5 AWS Mistakes You're Making That are Costing You


5 AWS Mistakes You’re Making That are Costing You

Making the shift to AWS can bring myriad benefits including improved application performance and reduced costs. However, moving to AWS does not guarantee that your application will run efficiently by default. Below are the 5 AWS mistakes to avoid that are costing you.

There is a tendency for companies that are migrating from more traditional data center operations to bring their traditional manner of operations with them to a cloud and end up missing some of the benefits their migration could provide.

Even companies that are starting fresh with AWS and taking a cloud-native approach can overlook some important tools because of AWS’s overwhelming number of services and feature. Like 5 Common Misconceptions about AWS Autoscaling, Here are some mistakes you probably have no idea you are making, but are costing you:

AWS Mistake #1: Infrastructure is not automatically provisioned

Managing your infrastructure is complex, with a lot of room for error. AWS provides a means to define resource requirements with CloudFormation templates that provide transparency into your system configuration and assure consistency.  For example, It is extremely difficult to select the correct EC2 instance types, especially in an environment that has a variable load. This is why it is important to use AWS CloudFormation. CloudFormation allows you to program your resource requirements and let automation take care of the implementation. With a CloudFormation template, you can be sure that your system will deploy consistently with expectations the first time and the hundredth time you deploy it.

Mistake #2: You’re not Utilizing Auto Scaling Groups

Autoscaling gives your system the ability to respond to changes in load. For example, it is common to operate web servers on virtual machines with an AWS Auto Scaling group. Auto Scaling Groups can be utilized to scale up and down in relation to workloads. To trigger auto scaling you can set alerts on certain metrics or thresholds, (e.g. the number of requests given to the load balancer). When done correctly (see the next mistake), autoscaling enables you to automatically program a response to these alerts to scale up or down.

It may seem that Auto Scaling groups simply focus on auto scaling, but this is not the case. It is essential that every individual EC2 instance is launched with an Auto Scaling group. Why? Auto Scaling groups also consistently manage your EC2 instances to meet your system requirements. You can make sure that your system will always have 3 or 5 or 35 instances running and Auto Scaling will make it so. For no additional charge!

Mistake #3: Not scaling down resources not being used

When your application is not right-sized, you are wasting money (if over-provisioned) or risk poor performance or even application failure (if under-provisioned). We tend to see this when applications are being managed manually. Operators might underestimate the load with an eye on saving money or overestimate with an eye on maintaining service without fully considering cost implications. 

The far greater tendency seems to be keeping an extra instance or ten around “just in case” or provisioning larger than necessary instances for the same reason. We also see operators manually scaling to meet load or automating only the scale up response and then forgetting to scale back down when load has dropped. Even though you have an EC2 instance that is sitting idle or not using full capacity, this doesn’t mean you’re not being charged for it – you are. Here, again, CloudFormation and Auto Scaling are your friends.

Mistake #4: Not taking advantage of CloudWatch

You might be thinking that this has all been great so far, but setting up a metrics and monitoring system is hard.  On AWS, that excuse is gone. AWS CloudWatch is a service that every AWS service – infrastructure and application – communicates its metrics too. Your virtual machine will give CloudWatch data on disk activity, how much CPU your application is using, as well as your network traffic, and more. It is essential to use the insights you gain from this information to improve your cloud function. It is also key to automating your cloud.  Using CloudWatch is the simplest way to trigger Auto Scaling (up or down) or launch a CloudFormation template in response to changes in your system.  

AWS Mistake #5: Not using Trust Advisor 

Trusted Advisor is a great tool that compares your application to what AWS believes is the most efficient and secure. Although the basic (free) service focuses on a few security best practices, the upgraded (paid) access adds further security suggestions along with recommendations to improve application performance, optimize cost, and improve fault tolerance. You can feed these recommendations back into your CloudFormation templates and automatically update your system to apply the changes.


Hopefully, you were already aware of these five common AWS mistakes and, if not, we hope this has helped you find a few ways to upgrade your AWS cloud system to be safer, more reliable, more performant, and cost you less. 

