Apache Mesos Platform
An Apache Mesos Platform is a cluster management platform that manages distributed hardware resources into a single pool of computing resources that can be used by application frameworks to efficiently manage workload distribution for both batch jobs and long-running services.
- Context:
- Website: https://mesos.apache.org/
- Repository: https://gitbox.apache.org/repos/asf?p=mesos.git
- It was first developed by Benjamin Hindman, Andy Konwinski, and Matei Zaharia, and Ion Stoica (Hindman et al., 2011).
- It was released in 2020 Apache Software Foundation under an Apache License 2.0.
- It can manage a Mesos Cluster (composed of a Mesos Master Server and Mesos Salves).
- It includes the following technology:
- Example(s):
- Mesos v0.28.2, 2016-06-04 http://archive.apache.org/dist/mesos/0.28.2/
- Mesos v0.12.1, 2013-08-11 http://archive.apache.org/dist/mesos/0.12.1/
- …
- Counter-Example(s):
- See: Data Center, JobServer, OS Resource Management, Zookeeper, Distributed Systems Kernel, Marathon Init Service, Mesosphere Marathon, Virtualization System, Spark Standalone Manager.
References
2024a
- (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Apache_Mesos Retrieved:2024-5-12.
- Apache Mesos is an open-source project to manage computer clusters. It was developed at the University of California, Berkeley.
2024b
- (ChatGPT-OpenAI, 2024) ⇒ Prompt: Summarize the concept Apache Mesos in 1- 2 paragraphs. ChatGPT (model:gpt-4-turbo-preview). Retrieved: 2024-05-12.
- QUOTE: Apache Mesos is a powerful open-source project designed to manage computer clusters more efficiently. It provides a unified interface for the distribution and management of resources across all the machines in a network or cluster, making it easier for developers and administrators to deploy and run distributed applications, no matter the size or complexity of their infrastructure. By abstracting the resources available on each individual machine and presenting them as a single pool of resources, Mesos enables dynamic resource sharing and isolation, which in turn improves the scalability and fault tolerance of applications. This is particularly beneficial for running large-scale applications, such as those needed by big data analytics and complex web services.
The core idea behind Mesos is to optimize the use of resources through effective scheduling and resource allocation, making sure that tasks are executed where and when they are most efficient. It supports a wide variety of execution environments through its containerization capabilities and can seamlessly integrate with other container orchestration tools like Docker and Kubernetes, as well as big data frameworks such as Hadoop and Spark. Its pluggable architecture makes it highly versatile for a range of deployment scenarios. Apache Mesos stands out by providing a layer that not only facilitates application deployment but also enhances overall system performance and resilience.
- QUOTE: Apache Mesos is a powerful open-source project designed to manage computer clusters more efficiently. It provides a unified interface for the distribution and management of resources across all the machines in a network or cluster, making it easier for developers and administrators to deploy and run distributed applications, no matter the size or complexity of their infrastructure. By abstracting the resources available on each individual machine and presenting them as a single pool of resources, Mesos enables dynamic resource sharing and isolation, which in turn improves the scalability and fault tolerance of applications. This is particularly beneficial for running large-scale applications, such as those needed by big data analytics and complex web services.
2015
- (Apache Mesos, 2015) ⇒ http://mesos.apache.org/
- QUOTE: Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively. …
… Mesos is built using the same principles as the Linux kernel, only at a different level of abstraction. The Mesos kernel runs on every machine and provides applications (e.g., Hadoop, Spark, Kafka, Elastic Search) with API’s for resource management and scheduling across entire datacenter and cloud environments.
- QUOTE: Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively. …
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/JobServer# Retrieved:2014-11-20.
- Mesos is a cluster management platform that manages distributed hardware resources into a single pool of resources that can be used by application frameworks to efficiently manage workload distribution for both batch jobs and long-running services. JobServer functions as a Mesos application framework to distribute jobs on a cluster of servers. JobServer integrates with Mesos by sending jobs to Mesos for execution and for efficient resource allocation.
2011
- (Hindman et al., 2011) ⇒ Benjamin Hindman, Andy Konwinski, Matei Zaharia, Ali Ghodsi, Anthony D. Joseph, Randy Katz, Scott Shenker, and Ion Stoica (2011). "Mesos: A platform for Fine-Grained resource sharing in the data center". Im: 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI 11).
- QUOTE: Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today’s frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them.
Figure 2: Mesos architecture diagram, showing two running frameworks (Hadoop and MPI).
- QUOTE: Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today’s frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them.
