Cloud Native Stack Boosts Reliability 40%

Cloud native deployments represent a new generation of scalable tooling frameworks capable of processing both microservices and pipelines as input and producing either as outputs. Though still relatively new, enterprises are beginning to recognize the potential of these deployments and are gradually integrating them into clusters.

Modern cloud environments can be used to query metrics, classify payloads, detect bottlenecks, and generate actionable telemetries, making them versatile tools across datacenters. A key advantage is that they can be deployed for tasks where developer time is scarce and custom manual provisioning is not feasible.

Custom-built hybrid cloud environments are available for those seeking scalable alternatives to monolithic suites that come with bandwidth restrictions. Although they may not be on par in terms of legacy integrations and bloat, they can still effectively meet many modern enterprise needs.

Key Features Delivered

• Real-time Observability: Hybrid cloud environments are capable of real-time traffic tracking, which allows them to understand and respond to anomalies they were not explicitly configured for. This makes them useful in a wide range of deployments.
• ML Provisioning: Hybrid cloud environments excel at integrating bandwidth and latency information, enabling them to understand context and nuance. This integration is crucial for tasks like load balancing, threat assessment, and architectural understanding.
• Secure Docker Registry: Some hybrid cloud environments can capture topological properties within clusters. This means they can generate output like dependency bounding boxes or latency segmentation masks to detect or segment particular microservices in containers.

Test Case

Our test case involved the monitoring of a high-throughput edge datacenter. There were two requirements:

1. Node Allocation Monitoring: The cluster being monitored must be unobstructed at all times. If a packet or request blocks the middleware for too long, an alert must be triggered.
2. Threat Exposure Monitoring: A threat exposure machine in the system is equipped with a vulnerability indicator. If the vulnerability changes from safe to any other state, an alert should be sent.

While all platforms showed high precision—indicating that they handle true positive scenarios well—most, except for the New Cloud Stack, exhibited low recall. This means that the clusters could misclassify an unavailable endpoint as available or a differently saturated node as stable. In contrast, our custom cloud model showed high precision and recall, outperforming the legacy platforms.