The 3-axis scalable service-cloud resource modeling for burst prediction under smart campus scenario

Razzaq, Mirza Abdur and Mahar, Javed Ahmed and Ahmad, Muneer and Ali, Ihsan and Alroobaea, Roobaea and Almansour, Fahad and Roy, Kumarmangal (2021) The 3-axis scalable service-cloud resource modeling for burst prediction under smart campus scenario. IEEE Access, 9. pp. 116927-116941. ISSN 2169-3536, DOI

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Internet of Things (IoT) enables smart campuses more convenient for cloud services. The availability of cloud resources to its users appears as a fundamental challenge. The existing research presents several auto-scaling techniques to scale the resources with the increase in users' demands. However, still, the cloud users of auto-scaled servers experience service disruption, delayed responses, and the occurrence of service bursts. The prevailing burst management framework exhibits limitations in the context of burdening the existing auto-scaled machines for cost estimation and resource allocation. This research presents a 3-axis auto-scaling framework for load balancing and resource allocation by incorporating a dedicated cost estimator and allocator (on the z-axis). The cost estimation server develops a log of existing load estimates of vertical and horizontal servers and scales the new users' requests in case the vertical threshold is breached with new requests. The cost estimator, in its data structure, keeps track of the current resources available at both vertical and horizontal servers. The historical information of available resources and the new resources' requests is decided by the cost estimator as per demand and supply scenario. The general characteristics of servers are resources pooling, requests queue development, burst identification, automatic scaling, and load balancing. The cost estimator also prioritizes vertical servers for resource allocations, and switches to the horizontal server when the vertical server reaches its 75% quota of resources. The study simulates 1000 users' requests of smart campus, adopts state-of-the-art ensemble with bagging strategy and handles an effective class imbalance situation.

Item Type: Article
Funders: Taif University, Taif, Saudi Arabia (TURSP-2020/36), Faculty of Computer Science and Information Technology, University of Malaya (PG035-2016A)
Uncontrolled Keywords: Servers; Internet of Things; Scalability; Cloud computing; SensorsResource management; Estimation; 3-axis scalability model; Auto-scaling; Cloud computing; Horizontal scalability; The Internet of Things (IoT); Predictive modeling; Quality of service (QoS); smart campus; Vertical scalability; Workloads
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 17 Feb 2022 08:28
Last Modified: 17 Feb 2022 08:28

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