As helpful as fixing these mistakes will be to your system reliability and bottom line, you can still do better. At Opsani, we help you focus on delivering the core values of your business by automating away the toil – the repetitive and manual tasks – associated with optimizing systems that are constantly changing.  Opsani leverages artificial intelligence and machine learning, particularly deep reinforcement learning, to predict traffic spikes and resource requirements will accurately predict the best moment to scale up or down, and seamlessly integrates with AWS tooling to automate the scaling process. No toil necessary. To find out how Opsani can reduce your AWS spend, check out AWS does not Equal Cloud Optimization. 

Contact Opsani to know more about their cloud cost optimization technology and products. You can also sign up for a free trial to test and experience the Opsani advantage.

How to Improve Your Business Performance 3-5x


How to Improve Your Business Performance 3-5x

3-5x improvement in performance in regards to your cloud applications sounds great! But what does that mean in terms of your business?  In this video, Pavel, our Product Development Engineer, Peter, our CTO and VP of Engineering, and Amir, our VP of Marketing, will explain how by letting Opsani take over your cloud optimization, beyond just performance improvements, you will see 3 big business improvements. To learn what those 3 business improvements are, check out this quick 3-minute video below. To learn more from Pavel, Peter, and Amir check out How We Find Parameters To Optimize!

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How We Find What Parameters to Optimize Your Application


How We Find What Parameters to Optimize

Pavel, our Product Development Engineer, Peter, our CTO and VP of Engineering, and Amir, our VP of Marketing, will walk you through how to find what parameters to optimize your application. Although the terms of things that can be optimized for a java application is well known, the problem is the list is too long and is probably in the thousands. Therefore if you don’t know what to look for, trying random parameters and settings vitals isn’t going to get you anywhere. There are people who posses the knowledge to be able to fine tune a java application. However, from what we have seen, there’s not a lot of people that can do this. Consequently, we see a lot of java apps that are not well tunedOur video will break down how Opsani technology uses machine learning to determine what parameters are essential to optimize your application.

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How to get 10x more out of Kubernetes-orchestrated Java Workloads


How to get 10x more out of Kubernetes-orchestrated Java Workloads

As cloud optimization is becoming more of a necessity, understanding how to optimize Java can save you money while increasing performance. You might be wondering, ‘How is this possible?’ In this webinar, our highly experienced engineers explained the fundamentals of optimizing your Java cloud applications. They cover the best parameters to optimize, best practices for optimal Java on K8s, and the unholy marriage of Kubernetes and Java.

FinOps 101


FinOps 101: The Best Stage To Optimize Your Apps

How To Unify Cost Optimization Strategy & Business Profitability

Executive Summary:

FinOps is an operational framework that combines efficiency and best practices to deliver financial and operational control of a company’s cloud spend. FinOps has its roots in the ways in which the rise of the cloud has complexified the relationship between DevOps teams and finance departments. FinOps approaches aim to reduce cloud overspend and unite teams in sensible financial conduct.

The main motivation of the FinOps framework is cost optimization, and the main motivation of the well-known BCG Growth-Share Matrix is business profitability. Combining the two can bring maximum efficiency to a company’s financials. Similarly, combining a FinOps framework with Cloud Optimization approaches can bring AI-powered technologies to bear on the key goals of FinOps, and further empower company goals.

What is FinOps?

FinOps – short for Financial Operations – is a financial cloud management approach. At its core, FinOps combines best practices, a culture of efficiency, and effective systems, to produce absolute financial and operational control for cloud spending. FinOps increases a company’s ability to comprehend cloud computing costs and make necessary tradeoffs.

As agile DevOps teams break down silos, increase agility, and transform digital product development, FinOps brings together business and finance professionals with new technologies and procedures to elevate the cloud’s business value.

FinOps, if implemented correctly, enables companies and organizations to make better decisions regarding the growth and scale of their cloud utilization. This leads to better control over spending, optimum resource utilization, and significantly lower cloud costs.


How the FinOps Movement Started

The origins of the FinOps movement lie in the early 2000s, when DevOps took center stage and blew decades of established software development culture out of the water.

With the rise of DevOps, two previously siloed departments (Development and Operations) came together to function as one unit. They began developing new philosophies, uncovering best practices, utilizing new tools, and finding new ways to collaborate cohesively. Engineers and ops teams could no longer blame one other for slow servers or flawed code. They had to function together to solve issues, even if it meant retraining people in this new system of work.

