| Autor: |
Mazen Farid, Rohaya Latip, Masnida Hussin, Nor Asilah Wati Abdul Hamid |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Symmetry, Vol 12, Iss 4, p 551 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2073-8994 |
| DOI: |
10.3390/sym12040551 |
| Popis: |
Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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