Modern applications are highly dependent on data. The manifold increase in the data generated and processed by organizations is continually changing the way we store and process it. When planning modern infrastructure for storing and processing the data, we can no longer hope to simply buy hardware with more capacity to solve the problem. Different frameworks for batch processing, stream processing, user-facing services, graph processing, and ad hoc analysis are every bit as important as the hardware they run on. These frameworks are the applications that power the data center world.

The size and variety of big data means traditional scale-up strategies are no longer adequate for modern workloads. Thus, large organizations have moved to distributed processing, where a large number of computers act as a single giant computer. The cluster is shared by many applications with varying resource requirements, and the efficient sharing of resources at this scale among multiple frameworks is the key to achieving high utilization. There is a need to consider all these machines as a single warehouse scale computer. Mesos is designed to be the kernel of such computers.

Traditionally, frameworks run in silos and resources are statically partitioned among them, which leads to an inefficient use of resources. The need to consider a large number of commodity machines as a single computer, and the ability to share resources in an elastic manner by all the frameworks requires a cluster computing framework. Mesos is inspired by the idea of sharing resources in a cluster between multiple frameworks while providing resource isolation.