Once the cloud and IaaS models came to prominence, the lines between technology, finance and procurement also started to become a problem. Infrastructure providers had to be on-demand, scalable, self-serviced, and measurable (OSSM). This meant that an engineer could essentially spend company resources to immediately scale up programs and fix performance issues without requiring approval from the finance and procurement departments. For the DevOps teams, this was wonderful. For the CFO and finance teams, it was not so wonderful.

Engineers, who worry constantly about performance issues and were once constrained by limited hardware, now had the freedom to throw money at a problem. But the CFO and finance teams were left with a financial mess.

Eventually, the realization dawned that something had to change. A balance needed to be achieved, to ensure that organizations don’t spend too many company resources, but are able guarantee performance. Different departments needed to integrate and shift into a shared accountability system.

This is when FinOps emerged. Like DevOps, it consisted of a new operating model with new frameworks and silo breakdowns. But unlike DevOps, it was an approach years in the making.

“It’s a cultural shift that I’ve watched happening in organizations around the world over the last 6-7 years,” wrote J. R. Storment of the FinOps Foundation in 2019.

“I first saw FinOps pop up in San Francisco circa 2013 at companies like Adobe and Uber, who were early scalers in AWS. Then I saw it when working in Australia in 2015 in forward-looking enterprises like Qantas and Australia Post. Then, during a two-year tour of duty in London starting in 2017, I’ve watched giant companies like BP and Sainsbury’s work diligently to develop this new culture.”

The Three Phases of the FinOps Journey

Transitioning to a FinOps culture consists of three main phases. These phases can happen simultaneously and iteratively in one company depending on the application, business unit, or team.


The first phase of the FinOps journey involves the use of people, tools, and processes to empower teams and organizations and provide the following benefits:

  • Proper allocation of cloud resources to enable accurate chargeback and showback;
  • Benchmarking as a cohort to provide organizations with key metrics for a high-performing team;
  • Effective budget plans to drive ROI while simultaneously avoiding overspend;
  • Accurate forecasting to prevent financial “surprises”, and;
  • Real-time visibility of the cloud’s on-demand and elastic nature to assist in making informed decisions.

The Inform phase is critical, because this is where your organization educates everyone involved and establishes the understanding that what you can measure, you can control.


The next FinOps phase is to bring into service all that information and empowerment to optimize your cloud usage and footprint. Some of the steps your organization can take are:

  • Transition from on-demand capacity (which is the most expensive feature of the cloud) into Reserved Instances (RI) where possible;
  • Take advantage of Committed Use Discounts (CUD, from Google Cloud) through longer-term commitments to enforce cost controls;
  • Rightsizing and reducing waste such as orphaned resources and unused instances and storage, and;
  • Utilizing AI-powered cloud optimization tools that improve your application’s efficiency, improving app performance while reducing cloud spend.


This third phase isn’t technically the last one. Rather, it’s a reminder that this journey isn’t a one-off activity. This cultural initiative should be integrated, baked, and automated  into the daily operations, if the goal is to achieve ongoing success. Organizations who aim to build a Cloud Cost Center of Excellence should also continuously evaluate the metrics they’re tracking with the business objectives they have and the current trends in their industry. This rinse-and-repeat process needs business, financial, and operational stakeholders who embrace the culture of FinOps.

The Structure of a FinOps Team

A FinOps team is composed mainly of the executives, FinOps practitioners, DevOps, and Finance and Procurement. Each of these individuals/teams have a different role in the FinOps framework:

