The architecture of Mesos
Mesos is an open-source platform for sharing clusters of commodity servers between different distributed applications (or frameworks), such as Hadoop, Spark, and Kafka among others. The idea is to act as a centralized cluster manager by pooling together all the physical resources of the cluster and making it available as a single reservoir of highly available resources for all the different frameworks to utilize. For example, if an organization has one 10-node cluster (16 CPUs and 64 GB RAM) and another 5-node cluster (4 CPUs and 16 GB RAM), then Mesos can be leveraged to pool them into one virtual cluster of 720 GB RAM and 180 CPUs, where multiple distributed applications can be run. Sharing resources in this fashion greatly improves cluster utilization and eliminates the need for an expensive data replication process per-framework.
Some of the important features of Mesos are:
- Scalability: It can elastically scale to over 50,000 nodes
- Resource isolation: This is achieved through Linux/Docker containers
- Efficiency: This is achieved through CPU and memory-aware resource scheduling across multiple frameworks
- High availability: This is through Apache ZooKeeper
- Monitoring Interface: A Web UI for monitoring the cluster state
Mesos is based on the same principles as the Linux kernel and aims to provide a highly available, scalable, and fault-tolerant base for enabling various frameworks to share cluster resources effectively and in isolation. Distributed applications are varied and continuously evolving, a fact that leads Mesos design philosophy towards a thin interface that allows an efficient resource allocation between different frameworks and delegates the task of scheduling and job execution to the frameworks themselves. The two advantages of doing so are:
- Different frame data replication works can independently devise methods to address their data locality, fault-tolerance, and other such needs
- It simplifies the Mesos codebase and allows it to be scalable, flexible, robust, and agile
Mesos' architecture hands over the responsibility of scheduling tasks to the respective frameworks by employing a resource offer abstraction that packages a set of resources and makes offers to each framework. The Mesos master node decides the quantity of resources to offer each framework, while each framework decides which resource offers to accept and which tasks to execute on these accepted resources. This method of resource allocation is shown to achieve a good degree of data locality for each framework sharing the same cluster.
An alternative architecture would implement a global scheduler that took framework requirements, organizational priorities, and resource availability as inputs and provided a task schedule breakdown by framework and resource as output, essentially acting as a matchmaker for jobs and resources with priorities acting as constraints. The challenges with this architecture, such as developing a robust API that could capture all the varied requirements of different frameworks, anticipating new frameworks, and solving a complex scheduling problem for millions of jobs, made the former approach a much more attractive option for the creators.
