Host workloads in a cloud environment with Autoscaling

Cyfuture Cloud offers autoscaling powered by VMware's NSX Advanced Load Balancer, operates on a sophisticated elastic fabric architecture. This architecture acts as a dynamic framework, allowing the scaling of SEs (Service Engines) and application servers in response to fluctuating demands in workload, seamlessly adapting their capacity in real-time.

This autoscaling mechanism delves into a granular level, incorporating specific parameters related to CPU and RAM scaling. When load variations occur, Cyfuture Cloud's system intelligently adjusts these resources, dynamically allocating computing power and memory based on the immediate requirements of the workload.

What sets this apart is not just the utilization of inherent cloud capabilities but also the active management and optimization of these resources. NSX Advanced Load Balancer not only harnesses the elasticity of cloud environments but also leverages intricate metrics and data gathered within its ecosystem. By interpreting and acting upon these metrics, it orchestrates a sophisticated allocation of resources, ensuring optimal performance and efficient capacity management in real-time scenarios. This meticulous approach helps in maintaining high availability and efficient resource utilization, meeting demands without compromising on performance or reliability.

Additionally, Cyfuture Cloud offers various types of autoscaling as per business requirements, here are the ones listed below:

  • 1) Predictive auto scaling
  • 2) Schedule-based auto scaling
  • 3) Utilization metrics based auto scaling

Key Features

With Cyfuture Cloud, you can experience the following scaling features along with schedule and predictive scaling

By using NSX Advanced Load Balancer policies, both types of scaling can be done automatically.

It supports the above-mentioned autoscaling features in all ecosystems.

Virtual Service Scaling

SEs are capable of processing a maximum amount of traffic and are usually measured by the traffic throughput or SSL transactions per second of a SE. This SE capacity is a function of a variety of factors, such as the size of the SE VM (number of cores or memory), the type of traffic, and the ecosystem within which the SE is running.

Virtual services are typically placed on a single SE in the default configuration of a system. It is possible, however, to scale out the virtual service to other SEs in the event that the SE alone is not enough to handle the traffic for the virtual service. As a result, the traffic of the virtual service is handled by more than one SE at the same time.

Virtual services that need to be scaled out or scaled in can either be done manually or automatically.

Virtual Service Scaling

If you wish to configure thresholds beyond which a virtual service should be scaled out to a new SE, or scaled back into fewer SEs, then one of these SE parameters can be used to configure the thresholds beyond which a virtual service should be scaled into a new SE:

CPU utilization of the SE

CPU utilization of the SE

Bandwidth, in Mbps, being served by the SE

Bandwidth, in Mbps, being served by the SE

Connections per second (CPS) being served by the SE

Connections per second (CPS) being served by the SE

Packets per second (PPS)

Packets per second (PPS)

Application Server Scaling

As part of the virtual service load balancing, it is equally important to ensure that enough capacity is available at the application instance tier to handle traffic loads in conjunction with virtual service load balancing.

In light of the fact that public cloud infrastructure is charged based on usage or uptime, it is important to have enough capacity based on usage, as well as the flexibility to scale resources on demand.

Autoscaling is a feature provided by public clouds. It is possible to create virtual machines and configure them based on the templates for autoscaling servers. A scale-out or scale-in can be done either manually or based on a certain set of load conditions that are present at the time.

Application Server Scaling

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