  • Executives
    • Includes a VP/Head of Infrastructure, Head of Cloud Center of Excellence, and a CTO or CIO.
    • Their focus is to drive accountability, build transparency, and to ensure budget efficiency.
  • FinOps Practitioners
    • Includes a FinOps Analyst, Director of Cloud Optimization, Manager of Cloud Operations or a Cost Optimisation Data Analyst.
    • These individuals will be focused on the teams’ budget allocation, and on forecasting cloud spend.
  • DevOps Team
    • Mainly composed of engineers and Ops team members (Lead Software Engineer, Principal Systems Engineer, Cloud Architect, Service Delivery Manager, Engineering Manager, and/or Director of Platform Engineering)
    • DevOps will focus on building and supporting services for the organization.
    • At this point, cost is introduced to them as a metric that should be tracked and monitored like other performance metrics. DevOps teams have to consider efficient design and use of resources, as well as identify and predict spending anomalies.
  • Finance and Procurement
    • Often include Technology Procurement Manager, Global Technology Procurement, Financial Planning and Analyst Manager, and Financial Business Advisor.
    • They will use reports provided by the FinOps team for accounting and forecasting, working closely with them to understand historic billing data  and build out more accurate cost models.
    • These forecasts and cost models will then be used to engage in rate negotiations with cloud companies and service providers.

FinOps and the BCG Growth-Share Matrix

One powerful way to leverage a FinOps framework is to combine it with the BCG Growth-Share Matrix – a model that many veterans of the business world will be familiar with.

What is the Growth-Share Matrix?

The Growth-Share Matrix was invented by the Boston Consulting Group’s (BCG) founder Bruce Henderson in 1968. It is a portfolio management framework that aids organizations in determining which product, service, or business to prioritize and focus on.

The BCG Growth-Share Matrix is a table comprising four quadrants that represent the degree of profitability of a product, service, or business:

  • the Stars;
  • the Question Marks;
  • the Cash Cows, and;
  • the Dogs (also known as Pets).

Each product/service/business is assigned to one of these categories, based on certain factors, but most especially on their capability for growth and their market share size. Executives can then decide which ones to focus on to drive more value and generate more profit.

How does the BCG Growth-Share Matrix help us refine a FinOps framework?

The main motivation of the FinOps framework is cost optimization, and the main motivation of the Growth-Share Matrix is business profitability. Combining the two can bring maximum efficiency to a company’s financials.

How the BCG Growth-Share Matrix Works

This business framework was built on the principle that market leadership = sustainable, superior returns. It reveals two fundamental elements of business that organizations need to consider before investing in a business:

  • Market attractiveness, which is driven by relative market share, and;
  • Company competitiveness, which is driven by the company’s growth rate.

The market leader eventually obtains a self-reinforcing cost advantage, which competitors find difficult to emulate. Such high growth rates tell organizations which markets have the highest growth potential, as well as the ones that don’t. The four symbols of the Matrix represent a certain combination of growth and relative market share:

The Stars

These are high-growth, high-share businesses that have considerable future potential and are very lucrative to invest in. These are the market leaders – businesses that make up a great portion of their industry and generate the most income. They need high funding, to maintain their growth rate. But if the business manages to maintain its status as a market leader when market growth slows, it becomes a Cash Cow.

Worst case scenario is when new innovations and technological advancements outplay your business, and instead of your Star becoming a Cash Cow, it becomes a Dog. This often happens in rapidly-changing markets and industries, and it can catch companies off guard.

The Question Marks

Question Marks are high growth, low share businesses that pose a strategic challenge. Depending on their chances of becoming Stars, companies either invest in or discard them. Startups and ventures often possess this designation.

With the right circumstances and the right management, a Question Mark can turn into a Star and, eventually, a Cash Cow. But sometimes, even after a large amount of investments, Question Marks still don’t develop into market leaders, and they end up as Dogs. This is why companies need careful consideration when it comes to deciding matters with Question Marks.

The Cash Cows

Cash Cows are low growth, high share businesses. They are marketplace leaders, generating more cash than they consume. The growth of Cash Cows isn’t high, but they often commonly have a lot of users already. They  act like the backbone of the company, providing revenue on almost all fronts.

Other, more “mature” markets consider these businesses to be “plain and boring”, especially because they run on a low-growth environment. But their cash generation is constant, and corporations value them a lot for it. The cash they generate also helps Stars and Question Marks transform into Cash Cows.

However, mismanagement and other negative circumstances degrade a Cash Cow into a Dog, so companies should also continue investing in Cash Cows to “milk” it passively and maintain its level of productivity.

The Dogs

The Dogs are the worst form a business can take. Dogs are low share, low growth businesses, meaning they’re in a mature and slow-growing industry and have low market shares. Often, they are cash traps that tie up company funds over long periods, drain resources due to low-to-negative cash returns, and depress a company’s return-on-assets ratio.

