Hybrid Auto-Scaled Service-Cloud-Based Predictive Workload Modeling and Analysis for Smart Campus System

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 scaleup 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 timestamped diverse service-requests appearing with different workloads.INDEX TERMS Auto-scaling, cloud computing, horizontal scalability, internet of things, predictive modeling, quality of service (QoS), smart campus, vertical scalability, workloads

Application of Internet of Things Technology in Mobile Education of Smart Campus Culture and Etiquette

“…As the lower concept of management, school management is also facing unprecedented opportunities and challenges under the influence of modern advanced science and technology [1]. We can all realize that the economic management, government management, enterprise management, and other related management existing in the society have been at the forefront of advanced science and technology, and today's world is in a period of great development, change, and adjustment.…”

Predictive Hybrid Autoscaling for Containerized Applications

“…Only a few existing studies are nearest to the hybrid approach. The authors in [14] propose the hybrid auto-scaled service cloud model to ensure (QoS) for smart campus-based applications. They vertically scale the server in normal load and switch to horizontal scaling mode in burst cases.…”

“…By using machine learning to support scaling decisions, we may turn the default reactive method in Kubernetes into the predictive method and quickly satisfy user demand. In addition, because cloud applications normally experience burst workloads (a sudden sharp increase in workload rate) [14], a burst detection mechanism should be combined with workload forecasting. This combination can avoid the saturated pod performance problem of the vertical scaling part in hybrid scaling decisions.…”

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

“…In VTS, nodes are connected via LAN to make clusters for parallel processing and in HTS, vertical clusters are combined for sequential processing. The authors in [26] presented a hybrid auto-scaling approach of vertical and horizontal scalability using workload prediction with an ensemble classifier. The ensemble algorithm detects bursts in-service loads, and auto-scales accordingly.…”

“…In [26], Class imbalance situation is not dealt and the vertical server burdens due to load prediction and auto scaling.…”