Resources

Customers can reduce the cost of nodes by ~ 56% when they introduce both, Karpenter for node auto scaling on EKS and replacing HPA with Smart Scaler for the pod autoscaling microservices. Karpenter simplifies Kubernetes infrastructure with the right nodes. Smart Scaler simplifies Kubernetes using GenAI to autoscale pod based on Application behavior and infrastructure metrics.

Effectively managing connectivity across multiple clouds and clusters has become a pivotal challenge for today's enterprises. The complexities of network architecture, combined with the limitations of existing solutions like Cilium, Skupper, and Submariner, call for a robust, scalable, and user-friendly connectivity solution. Avesha KubeSlice addresses the connectivity needs of multicloud and multi-cluster environments, setting a new standard for efficient and secure networking.

In this paper, Avesha CEO Raj Nair describes in detail the Smart Scaler solution, which is designed to dynamically scale pods within cloud- native environments based on traffic predictions and microservice pod capacity estimations. Smart Scaler is built on predictive analytics, Generative AI and Reinforcement Learning (RL), and it enables efficient management of resources, enabling organizations to meet fluctuating demands while minimizing waste and operational costs.

In the age of cloud computing, businesses are tapping into the sheer might of scalable and adaptable infrastructures to tackle the ever-shifting demands of dynamic workloads.

In this application note, we describe various use cases for Avesha's innovative product portfolio namely KubeSlice, Smart Scaler and Global Load Balancer. Together, these products offer a complete virtualization of Kubernetes and the freedom to connect workloads that are run in different locations using a programmatic or GUI control.

In this report we evaluate five edge computing use cases that are strong potential areas of focus, and evaluate the maturity, benefits and challenges of addressing each.

Smart Scaler is an essential tool in any Kubernetes toolbox. While HPA (Horizontal Pod Autoscaling) by itself is a significant improvement over cloud auto scaling. But, without the power of Reinforcement Learning, horizontal pod autoscaling will never fully meet the needs of organizations that require the full power of Kubernetes Scalability. </br> Dive into the world of horizontal pod autoscaling with Reinforcement Learning and discover how it can revolutionize your scaling strategies.

Explore the role of an application fabric as a concept that captures the essence of a declarative distributed deployment model that enables a high-velocity infrastructure, which is essential for business applications for modern applications

Application fabric is a surface area of applications to migrate across clusters and clouds. In this writeup, we examine the underlying issues and develop the key concepts behind an application fabric.

For securing your Kubernetes clusters, isolating applications is key. An easy way to isolate critical applications and reduce the attack surface is to put them on a Slice using KubeSlice.

A multi-cloud or hybrid strategy gives enterprises the freedom to use the best possible cloud native services for application workloads.

Multi-tenancy in Kubernetes is a way to deploy multiple workloads in a shared cluster with isolated network traffic, resources, user access, and last but not least control plane access.

Cloud native application delivery – and Kubernetes orchestration technology specifically – are finally past the early adoption territory where only new cutting-edge vendors dared to tread.

Providing a meaningful security posture for Kubernetes environments is crucial for the enterprise, however, it proves to be elusive for many cluster administrators. As enterprises adopt a shift-left mentality relating to their security posture, KubeSlice offers the ability to provide application-based security guardrails during the build cycle, enabling a richer application security posture for their applications and microservices running on Kubernetes.

Avesha Systems optimizes cost and latency across multiple Kubernetes clusters and cloud infrastructures through an application mesh with a multitenant environment for connectivity, authorization and workload direction.

Adoption of cloud-native technology such as containers and Kubernetes is among the most disruptive forces in today's enterprise IT market. A growing number of organizations of all sizes and across industries are seeking cloud-native benefits including efficiency, developer speed and productivity, and application portability. Regardless of where they are on their digital transformation journey, enterprises can either take advantage of technology such as cloud native as widely as possible across their platforms, environments and applications, or risk getting left behind in the market by organizations more aggressively and broadly embracing innovation.

It might seem like a non-sequitur because of the tedious process involved in the traditional approach of modernizing monolith applications starting with breaking down the monolith into one or more microservices. The objective is to achieve a more resilient and flexible application deployment. However, the process of achieving this objective without significant development resources and time is a non-starter – that is, until now. In this article, we explore an alternative approach that offers the benefits of a modernized workload for monolith VM applications with little or no-coding.

The web service landscape has galvanized toward a microservice-based SaaS that draws the parallel to the adoption of the Web in the early 2000s. However, underlying this massive transformation is a landscape of multi-cloud infrastructures with applications running over them designed to fit into the managed service platform of a particular cloud environment, unfit to the needs of globally distributed or disaggregated services.

Create an intelligent multi-cluster/multi-cloud service mesh to automate your application connectivity

Avesha's automated RL-based Load Balancing (LB) optimizes performance and minimizes footprint, preventing overloading and low latency of servers. The Avesha automated LB solution assigns requests to servers of corresponding length, and optimizes utilization of servers with differing capacity limits. The RL-based LB automatically reacts to changes in server and network state, and takes actions to prevent high latency.

The Avesha Application Slice customizes footprints to fit enterprise needs, using fewer integration points. The Application Slice comes with multiple benefits including reduced blast radius failover (increasing resilience), reduced air gap, segmentation, velocity, and simplified communication with increased reachability. Segmentation is key to increased security, and with an application split over multiple clusters, security is always the top priority.

The Avesha Smart Application Cloud Framework is one solution for faster deployment of services, easy integration, fluid workload mobility, run-time security and compliance, managed scalability, and an autonomous infrastructure.

With Avesha’s Application Slice technology, each slice is strictly segmented and has a zero-trust security federation. This improves security, contains failovers, and prioritizes traffic within the slice. With Avesha’s Slice technology, there is a standby cluster that will re-route to the other slices in the active cluster when the one slice experiences a failure. This technology limits the amount of nodes that are wasted capacity and greatly improves database resiliency compared to a normal cloud deployment.

Head-to-Head gamers have incurred an unfair disadvantage due to latency as a result of the distance between the gaming server and the AWS cloud. The Avesha Smart Application Cloud addresses this issue of latency with an overlay of application slices which edgify applications, and reduces high latency through segmentation technology to ensure fair advantages for all gamers.

Avesha's Smart Application Cloud platform uses the core Avesha technology called an Application Slice – which is an overlay, where the underlay of various networks disappears from the application view. This simplification will help prevent increased inertia towards the idea of edgefication and disaggregation. Reducing such inertia is what will allow for a widespread adoption of edge technologies that assist in a multitude of applications from medical to gaming services.

With Avesha's Smart Application Mesh, doctors are provided with a 'second set of eyes' to detect polyps in colonoscopy procedures with a 95% accuracy rate. This platform provides real-time results with features allowing for remote specialists to assist while not being physically present. It allows for hands-free voice commands by physicians and nurses using Voice NLP (Natural Language Processing) and automated reporting using Robotic Process Automation (RPA).

Avesha's Smart Application Cloud platform uses the edge in processing AI workloads and reducing high latency in a clinical setting specifically during colonoscopy procedures. This benefit to doctors and nurses greatly reduces errors, adds AI assistance, and provides better patient care through NLP (Natural Language Processing), and Automated Report technology.