Dogs can still sometimes play a role in a company – for instance, one Dog may complement the products of other business units. But common marketing advice to deal with Dogs is to remove them from the company’s portfolio altogether, through divestiture or liquidation. Unless the organization finds a new strategy to reposition Dogs and lift them up from their status, they will most likely hurt the company in the long run.

Utilizing the Growth-Share Matrix

When adopting a FinOps culture, companies should simultaneously evaluate their product offering(s) using the Growth-Share Matrix with optimization strategies. While the FinOps practitioners establish the new procedures for FinOps to take hold, executives should take the time to look at their product lines and product features and thoroughly examine them. The expertise of the FinOps practitioners in benchmarking and forecasting should help them determine which product features and product offerings are on the way to becoming Cash Cows, and which ones are not.

Eventually, all products will either turn into Cash Cows (which is the best kind of product/business to apply optimization to) or Dogs. Careful evaluation on both the business side and the operational side can help companies and organizations make the right decisions.

Where Optimization Should Be Focused

Question Marks are not ideal as focus points of optimization. Yes, these kinds of products are growing. But the market isn’t really adopting Question Marks, due to their low share. With less adoption, costs are not skyrocketing. This also means that companies won’t be able to see many returns from them (yet).

Dogs are likely to be discontinued anyway – so you might as well cut losses as early as possible. Stars should definitely be optimized, but they are growing so fast that they are the hardest ones to control in a really granular way.

During the Optimize phase, it’s the Cash Cows that FinOps companies should focus on. Lots of people are already using them, and the user base will most likely be pretty steady, so there won’t be much “growth” to unsettle things. Organizations should prioritize reducing their Cash Cow’s cloud footprint and usage. This allows the Cash Cows to function more efficiently and generate more revenue for the company.

As the Operation phase rolls in, companies can start working on their Stars and Question Marks.

FinOps and Cloud Optimization

AWS autoscaling

Cash Cows are where optimization should be focused. The best way to optimize and squeeze more revenue out of these is to bring costs down. Reducing Cash Cows’ cloud footprint and usage will help companies to successfully integrate both the FinOps and the BCG Growth-Share Matrix frameworks. However, you also need to consider the performance of your Cash Cow. It’s no good cutting costs if performance suffers. You don’t want to jeopardize user experience. This is where cloud optimization comes in.

Cloud optimization can play a significant role in an effective FinOps culture. Cloud optimization is all about minimizing cloud spend, and preventing unnecessary wastage in DevOps budgets – the same motivations that gave birth to the FinOps movement in the first place.

Cloud apps are complicated. The right tweaks and changes to resource allocation and parameters can have a big impact on cost. But even a basic application can have trillions of different permutations of resource and parameter settings. And these settings change fast. With daily code releases, infrastructure features and updates, and traffic changes and user growth, no-one short of a superhuman can keep up.

Leveraging the Capabilities of Artificial Intelligence

An AI-driven cloud optimization technology will perform automation better than any human ever will. AI systems, unlike humans, do not grow tired, do not easily forget, and can take in and calculate variables at hyperspeed. Such technology is what is needed to help organizations optimize entire systems, providing improved performance with minimal costs. This allows them to stick to the overarching principles of the FinOps culture, and leaves the engineers and operations teams with more time to focus their efforts on development and innovation.


Adopting a FinOps framework is a lifetime commitment for a company. Sometimes, the process doesn’t go as smoothly as planned, especially for new companies who are still trying to figure out the business side of the cloud.

However, with the right guiding principles, the right mindset, the right people, and the right cloud optimization tools, any company can successfully pull off a FinOps transition, saving them millions of dollars in resources and ensuring the company’s longevity in the industry. Combining the FinOps framework with a proven and effective model like the BCG Growth-Share Matrix gives companies a way to sharpen their thinking around FinOps goals, and better position themselves for success.


For more reads, check out these other articles:

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What Is Continuous Integration and Optimization?


What Is Continuous Optimization?

