AI-based Edge Cloud Monitoring.

For over the last few years, centralized cloud computing has been considered a conventional IT delivery platform. Even though cloud computing is omnipresent, the emergence of new technologies and requirements started to bring up the limitations cloud computing possessed. The main impact or challenge faced with the era of cloud computing was in latency and bandwidth. As the geographical location of data centers is far from the data entry point, there was a high latency time in processing. This increased time can lead to a delay in the real-time processing of data. Another scenario is in the use of bandwidth. As the communication becomes ceaseless between servers and end-users, this eventually ends in high bandwidth use. With all these challenges front, it became difficult to cope with the evolving IT requirements. Thats where the new paradigm of computing came into the picture, which is the era of edge computing.
The Era of Edge Computing :
Cloud computing and edge computing are complementary. Edge computing refers to the mode of data processing that is closer to the source of data. That is, data processing that brings computing closer to the end-user side. This minimizes the need for long-distance communications between client and server and reduces the need for data to travel. Bringing this computation closer helps in reducing the data processing time that eventually helps in reducing the latency. The reason for the low latency is that the computation is taking place at the network edge. The use of bandwidth also became low as edge computing provides pre-processing and caching. This benefited the entire IT operations to a great extent. Also, edge computing helps in processing time-sensitive data. With the implementation of edge computing, the performance of the system was improved as analytics capabilities were brought much closer to the end-user side. Moreover, the edge computing concept comes into best play in remote locations where there is limited or no connectivity to a centralized location. With a basic understanding of the edge computing concept, let's take a deeper look into the containerization of the edge concept.
The Role of Kubernetes in Edge Computing:
With the arrival of edge computing came the concept of containerization of edge platforms, i.e., the implementation of edge containers. Edge containers allow organizations to decentralize service by moving key components of their applications to the edge network. By this implementation, organizations were able to achieve low network costs and faster response time. For the management of these containers, the role of Kubernetes came into action. KubeEdge is an open-source platform enabling edge computing. This platform provides native containerized application orchestration capabilities to hosts at Edge. Also, the platform facilitates fundamental infrastructure support for network and application deployment. With the deployment of KubeEdge, cloud computing capabilities were brought into edge computing. With KubeEdge, scalability and resource optimization are achieved as edge components were containers and, they could run on low resources. With this, the next phase needed is the Observability of the entire system. Let's look into some of the basic observability requirements.
Edge Observability:
Edge computing observability requires a single pane of glass view wherein all the edge components can be monitored or observed in a big picture. As Edge computing takes place at the end-user side, which is more distributed in nature, the need for having a unified monitoring tool is a must. The observability phase enables achieving this unified method with the implementation of robust SLAs across cloud operational services. The Observability eases the deployment and also helps in the data collection such as KPI and log. Based on this collection, the data analysis part is made simple and, on this analysis, it is easy to perform certain remediation actions needed. Apart from that, central management across all edge sites was achieved with Observability. Every data center, public cloud, and all edge components were managed and monitored from a single tool.
AI-Based Observability in Edge Computing:
With the implementation of AI-based Observability, edge computing capabilities were shifted to a whole new level. With Observability on deck, the data collection process is simplified. The telemetry data collection was achieved without any agents, i.e., the instrumentation takes place at K8's layers. The Observability enabled storing all Edge operational data into a centrally stored place. This enabled ease of access to data. Moreover, by using machine learning-based analytics, the process of analyzing and getting inferences from data became easier. This enabled performing an anomaly detection mechanism where any unknown problems were automatically identified from the data. Also, this enabled in providing the right level of inference to take real-time decisions. With the implementation of the AI system, the right-sizing and optimization were achieved using advanced metaheuristics and genetic algorithms. 
The concept implementation of edge computing became revolutionary for IT organizations. Edge computing reduced the latency and saved up the bandwidth, thus helped in computing much closer to the end-user side.
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