Kazuhm SaaS platform unifies the compute resources of an organization from desktops, to servers, to cloud, to edge, creating a private grid to place and process containerized workloads, optimize IT costs, security, and performance.
Through an easy user interface, customers leverage Kazuhm today to simplify Kubernetes and the deployment of popular data science applications, build their own private distributed compute networks, run workloads on-premises enabling the lowest possible latency, and easily manage multi-cloud and hybrid cloud environments.
Kubernetes-Made-Easy -- Set up and cluster deployment is super quick with container placement and host monitoring intuitively simple.
Multi-Cloud, Hybrid-Cloud Management -- Escape from vendor lock-in and centrally manage all your Public Cloud Hosts for FREE.
Data Science On Demand -- Simplify deployment of Spark and Jupyter and process workloads both on-premise and in the cloud.
Offset Cloud Costs -- Get “Cloud Smart”. Process containerized workloads on your Linux and Windows desktops and servers to offset cloud costs.
Low-Latency Workload Processing -- Reduce latency and improve performance by processing your data on-premise or at the edge – when milliseconds count.
Distributed Computing Anywhere -- Connect your desktops, both Windows and Linux, and servers or even your edge devices to create a powerful compute fabric.
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Based on our record, Apache Mesos seems to be more popular. It has been mentiond 7 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
When we adopted Kubernetes at Criteo, we encountered initial hurdles. In 2018, Kubernetes operators were still new, and there was internal competition from Mesos. We addressed these challenges by validating Kubernetes performance for our specific needs and building custom Chef recipes, StatefulSet hooks, and startup scripts. - Source: dev.to / about 2 months ago
In the beginning, there was docker. In 2013, building on linux internals, docker packaged containers for mass adoption and made it easy to share a complete runtime environment for an application across the network. Check out their first demo at PyCon 2013 (I was there!) At the time, serious workloads ran on something like Mesos, which was not “container-native” and had its own way of packaging and distributing... - Source: dev.to / 3 months ago
Distribution of containers to servers, clusters, and data centers Keeping applications up and running with the required number of instances Upgrading applications without downtime These issues are also known as cloud-native characteristics of modern applications. Therefore, a need for container orchestration systems has arisen. There are three leading container orchestrators on the market: Docker Swarm... - Source: dev.to / 10 months ago
Https://mesos.apache.org/ >Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines. - Source: Hacker News / over 1 year ago
Spark works locally on stand-alone clusters and on Hadoop YARN, Apache Mesos, Kubernetes, and other managed Hadoop platforms. - Source: dev.to / over 1 year ago
Activeeon - ProActive Workflows & Scheduling is a java-based cross-platform workflow scheduler and resource manager that is able to run workflow tasks in multiple languages and multiple environments: Windows, Linux, Mac, Unix, etc.
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
Mesosphere DCOS - Mesosphere DCOS organizes your entire infrastructure as if it was a single computer.
BOINC - BOINC is an open-source software platform for computing using volunteered resources
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Charity Engine - Charity Engine takes enormous, expensive computing jobs and chops them into 1000s of small pieces...