Continuous Integration (CI) and Continuous Delivery (CD) have become cornerstones of modern software development and deployment practices. But they do not help you manage the exploding complexity of optimizing cloud applications, where even simple applications can have trillions of potential combinations of resource, middleware, and application settings. This is where Continuous Optimization (CO) comes in.

This webinar introduces the concept of Continuous Integration Optimization — the application of advanced AI automation to optimize all of the relevant parameters — and shows how it ensures the best possible combination of performance, cost, and efficiency for your cloud applications. Learn how leading development organizations are extending the CI/CD pipeline to include CO, using AI to master complexity far beyond human capability to manage, and getting the most out of their cloud applications.

Kubernetes: Everything You Need to Know


Kubernetes: Everything You Need to Know

In this article, Kubernetes: Everything You Need To Know we will shed light on this amazing container orchestration system.  It is a tool that facilitates the automation of all aspects of application deployment and management. Kubernetes plays a key role within the world of cloud applications. The platform accelerates time to market, offers enhanced scalability and availability, combines neatly with cloud optimization tools, works flexibly across multiple and hybrid cloud environments, and makes all aspects of cloud migration smoother.

Containers: A Brief Overview

To understand Kubernetes, you first need to understand containers.

Back in 2013, Docker changed everything. Building on existing virtualization technologies, Docker introduced containers. A container is an abstraction, implemented at the kernel level, that consists of an entire runtime environment. This means that a container contains an application, but it also contains all of its dependencies, libraries and configuration files.

Containers allow you to quickly and smoothly move software from one computing environment into another. (For example, from staging to production, or from a physical server to a virtual machine (VM).) By “containerizing” an application and its dependencies, differences in infrastructure or OS distributions are abstracted away. An app will run the same on your laptop as it does in the cloud.

Unlike with older virtualization and VM frameworks, containers are able to share an operating system kernel with one another thanks to their relaxed isolation properties. As a result, a container is considerably more lightweight than a VM (virtual machine), which typically contains its own dedicated OS. This means that servers can host many more containers than it can VMs.

Containers are integral to modern DevOps frameworks. Their modular nature is what allows for a microservices approach, where different parts of an app are split up across different containers. Containers allow for quick and easy rollbacks, due to their image immutability. Containers accelerate the time-to-value for code, allowing releases to arrive daily, rather than quarterly. In modern cloud computing, containers are fast becoming the new norm. The 2019 Container Adoption Survey found that 87% of respondents used container technologies, compared to just 55% back in 2017. 451 Research predicts that containers will be a $4.3-billion industry by the end of 2022.

Container Organization: Kubernetes to the Rescue

What is Kubernetes exactly?

But: Containers need to be managed. They are complex entities, and many DevOps teams are managing thousands of containers. 

Enter Kubernetes. Originally designed by Google, Kubernetes (pronounced “koo-ber-NET-eez”) is an open-source container orchestration software designed to simplify the management, deployment, and scaling of containerized applications. Also referred to as “K8s”, “Kube”, or “k-eights”, Kubernetes is actually the Greek word for helmsman – the person in charge of steering a ship. Kubernetes integrates with a range of container tools, but the majority of people pair it with Docker. 

In short, Kubernetes is what people use to manage their containers. Kubernetes automates and simplifies the various processes involved in the deployment and scaling of containers, as well as in directing traffic into and between containerized applications. In a production environment, enterprises need full control over the containers that run their applications, to ensure that there is no downtime. Kubernetes gives them this level of control. 

Kuberenetes also facilitates the efficient management of clusters of containers. Kubernetes clusters can be distributed across multiple environments, including on-premise, public, private, or hybrid clouds. This makes Kubernetes an ideal hosting platform to manage cloud-native applications that rely on rapid, real-time scaling (such as data streaming through Apache Kafka).

How Kubernetes Works: Some Technical Details

From a 30,000-foot level, Kubernetes provides DevOps reams with a framework to run distributed networks of containers resiliently. Kubernetes enables flexible and reliable scaling of large clusters of containers, provides failover for applications, provides deployment patterns, and everything else teams need.

The base of the Kubernetes architecture consists of the containers that communicate with each other via a shared network. These containers form into clusters, where several components, workloads, and capabilities of the Kubernetes environment are configured.

