The 3-Axis Scalable Service-Cloud Resource Modeling for Burst Prediction Under Smart Campus Scenario

IEEE Access

Roobaea Alroobaea¹  Fahad Almansour²  Mirza Abdur Razzaq³  Javed Ahmed Mahar³  Ihsan Ali⁴  Kumarmangal Roy⁵  Muneer Ahmad⁵ 

[1] Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia;Department of Computer Science, College of Sciences and Arts in Rass, Qassim University, Buraydah, Saudi Arabia;Department of Computer Science, Shah Abdul Latif University, Khairpur, Sindh, Pakistan;Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia;Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia;

关键词: 3-axis scalability model;    auto-scaling;    cloud computing;    horizontal scalability;    the~Internet of Things (IoT);    predictive modeling;   

DOI  :  10.1109/ACCESS.2021.3105539

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown