Impact of Resource over-Reservation (ROR) and Dropping Policies on Cloud Resource Allocation

In Cloud environment, Cloud Providers (CP) grant access to computing and storage resources through Web-portals. Resource virtualization is a key enabler for providing such services. Thanks to virtualization, multiple Virtual Machines (VMs) can be hosted by the same Physical Machine (PM). Existing CPs, such as Amazon Web Services and Windows Azure, offer pre-configured VMs with fixed-size computing, storage and network capacities. In this context, end-users can only choose from a set of predefined VM instances offered by the CPs. However, it is expected that, in the near-future, end-users will be able to access the Cloud without any restriction on the size of the required resources. They will be charged according to the amount of resources used. In such scenario, the major problem faced by a CP is to select the appropriate PM that will host a new VM while still satisfying the end-user requirements. This resource allocation problem is similar to the well-known online Bin Packing problem. In this paper, we investigate several algorithms that were proposed to solve the Bin Packing problem, and compare them in a Cloud environment in terms of resource utilization and percentage of dropped VMs. The novel concept of Resource Over-Reservation (ROR) is introduced as a mean to reduce the percentage of dropped VMs and to improve resource utilization.