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A Resource and Policy Aware VM Scheduler for Medium Scale Clouds
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Latest commit 7757859 Feb 20, 2015

README.md

Smart Cloud Scheduler

##Problem Many universities and enterprises are now setting up their own small-to-medium scale private clouds. Such private cloud are becoming popular, as they provide the ability to multiplex existing computing and storage resources within an organization while supporting diverse applications and platforms. It also provides better performance, control, and privacy. In a medium-scale cloud such as a university or enterprise cloud, there are different types of users including students, lecturers, internal/external researchers, and developers, who get benefits from the cloud in different ways. They may have varying requirements in terms of resources, priorities, and allocation periods for the resources. These different requirements may include processing intensive and memory intensive applications such as HPC (High Performance Computing) applications and data mining application, as well as labs which needs to be deployed on a specific set of hosts and for a particular period of time. Priority schemes and Dynamic VM (Virtual Machine) migration schemes should be used to satisfy all these requirements in an organized manner. However, currently known IaaS cloud platforms have no native capability to perform such dynamic resource allocations and VM preemption mechanisms. Therefore, it is important to extend existing cloud platforms to provide such policy, resource, and deadline aware VM scheduling.

##So what's the solution?

We are proposing a resource scheduling mechanism which can be used as an extension to an existing IaaS cloud platform to support dynamic resource and policy aware VM scheduling for Medium Scale Clouds. Resource scheduling algorithm schedules VMs while being aware of the capabilities of the cloud hosts and current resource usage, which is monitored continuously using a resource monitor. The resource scheduler will allocate resources according to predefined priority levels of a particular user who issued the resource request. Time-based scheduling (e.g., deploying labs for a particular period of time) is also performed while considering the priority levels and existing resource allocations. To provide these features we extend an existing IaaS cloud platform to include resource and policy aware VM creation, migration, and preemption.

This repository contains the implementation of Smart Cloud Scheduler.

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