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Applications performance, Kubernetes cluster utilization, and cost efficiency are crucial for the success of your cloud-native technology. It is a challenge to bring the whole team on-board in this journey and keeping them agile. Engineering Teams and DevOps need to manage a lot of moving parts while figuring out a proper migration path to be cloud-native. They need to maintain legacy systems and redesign them to meet the best containerization and operational patterns.
Smart engineers proactively anticipate and tackle challenges that will slow their team down.
Improving your users’ experience, enabling your team to have a reasonable control of Kubernetes cluster resources, and maximizing the return on investment of your company’s cloud infrastructure are crucial for your agility and success. This is a step-by-step guide to achieve these three goals in a very short time and effortlessly.
Companies that emphasize the importance of innovating their customer experience are quick to see the value of adopting the cloud-native development model.
According to IBM’s research on cloud-native adoption, below are the most important factors for the success of any team in their cloud-native journey:
Achieving these goals with the current tools is hard and time-consuming. Teams need to learn about the fast-evolving cloud-native stack while innovating fast. Offloading some parts in the early days adopting cloud-native architecture is important for you and your team. You want to move fast on what really matters to your business and your team.
Magalix provides a cloud-native solution to monitor your application's performance, keep Kubernetes cluster utilization tuned to business needs and to get the most value out of your cloud provider. Magalix features are unlocked with a single command line. That command installs our agent. You will get in few minutes the full picture of your applications and Kubernetes cluster. Once your Kubernetes cluster is connected, you will get the following:
The only pre-requisite to start optimizing your Kubernetes cluster is to have admin rights on your existing Kuberentes cluster(s). You will go through 4 simple steps to get a deeper understanding of your applications performance, cluster utilization, and cost optimization.
In order to get started you will need to install Magalix agent in your Kubernetes cluster. The installation process is very simple. Copy the provided kubectl command and past it inside your cluster’s terminal.
Note: The provided URL to the agent’s deployment.yaml file is valid for 4 hours only and specific to your cluster. You should not share or reuse this file to connect other clusters. You can connect as many clusters as you need but each will need its own unique YAML file.
Run the kubectl command in your Kubernetes terminal to provision Magalix Agent.
You should get a confirmation in your terminal that command executed without any issues. The following takes place in the background:
If your cluster is having restrictions downloading files directly from the Internet, you can download the YAML file and run it yourself. To learn more about the content and what the YAML file does, please read our reference documentation.
- Magalix agent is open source and you can examine, or even better contribute to its code base.
- Magalix agent is scanned before making it publicly available for any security vulnerabilities.
- During your free trial, you can connect as many clusters as you want. There is no restriction on that.
You can install Magalix agent through GCP market place. If you have a GKE cluster you can install Magalix agent with a single click of a button. Give Magalix page on GKE marketplace a visit to install the agent on any of your GKE clusters.
Click on the CONFIGURE button at the Magalix agent GCP marketplace page to install Magalix agent.
The next screen will ask you to insert basic details to configure the agent.
Once you are done with necessary inputs, the agent will be automatically installed and configured in your cluster. Check your inbox for the welcome email and login at https://console.magalix.com to make sure that your cluster is properly connected.
Once data start flowing to Magalix backend, a lot of magical steps takes place. Magalix will analyze the performance, capacity and cost efficiency of your Kubernetes cluster. It will build several dashboards to give you a global overview of these aspects across your clusters. Take a tour of Magalix console to familiarize yourself with what you can observe and achieve with it.
Let’s check if your applications have any performance bottlenecks. The performance analysis card in your home dashboard tells if your containers were throttled recently. Magalix tracks two cpu.stats metrics to detect CPU performance bottlenecks:
The higher the number of these metrics, the worse your container is going to perform. Magalix tracks these metrics across your whole cluster and suggests if you need to adjust CPU resources that should be allocated to containers and microservices.
The home screen performance analysis card lists the top throttled containers for the last 24 hours. The CPU cores deficit/sec is an estimate of how many cores should have been allocated to the container and avoid it being throttled. In the below example, the forecaster-worker had 20 instances and on average each instance needed 2 additional cores. The side chart shows collectively CPU throttling overtime and if containers are having excessive loads at certain times of the day.