Every node in every cluster is provided a specific role within the Kubernetes infrastructure. One particular node typeis deployed as the master node. The master server is the cluster’s main point of contact. It is in charge of the majority of the centralized logic that Kubernetes supplies  This is basically the gateway and brain for the cluster. The master server reveals an API for both clients and users, monitors the health of other servers, identifies the best ways to split up and delegate work, and facilitates and organizes communication between other components. In highly available Kubernetes clusters, there are multiple master nodes (typically 3) in order to ensure that the cluster can be contacted and continues to operate even if a master node fails. Kubernetes arranges for the automatic failover between master nodes.

Worker nodes are tasked with accepting and running workloads. To ensure isolation, efficient management, and unmatched flexibility, Kubernetes places and runs applications and services in containers. A container runtime (like Docker or rkt) needs to be equipped per node for this setup to work.

Once everything is up and running, the master node sends work instructions to the worker nodes. Fulfilling these instructions, worker nodes stand up or tear down containers accordingly, make adjustments to the networking rules to route and direct traffic appropriately.

Quick Definitions of Kubernetes Key Elements

Master node. Functions as the main control and contact point for administrators and users. It also distributes (schedules) workloads to worker nodes and handles failover for master and worker nodes.

Worker nodes. Act on assigned tasks and perform requested actions. Worker nodes take their instruction from the master node.

Pods. A group of containers that share network and storage and are alwaysplaced in a single node. Containers within the same pod typically collaborate to provide application’s functionality and are relatively tightly coupled.

Replication controller. This provides users with total control over the number of identical copies of a pod operating on the cluster.

Service. A named abstraction that exposes an application running on a set of pods as a network service and load balances traffic among the pods.

Kubelet. Running on nodes, Kubelet takes the container manifests, reads them, and ensures the defined containers are activated and functioning.

kubectl. The command line tool for controlling Kubernetes clusters.

The Business Advantages of Kubernetes

Kubernetes brings obvious benefits when viewed from an IT or DevOps perspective. But how does Kuberentes positively impact the business goals of an enterprise? In five key ways:

1. Accelerated time to market.

Kubernetes allows enterprises to utilize a microservices approach to creating and developing apps. This approach enables companies to split their development teams into smaller groups, and achieve more granular focus on different elements of a given app. Because of their focused and targeted function, smaller teams are more nimble, and more efficient.

Additionally, APIs between microservices reduce the volume of cross-team communication needed to build and deploy apps. Teams can do more, while spending less time in discussions. Businesses can scale different small teams composed of experts whose individual functions help support thousands of machines.

Because of the streamlining effect of the microservices model that Kubernetes empowers, IT teams are able to handle huge applications across many containers with increased efficiency. Maintenance, management, and organization can all be largely automated, leaving human teams to focus on higher value add tasks.

2. Enhanced scalability and availability.

Today’s applications rely on more than their features to be successful. They need to be scalable. Scalability is not just about meeting SLA requirements and expectations; it’s about the applications being available when needed, able to perform at an optimum level when activated and deployed, and not swallowing up resources that they don’t need when they are inactive.

Kubernetes provides enterprises with an orchestration system that automatically scales, calibrates, and improves the app’s performance. Whenever an app’s load requirements change – due to an increasing volume of traffic or low usage – Kubernetes, with the help of an autoscaler, automatically changes the number of pods in a service in order for the application to remain available and meet the service level objectives at the lowest cost.

For instance, when concert ticket prices drop, a ticketing app experiences a sudden and large spike in traffic. Kubernetes immediately spawns new pods to handle the incoming load that is above the defined threshold. Once the traffic subsides, Kubernetes scales down the app back to configurations and metrics that optimize infrastructure utilization. In addition, Kubernetes’ auto-scaling capability not only relies on infrastructure metrics to initiate the scaling mechanism. It scales automatically using custom metrics as triggers to the scaling process.

3. Optimization of IT infrastructure-related costs.

Kubernetes reduces all expenses pertaining to IT infrastructure drastically, even when users are operating on a large scale. The platform groups apps together using hardware and cloud investments, and runs them on a container-based architecture.

