Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283515794> ?p ?o ?g. }
- W4283515794 endingPage "374" @default.
- W4283515794 startingPage "362" @default.
- W4283515794 abstract "Edge computing can greatly decrease the delay between users and cloud servers, which can significantly improve system service performance. However, it remains challenging for more efficient scheduling and allocation of users’ application demands with dependence constraints to edge cloud servers. Due to the randomness of the initial population, traditional intelligent optimization algorithms have poor convergence speed in addressing resource scheduling. Therefore, to minimize the execution time of the application, this paper proposes a hybrid algorithm to solve the resource scheduling problem with parallelism and subtask dependency. To improve the convergence speed of the algorithm, this paper makes full use of the features of deep Q networks (DQN) and genetic algorithms (GA). The initial population of GA is generated using DQN. Finally, to evaluate the effectiveness of our proposed algorithm, this paper selects three real scientific workflows for experiments. The experimental results show that the hybrid algorithm can converge quickly and improve the optimization effect in a short time." @default.
- W4283515794 created "2022-06-27" @default.
- W4283515794 creator A5015577864 @default.
- W4283515794 creator A5042511971 @default.
- W4283515794 creator A5047654219 @default.
- W4283515794 creator A5053085908 @default.
- W4283515794 creator A5080552736 @default.
- W4283515794 date "2022-08-01" @default.
- W4283515794 modified "2023-10-03" @default.
- W4283515794 title "A deep reinforcement learning based hybrid algorithm for efficient resource scheduling in edge computing environment" @default.
- W4283515794 cites W2126105956 @default.
- W4283515794 cites W2257979135 @default.
- W4283515794 cites W2580909119 @default.
- W4283515794 cites W2591182809 @default.
- W4283515794 cites W2746399491 @default.
- W4283515794 cites W2796394805 @default.
- W4283515794 cites W2897066997 @default.
- W4283515794 cites W2905287427 @default.
- W4283515794 cites W2907867704 @default.
- W4283515794 cites W2917147437 @default.
- W4283515794 cites W2918702815 @default.
- W4283515794 cites W2920916739 @default.
- W4283515794 cites W2921018545 @default.
- W4283515794 cites W2968052242 @default.
- W4283515794 cites W2981604716 @default.
- W4283515794 cites W2990913508 @default.
- W4283515794 cites W2995201943 @default.
- W4283515794 cites W3000708199 @default.
- W4283515794 cites W3007564224 @default.
- W4283515794 cites W3015267180 @default.
- W4283515794 cites W3015815948 @default.
- W4283515794 cites W3016426533 @default.
- W4283515794 cites W3022500044 @default.
- W4283515794 cites W3033881824 @default.
- W4283515794 cites W3036631744 @default.
- W4283515794 cites W3040914594 @default.
- W4283515794 cites W3047538493 @default.
- W4283515794 cites W3082502178 @default.
- W4283515794 cites W3084357507 @default.
- W4283515794 cites W3089883657 @default.
- W4283515794 cites W3103537989 @default.
- W4283515794 cites W3108259217 @default.
- W4283515794 cites W3119972824 @default.
- W4283515794 cites W3128183952 @default.
- W4283515794 cites W3168774276 @default.
- W4283515794 cites W3176466636 @default.
- W4283515794 cites W3187715211 @default.
- W4283515794 cites W3193908934 @default.
- W4283515794 cites W3214405322 @default.
- W4283515794 cites W4226155471 @default.
- W4283515794 doi "https://doi.org/10.1016/j.ins.2022.06.078" @default.
- W4283515794 hasPublicationYear "2022" @default.
- W4283515794 type Work @default.
- W4283515794 citedByCount "3" @default.
- W4283515794 countsByYear W42835157942022 @default.
- W4283515794 countsByYear W42835157942023 @default.
- W4283515794 crossrefType "journal-article" @default.
- W4283515794 hasAuthorship W4283515794A5015577864 @default.
- W4283515794 hasAuthorship W4283515794A5042511971 @default.
- W4283515794 hasAuthorship W4283515794A5047654219 @default.
- W4283515794 hasAuthorship W4283515794A5053085908 @default.
- W4283515794 hasAuthorship W4283515794A5080552736 @default.
- W4283515794 hasConcept C105795698 @default.
- W4283515794 hasConcept C111919701 @default.
- W4283515794 hasConcept C11413529 @default.
- W4283515794 hasConcept C120314980 @default.
- W4283515794 hasConcept C125112378 @default.
- W4283515794 hasConcept C126255220 @default.
- W4283515794 hasConcept C144024400 @default.
- W4283515794 hasConcept C149923435 @default.
- W4283515794 hasConcept C154945302 @default.
- W4283515794 hasConcept C206729178 @default.
- W4283515794 hasConcept C2908647359 @default.
- W4283515794 hasConcept C31258907 @default.
- W4283515794 hasConcept C33923547 @default.
- W4283515794 hasConcept C41008148 @default.
- W4283515794 hasConcept C58758708 @default.
- W4283515794 hasConcept C79974875 @default.
- W4283515794 hasConcept C85617194 @default.
- W4283515794 hasConcept C93996380 @default.
- W4283515794 hasConcept C97541855 @default.
- W4283515794 hasConceptScore W4283515794C105795698 @default.
- W4283515794 hasConceptScore W4283515794C111919701 @default.
- W4283515794 hasConceptScore W4283515794C11413529 @default.
- W4283515794 hasConceptScore W4283515794C120314980 @default.
- W4283515794 hasConceptScore W4283515794C125112378 @default.
- W4283515794 hasConceptScore W4283515794C126255220 @default.
- W4283515794 hasConceptScore W4283515794C144024400 @default.
- W4283515794 hasConceptScore W4283515794C149923435 @default.
- W4283515794 hasConceptScore W4283515794C154945302 @default.
- W4283515794 hasConceptScore W4283515794C206729178 @default.
- W4283515794 hasConceptScore W4283515794C2908647359 @default.
- W4283515794 hasConceptScore W4283515794C31258907 @default.
- W4283515794 hasConceptScore W4283515794C33923547 @default.
- W4283515794 hasConceptScore W4283515794C41008148 @default.
- W4283515794 hasConceptScore W4283515794C58758708 @default.
- W4283515794 hasConceptScore W4283515794C79974875 @default.
- W4283515794 hasConceptScore W4283515794C85617194 @default.