Click on any of the throttled containers to see more detailed analysis. You will see action items to improve that container’s performance. In below snapshot, you will notice that due to relatively low CPU limits the forecaster-worker is throttled multiple times. Magalix prediction model, the grey area, showing that the same CPU load will continue for the next 4 - 8 hours. Scroll down to see the recommendations
In below’s snapshot, you will notice a summary of generated recommendations an area chart to help you decide what to do next. The summary card gives you an idea of generated recommendations/decisions to improve resources management for that particular container. The area chart compares the used CPU/Memory cycles, recommended, and currently allocated.
Click on the number of generated decisions. You will see recommendations to improve the performance of this container - see below sample snapshot.
Click on any of these decisions to see full decision analysis. The decision analysis will help you understand how this recommendation could have improved your container’s performance. In the below decision analysis example, you will notice that Magalix is suggesting two changes:
In the next step how you will automatically resolve all those performance issues without manually going through all of these screens and numbers. But let’s see first how you can check for any capacity waste.
Capacity is wasted when containers get resources allocated to them more than they would use. Such a waste is quite common. Engineers would like to be at the safe side and consider the worst-case scenario. Let’s go again to the home dashboard and take a look at the capacity analysis card. This card provides an overview of:
Click on the decisions to see the full list of recommendations to save the identified wasted capacity.
You will see a list of decisions to improve utilization. You can filter by an entity and decision impact. Click on any decision to see the detailed analysis of its impact.
You will see the same decision analysis card. Magalix in the below-shared example is recommending to reduce allocated CPU and memory to save some resources. The card helps you to asses the recommendations by showing the historic and predicted metrics.
Magalix autopilot does the resources allocation automatically. It brings recommendations to life and executes them for you at the right times. Magalix proactively works on allocated resources to improve performance and resources utilization. Generated decisions are due in the near future, usually within 1 to 2 hours from generation time. When you enable the autopilot, the agent will execute the next set of decisions for your containers.
Autopilot is enabled at the level of the namespace. Once enabled, all decisions moving forward will be executed for you. You can enable the autopilot either from the cluster’s dashboard or from the namespace dashboard.
After applying the previous three steps, your cloud-native applications should now run without any performance penalties and with optimal resources utilization. It is time now to save some money and share some cool numbers with your manager and team :)
Magalix does a detailed analysis of your cluster nodes and provides recommendations to have the right capacity to achieve the highest ROI (Return on Investment) out of our cloud infrastructure. It will suggest the best combination of nodes and their sizes for best performance, utilization, and lowest cost possible.
Click on the Show Reports link to see the detailed recommendation and analysis of different options. In the below snapshot, Magalix Node Advisor given the recommendation to change nodes types and counts based on the workload analysis it has done last 12 to 24 hours. You will notice that the suggested capacity is lower than the current capacity to optimize current CPU utilization and provide around 45% cost savings based on demand pricing.
Magalix Node Advisor compares the pricing of different plans and how much you would be saving out of each one. In the below snapshot it is comparing three of the Google cloud billing plans.
You can configure the Node Advisor optimization policy for each of your clusters. The Node Advisor will initially work with the default configurations to maximize your savings. Its default configurations centered around increasing the density of your containers by decreasing the number of nodes whenever possible. However, you can configure it to match your specific environment needs. To tweak the Node Advisor to your needs, go to the cluster dashboard and click on the settings icon at the top right corner.
Below is the Node Advisor settings box. Each cluster has its own Node advisor settings. Settings change will be applied to the next analysis cycle.
- Magalix currently supports capacity and cost analysis on AWS, Azure, Google Cloud, and IBM.
- For on-prem and unsupported cloud infrastructure, Magalix provides recommendations on needed capacity without the cost analysis piece. It suggests the best per-node capacity, i.e. the number of cores and memory in GB, and the total number of nodes for your cluster.
It is even cooler to bring the rest of your team on-board and give them visibility to the performance and resources utilization of their applications. You can invite as many as you want to Magalix console. It will be always free to add team members.
Click on Invite Users at the top right menu.
Insert emails and names of users you would like to invite to Magalix. They will get their invitations immediately. They need to register using the email address you provided. They cannot use any other emails.
You can remove, de-activate or re-activate your team members access to Magalix console any time. Go to the top right menu and click on Manage Users to make any changes to your team.
If you haven’t connected your Kubernetes yet, Register and connect your existing cluster. Within a few minutes, cluster metrics will start flowing and our AI engine will engage shortly analyzing your containers and infrastructure.
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