Prior to Kubernetes, administrators addressed the instances of unexpected spikes by ordering tons of resources and putting them on reserve. While this helps them handle unforeseen and unpredicted increases in load, ordering too many resources quickly becomes extremely costly.

Kubernetes schedules and solidly packs containers, while taking into consideration the available resources. Because it automatically increases or decreases the load on applications based on prevailing business requirements, Kubernetes helps enterprises free up their manpower resources, which they can then assign to other pressing tasks and priorities.

4. Flexibility of multiple cloud and hybrid cloud environments.

Kuberentes helps enterprises to fully realize the absolute potential of multiple and hybrid-cloud environments. That’s a big plus, considering the number of modern companies running multiple clouds is increasing every day.

With Kubernetes, users find it much easier to run their app on any public cloud service or in a hybrid cloud environment. What this means for enterprises is they can run their apps on the most ideal cloud space, with the right-sized workloads.

This helps them avoid vendor lock-in agreements, which typically come with specific requirements around cloud configurations and KPIs. Getting out of lock-in agreements can be expensive, especially when there are other options that are much more flexible, more cost-effective, and have a bigger ROI, over both the short and long term.

5. Effective cloud migration.

Whether a Kubernetes user requires a simple lift and shift of the app, making adjustments to the app runs, or a total overhaul of the entire app and its services, migrating to the cloud can be a tricky enterprise, even for experienced IT professionals. But Kubernetes is designed to make such cloud migrations much easier.

How? Thanks to the nature of containers, Kubernetes can run across all environments consistently, whether on-premise, cloud, or hybrid. The platform supplies users with a frictionless and prescriptive path to transfer their on-premise applications to the cloud. By migrating via a prescribed path, users don’t have to face the complexities and variations that usually come with the cloud environment.

The Future of Kubernetes

For the moment, enterprise organizations are chiefly using Kubernetes because it is the best way to manage containers, and containers supercharge the possibilities of app creation and deployment. Kubernetes automates processes that are critical to the management of IT infrastructure and app performance, and helps organizations optimize their cloud spend.

The future of Kubernetes could get even more interesting. Chris Wright, VP and CTO of Red Hat, summarizes the new ecosystem that is emerging around Kubernetes: “Just as Linux emerged as the focal point for open source development in the 2000s, Kubernetes is emerging as a focal point for building technologies and solutions.”

Kubernetes is currently the leading container orchestration platform. But increasingly, it is doing more than simply enabling organizations to manage their containers, optimize their cloud apps, and reduce spend. Kubernetes is actually driving new forms of “Kubernetes-native” app and software development. As the shift toward microservices continues to pick up pace, organizations will create and deploy apps with Kubernetes in mind from the jump – as a formative influence, not merely a tool.

How To Start Optimizing Your Apps

How To Start Optimizing Your Apps


How To Start Optimizing Your Cloud Applications

For good reason, all organizations understand the importance of cloud optimization and want to optimize their cloud applications. However: 99% of cloud performance tuning efforts fail. If they ever even begin. Why? In this eBook we explain how to:

  • Experience a 2.5x increase in application performance, or a 40-70% decrease in cost. Overnight.
  • Achieve the same or better application reliability, at higher performance, for lower cost.

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What is the future of DevOps?

DevOps Interrupted: Addressing the Complexity of Optimization with AI


DevOps Interrupted: Addressing the Complexity of Optimization with AI

In this webinar, Amir Sharif (VP of Product & Marketing), will be sitting down to talk to Blake Watters (Director of Enterprise Services), to discuss optimization. What was once an integral part of new product releases, has somehow disappeared from the process altogether. Tune in to our webinar to hear a discussion on “where optimization has gone” and why we think this shift has happened.


Interested in learning more about Cloud Optimization? Download our free whitepaper!

Download Your Free Whitepaper

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Why Controlling OpEx Matters


Why Controlling OpEx Matters

In this webinar, Peter Nickolov, Opsani Co-Founder and CTO, and Amir Sharif, VP of Marketing, will discuss why controlling OpEx matters in today’s changing world and how it has evolved the IT industry, the ongoing drift from CapEx budgets to OpEx counterparts, and how to position your growth prospects in light of these changes.

Interested in learning more about Continuous Optimization? Download our free white paper!

Download Your Free White Paper