Evolutionary offloading in an edge environment
Due to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-09-01
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| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866520301559 |
| _version_ | 1818360981987786752 |
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| author | Samah A. Zakaryia Safaa A. Ahmed Mohamed K. Hussein |
| author_facet | Samah A. Zakaryia Safaa A. Ahmed Mohamed K. Hussein |
| author_sort | Samah A. Zakaryia |
| collection | DOAJ |
| description | Due to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at the edge of wireless networks. However, improper placement of mobile tasks on the edge servers may increase the waiting time and the transmission time. This, in turn, will increase the response time, and eventually violates the quality of service.This paper proposes an effective offloading strategy in a mobile edge environment using the queuing networks and an evolutionary algorithm, namely the genetic algorithm (GA). The queuing network is used to model the waiting time and the service time of the mobile tasks on the edge servers. The genetic algorithm finds the best allocation of mobile tasks on the edge servers to minimize tasks response time considering the transmission times and the load conditions on edge servers represented by the waiting times and the service times which are calculated using the queuing network. The proposed GA-based offloading algorithm is compared with another evolutionary algorithm, namely particle swarm optimization (PSO). Experimental evaluations show that the GA-based offloading algorithm outperforms both of round robin offloading and the PSO-based offloading algorithms, and effectively improves mobile applications response time. |
| first_indexed | 2024-12-13T21:09:26Z |
| format | Article |
| id | doaj.art-f46e8aff8072400e949ad7b136ad27ea |
| institution | Directory Open Access Journal |
| issn | 1110-8665 |
| language | English |
| last_indexed | 2024-12-13T21:09:26Z |
| publishDate | 2021-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj.art-f46e8aff8072400e949ad7b136ad27ea2022-12-21T23:31:23ZengElsevierEgyptian Informatics Journal1110-86652021-09-01223257267Evolutionary offloading in an edge environmentSamah A. Zakaryia0Safaa A. Ahmed1Mohamed K. Hussein2Corresponding author.; Department of Computer Science, Faculty of Computers and Informatics, Ismailia, EgyptDepartment of Computer Science, Faculty of Computers and Informatics, Ismailia, EgyptDepartment of Computer Science, Faculty of Computers and Informatics, Ismailia, EgyptDue to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at the edge of wireless networks. However, improper placement of mobile tasks on the edge servers may increase the waiting time and the transmission time. This, in turn, will increase the response time, and eventually violates the quality of service.This paper proposes an effective offloading strategy in a mobile edge environment using the queuing networks and an evolutionary algorithm, namely the genetic algorithm (GA). The queuing network is used to model the waiting time and the service time of the mobile tasks on the edge servers. The genetic algorithm finds the best allocation of mobile tasks on the edge servers to minimize tasks response time considering the transmission times and the load conditions on edge servers represented by the waiting times and the service times which are calculated using the queuing network. The proposed GA-based offloading algorithm is compared with another evolutionary algorithm, namely particle swarm optimization (PSO). Experimental evaluations show that the GA-based offloading algorithm outperforms both of round robin offloading and the PSO-based offloading algorithms, and effectively improves mobile applications response time.http://www.sciencedirect.com/science/article/pii/S1110866520301559Mobile edge computingComputation offloadingGenetic algorithm optimizationEvolutionary optimizationParticle swarm optimization |
| spellingShingle | Samah A. Zakaryia Safaa A. Ahmed Mohamed K. Hussein Evolutionary offloading in an edge environment Egyptian Informatics Journal Mobile edge computing Computation offloading Genetic algorithm optimization Evolutionary optimization Particle swarm optimization |
| title | Evolutionary offloading in an edge environment |
| title_full | Evolutionary offloading in an edge environment |
| title_fullStr | Evolutionary offloading in an edge environment |
| title_full_unstemmed | Evolutionary offloading in an edge environment |
| title_short | Evolutionary offloading in an edge environment |
| title_sort | evolutionary offloading in an edge environment |
| topic | Mobile edge computing Computation offloading Genetic algorithm optimization Evolutionary optimization Particle swarm optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1110866520301559 |
| work_keys_str_mv | AT samahazakaryia evolutionaryoffloadinginanedgeenvironment AT safaaaahmed evolutionaryoffloadinginanedgeenvironment AT mohamedkhussein evolutionaryoffloadinginanedgeenvironment |