Academic Journal
Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme
| Τίτλος: | Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme |
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| Συγγραφείς: | Danlami Gabi, Nasiru Muhammad Dankolo, Abubakar Atiku Muslim, Ajith Abraham, Muhammad Usman Joda, Anazida Zainal, Zalmiyah Zakaria |
| Πηγή: | Neural Computing and Applications. 34:14085-14105 |
| Στοιχεία εκδότη: | Springer Science and Business Media LLC, 2022. |
| Έτος έκδοσης: | 2022 |
| Θεματικοί όροι: | FOS: Computer and information sciences, CloudSim, Cloud datacenter, Computer Networks and Communications, QA75 Electronic computers. Computer science, Premature convergence, Edge datacenter, Metaheuristic, Local optimum, 02 engineering and technology, Cloud Computing and Big Data Technologies, Optimizing Information Freshness in Communication Networks, Simulated annealing, Bottleneck, 12. Responsible consumption, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Edge Computing, Cloud computing, Embedded system, Computer Sciences, Internet of Things and Edge Computing, Particle swarm optimization, Mathematical optimization, Scalability, Scheduling Policies, Computer science, Distributed computing, Mobile Edge Computing, Algorithm, Operating system, Datavetenskap (datalogi), Computer Science, Physical Sciences, Mobile edge computing, Mobile edge clouds, Mathematics, Fruit fly optimization, Information Systems |
| Περιγραφή: | Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers. |
| Τύπος εγγράφου: | Article Other literature type |
| Περιγραφή αρχείου: | application/pdf |
| Γλώσσα: | English |
| ISSN: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-022-07260-y |
| DOI: | 10.60692/568np-nta31 |
| DOI: | 10.60692/wvhm4-2t822 |
| Σύνδεσμος πρόσβασης: | http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-194467 |
| Rights: | CC BY |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....e0d02b979a091d58935f654cf0eee8e5 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 14333058 09410643 |
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| DOI: | 10.1007/s00521-022-07260-y |