6 posts tagged with "flow-control"

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.

San Francisco — FluxNinja is thrilled to announce the General Availability of its innovative open source tool, Aperture. This cutting-edge solution is designed to enable prioritized load shedding driven by observability and graceful degradation of non-critical services, effectively preventing total system collapse. Furthermore, Aperture intelligently auto-scales essential resources only when necessary, resulting in significant infrastructure cost savings.

In today's world, the internet is the most widely used technology. Everyone, from individuals to products, seeks to establish a strong online presence. This has led to a significant increase in users accessing various online services, resulting in a surge of traffic to websites and web applications.

Because of this surge in user traffic, companies now prioritize estimating the number of potential users when launching new products or websites, due to capacity constraints which lead to website downtime, for example, after the announcement of ChatGPT 3.5, there was a massive influx of traffic and interest from people all around the world. In such situations, it is essential to have load management in place to avoid possible business loss.

Graceful degradation and managing failures in complex microservices are critical topics in modern application architecture. Failures are inevitable and can cause chaos and disruption. However, prioritized load shedding can help preserve critical user experiences and keep services healthy and responsive. This approach can prevent cascading failures and allow for critical services to remain functional, even when resources are scarce.

To help navigate this complex topic, Tanveer Gill, the CTO of FluxNinja, got the opportunity to present at Chaos Carnival 2023 (March 15-16), which happened virtually, the sessions were pre-recorded. Though, attendees could interact with speakers since they were present all the time during the session.

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.