":"Intelligent health monitoring system modeling based on machine learning and agent technology","authors":"Jihed Elouni, Hamdi Ellouzi, Hela Ltifi, Mounir Ben Ayed","doi":"10.3233/MGS-200329","DOIUrl":"https://doi.org/10.3233/MGS-200329","url":null,"abstract":"","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"24 Suppl 2 1","pages":"207-226"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73062223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Context-aware multi-agent planning in intelligent environments","authors":"Houda Haiouni, R. Maamri","doi":"10.3233/mgs-190310","DOIUrl":"https://doi.org/10.3233/mgs-190310","url":null,"abstract":"","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"37 1","pages":"219-236"},"PeriodicalIF":0.7,"publicationDate":"2019-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90298188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the drawbacks of glowworm swarm optimisation (GSO) is its premature convergence, which leaves it often ineffective for solving complex practical problems. This paper proposes a new hybrid metaheuristic algorithm, that is, moth-flame glowworm swarm optimisation (MFGSO). The main idea of the hybrid algorithm is to combine the exploration ability in moth-flame optimisation (MFO) with the exploitation ability in GSO. Performance evaluations are conducted on benchmarking test functions in comparison with the basic GSO and other metaheuristic algorithms. The results show that MFGSO outperforms the basic GSO and other metaheuristic algorithms on most test functions in terms of local optima avoidance and convergence speed.
{"title":"Moth-flame glowworm swarm optimisation","authors":"D. Alboaneen, H. Tianfield, Yan Zhang","doi":"10.3233/mgs-190314","DOIUrl":"https://doi.org/10.3233/mgs-190314","url":null,"abstract":"One of the drawbacks of glowworm swarm optimisation (GSO) is its premature convergence, which leaves it often ineffective for solving complex practical problems. This paper proposes a new hybrid metaheuristic algorithm, that is, moth-flame glowworm swarm optimisation (MFGSO). The main idea of the hybrid algorithm is to combine the exploration ability in moth-flame optimisation (MFO) with the exploitation ability in GSO. Performance evaluations are conducted on benchmarking test functions in comparison with the basic GSO and other metaheuristic algorithms. The results show that MFGSO outperforms the basic GSO and other metaheuristic algorithms on most test functions in terms of local optima avoidance and convergence speed.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"18 1","pages":"305-326"},"PeriodicalIF":0.7,"publicationDate":"2019-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74192071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A multi-agent security framework for cloud data storage","authors":"Oussama Arki, Abdelhafid Zitouni, A. Dib","doi":"10.3233/MGS-180296","DOIUrl":"https://doi.org/10.3233/MGS-180296","url":null,"abstract":"","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"6 1","pages":"357-382"},"PeriodicalIF":0.7,"publicationDate":"2019-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79804130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient and scalable outsourced data access control with user revocation in cloud: A comprehensive study","authors":"S. Debnath, B. Bhuyan","doi":"10.3233/MGS-180297","DOIUrl":"https://doi.org/10.3233/MGS-180297","url":null,"abstract":"","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"131 1","pages":"383-401"},"PeriodicalIF":0.7,"publicationDate":"2019-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78134759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automatic generating algorithm of rewriting logic specification for multi-agent system models based on Petri nets","authors":"A. Boucherit, Abdallah Khababa, Laura M. Castro","doi":"10.3233/MGS-180298","DOIUrl":"https://doi.org/10.3233/MGS-180298","url":null,"abstract":"","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"45 1","pages":"403-418"},"PeriodicalIF":0.7,"publicationDate":"2019-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85176371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A multi-paradigm approach to model and verify mobile agent software systems","authors":"A. Belghiat, A. Chaoui","doi":"10.3233/MGS-180295","DOIUrl":"https://doi.org/10.3233/MGS-180295","url":null,"abstract":"","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"9 1","pages":"337-356"},"PeriodicalIF":0.7,"publicationDate":"2019-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79908837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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