Razzaq, Mirza Abdur and Mahar, Javed Ahmed and Ahmad, Muneer and Saher, Najia and Mehmood, Arif and Choi, Gyu Sang (2021) Hybrid auto-scaled service-cloud-based predictive workload modeling and analysis for smart campus system. IEEE Access, 9. pp. 42081-42089. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2021.3065597.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2019R1A2C1006159], Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) Support Program [IITP-2020-2016-0-00313] |
| Uncontrolled Keywords: | Scalability; Cloud computing; Servers; Internet of things; Education; Load modeling; Predictive models; Auto-scaling; cloud computing; horizontal scalability; the\~Internet of Things; predictive modeling; quality of service (QoS); smart campus; vertical scalability; workload |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Information Science |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 17 Jul 2022 07:02 |
| Last Modified: | 17 Jul 2022 07:02 |
| URI: | http://eprints.um.edu.my/id/eprint/28353 |
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