The document discusses improving resource utilization in cloud environments using multivariate probabilistic models. It notes that static scheduling approaches for allocating virtual machines to physical machines often results in underutilized resources. The proposed algorithm uses a multivariate probabilistic model to select suitable physical machines for virtual machine reallocation, generating a reconfiguration plan that considers the multi-dimensional characteristics of virtual machines and physical machines in order to improve resource utilization while reducing migration costs.

1. Improving Resource Utilisation in the Cloud Environment using
Multivariate Probabilistic Models
 Resource provisioning based on virtual machine (VM) has been widely accepted and adopted in cloud
computing environments.
 A key problem resulting from using static scheduling approaches for allocating VMs on different physical
machines (PMs) is that resources tend to be not fully utilised.
 Although some existing cloud reconfiguration algorithms have been developed to address the problem, they
normally result in high migration costs and low resource utilisation due to ignoring the multi-dimensional
characteristics of VMs and PMs.
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By using a multivariate probabilistic model, our algorithm selects suitable PMs for VM re-allocation which
are then used to generate a reconfiguration plan. We also describe two heuristics metrics which can be used
in the algorithm to capture the multi-dimensional characteristics of VMs and PMs.
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The virtualisation technology coupled with cloud reconfiguration algorithms enables more efficient
cloud resource utilisation in Internet Data Centres.
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For better resource utilisation, many cloud providers start with static allocation of VMs to physical
machines (PMs) using a resource scheduler
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It uses a multivariate probabilistic normal distribution model to select suitable PMs for VM reallocation before a reconfiguration plan is generated, which leads to less number of VMs.