3 posts tagged with "istio"

Service Mesh technologies such as Istio have brought great advancements to the Cloud Native industry with load management and security features. However, despite all these advancements, Applications running on Service Mesh technologies are unable to prioritize critical traffic during peak load times. Therefore, we still have real-world problems such as back-end services getting overloaded because of excess load, leading to a degraded user experience. While it is close to impossible to predict when a sudden increase in load will happen, it is possible to bring a crucial and essential feature in Service Meshes to handle unexpected scenarios: Observability-driven load management.

In this blog post, we will discuss the benefits of Istio and how FluxNinja Aperture can elevate Istio to be a robust service mesh technology that can handle any unexpected scenario by leveraging advanced load management capabilities.

Service meshes are becoming increasingly popular in cloud-native applications, as they provide a way to manage network traffic between microservices. Istio, one of the most popular service meshes, uses Envoy as its data plane. However, to maintain the stability and reliability of modern web-scale applications, organizations need more advanced load management capabilities. This is where Aperture comes in, offering several features, including:

In today's world of rapidly evolving technology, it is more important than ever for businesses to have systems that are reliable, scalable, and capable of handling increasing levels of traffic and demand. Sometimes, even the most well-designed microservices systems can experience failures or outages. There are several examples in the past where companies like Uber, Amazon, Netflix, and Zalando faced massive traffic surges and outages. In the case of Zalando (Shoes & Fashion Company), the whole cluster went down; one of the attributes was high latency, causing critical payment methods to stop working and impacting both parties, customers, and the company. This outage caused them a monetary loss. Later on, companies started adopting the graceful degradation paradigm.