FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3136198539> ?p ?o ?g. }

• W3136198539 endingPage "42089" @default.
• W3136198539 startingPage "42081" @default.
• W3136198539 abstract "The Internet of Things is an emerging technology used in cloud computing and provides many services of the cloud. The cloud services users mostly suffer from service delays and disruptions due to service cloud resource management based on vertical and horizontal scalable systems. Adding more resources to a single cloud server is called vertical scaling, and an increasing number of servers is known as horizontal scaling. The service-bursts significantly impact the vertical scaled environment where the scale-up degrades the service quality and users' trust after reaching the server's maximum capacity. Besides, the horizontally scaled environment, though being resilient, is cost-inefficient. It is also hard to detect and manage bursts online to sustain application efficiency for complex workloads. Burst detection in real-time workloads is a complicated issue because even in the presence of auto-scaling methods, it can dramatically degrade the application's efficiency. This research study presents a new bursts-aware auto-scaling approach that detects bursts in dynamic workloads using resource estimation, decision-making scaling, and workload forecasting while reducing response time. This study proposes a hybrid auto-scaled service cloud model that ensures the best approximation of vertical and horizontal scalable systems to ensure Quality of Service (QoS) for smart campus-based applications. This study carries out the workload prediction and auto-scaling employing an ensemble algorithm. The model pre-scales the scalable vertical system by leveraging the service-load predictive modeling using an ensemble classification of defined workload estimation. The prediction of the upcoming workload helped scale-up the system, and auto-scaling dynamically scaled the assigned resources to many users' service requests. The proposed model efficiently managed service-bursts by addressing load balancing challenges through horizontal auto-scaling to ensure application consistency and service availability. The study simulated the smart campus environment model to monitor the time-stamped diverse service-requests appearing with different workloads." @default.
• W3136198539 created "2021-03-29" @default.
• W3136198539 creator A5044080887 @default.
• W3136198539 creator A5056548550 @default.
• W3136198539 creator A5081405486 @default.
• W3136198539 creator A5082033110 @default.
• W3136198539 creator A5087270783 @default.
• W3136198539 creator A5065999819 @default.
• W3136198539 date "2021-01-01" @default.
• W3136198539 modified "2023-10-17" @default.
• W3136198539 title "Hybrid Auto-Scaled Service-Cloud-Based Predictive Workload Modeling and Analysis for Smart Campus System" @default.
• W3136198539 cites W1970384873 @default.
• W3136198539 cites W2018836191 @default.
• W3136198539 cites W2167090833 @default.
• W3136198539 cites W2526683572 @default.
• W3136198539 cites W2591994024 @default.
• W3136198539 cites W2733889053 @default.
• W3136198539 cites W2792466321 @default.
• W3136198539 cites W2887327525 @default.
• W3136198539 cites W2903547652 @default.
• W3136198539 cites W2966520611 @default.
• W3136198539 cites W2977470299 @default.
• W3136198539 cites W2979365780 @default.
• W3136198539 cites W2998767370 @default.
• W3136198539 cites W2998993396 @default.
• W3136198539 cites W3005719296 @default.
• W3136198539 cites W3006423581 @default.
• W3136198539 cites W3015028018 @default.
• W3136198539 cites W3023238978 @default.
• W3136198539 cites W3023451744 @default.
• W3136198539 cites W3027366302 @default.
• W3136198539 cites W3030214482 @default.
• W3136198539 cites W3035619533 @default.
• W3136198539 cites W3036276721 @default.
• W3136198539 cites W3037362184 @default.
• W3136198539 cites W3041171118 @default.
• W3136198539 cites W3048785087 @default.
• W3136198539 doi "https://doi.org/10.1109/access.2021.3065597" @default.
• W3136198539 hasPublicationYear "2021" @default.
• W3136198539 type Work @default.
• W3136198539 sameAs 3136198539 @default.
• W3136198539 citedByCount "5" @default.
• W3136198539 countsByYear W31361985392021 @default.
• W3136198539 countsByYear W31361985392022 @default.
• W3136198539 countsByYear W31361985392023 @default.
• W3136198539 crossrefType "journal-article" @default.
• W3136198539 hasAuthorship W3136198539A5044080887 @default.
• W3136198539 hasAuthorship W3136198539A5056548550 @default.
• W3136198539 hasAuthorship W3136198539A5065999819 @default.
• W3136198539 hasAuthorship W3136198539A5081405486 @default.
• W3136198539 hasAuthorship W3136198539A5082033110 @default.
• W3136198539 hasAuthorship W3136198539A5087270783 @default.
• W3136198539 hasBestOaLocation W31361985391 @default.
• W3136198539 hasConcept C111919701 @default.
• W3136198539 hasConcept C120314980 @default.
• W3136198539 hasConcept C2778476105 @default.
• W3136198539 hasConcept C31258907 @default.
• W3136198539 hasConcept C41008148 @default.
• W3136198539 hasConcept C48044578 @default.
• W3136198539 hasConcept C5119721 @default.
• W3136198539 hasConcept C77088390 @default.
• W3136198539 hasConcept C79403827 @default.
• W3136198539 hasConcept C79974875 @default.
• W3136198539 hasConcept C93996380 @default.
• W3136198539 hasConceptScore W3136198539C111919701 @default.
• W3136198539 hasConceptScore W3136198539C120314980 @default.
• W3136198539 hasConceptScore W3136198539C2778476105 @default.
• W3136198539 hasConceptScore W3136198539C31258907 @default.
• W3136198539 hasConceptScore W3136198539C41008148 @default.
• W3136198539 hasConceptScore W3136198539C48044578 @default.
• W3136198539 hasConceptScore W3136198539C5119721 @default.
• W3136198539 hasConceptScore W3136198539C77088390 @default.
• W3136198539 hasConceptScore W3136198539C79403827 @default.
• W3136198539 hasConceptScore W3136198539C79974875 @default.
• W3136198539 hasConceptScore W3136198539C93996380 @default.
• W3136198539 hasFunder F4320322120 @default.
• W3136198539 hasFunder F4320328359 @default.
• W3136198539 hasLocation W31361985391 @default.
• W3136198539 hasOpenAccess W3136198539 @default.
• W3136198539 hasPrimaryLocation W31361985391 @default.
• W3136198539 hasRelatedWork W1980717413 @default.
• W3136198539 hasRelatedWork W2030385910 @default.
• W3136198539 hasRelatedWork W2046698369 @default.
• W3136198539 hasRelatedWork W2057320978 @default.
• W3136198539 hasRelatedWork W2279849495 @default.
• W3136198539 hasRelatedWork W2963616097 @default.
• W3136198539 hasRelatedWork W3010663160 @default.
• W3136198539 hasRelatedWork W3210777720 @default.
• W3136198539 hasRelatedWork W3210966092 @default.
• W3136198539 hasRelatedWork W4226134848 @default.
• W3136198539 hasVolume "9" @default.
• W3136198539 isParatext "false" @default.
• W3136198539 isRetracted "false" @default.
• W3136198539 magId "3136198539" @default.
• W3136198539 workType "article" @default.