On an effective scheduling problem in computation clusters

At present, big companies such as Amazon, Google, Facebook, Microsoft, Yahoo! own huge datacenters with thousands of nodes. These clusters are used simultaneously by many users. The users submit jobs containing one or more tasks. Task flow is usually a mix of short, long, interactive, batch, and tasks with different priorities. Cluster scheduler decides on which server to run the task, where the task is then run as a process, container or a virtual machine. Scheduler optimizations are important as they provide higher server utilization, lower latency, improved load balancing, and fault tolerance. Achieving good task placement is hard. The problem has multiple dimensions and requires algorithmically complex optimizations. This increases placement latency and limits cluster scalability. In this paper we consider different cluster scheduler architectures and optimization problems.