A Novel Virtual Machine Migration Model for Optimizing Cloud Resource Utilization
DOI:
https://doi.org/10.5281/zenodo.20395041Keywords:
Load Balancing, Cloud Computing, Energy-Efficient Computing, Virtual Machine Migration, Resource Utilization, SLA-Aware OptimizationAbstract
Cloud Computing has emerged as an important paradigm for delivery of scalable computing and storage resources using virtualization techniques. As cloud workloads grow rapidly, however, virtual machine (VM) overloading, energy usage, SLA violations and low system performance are common results. To address these issues, this work proposes an adaptive VM migration design for efficient resource utilization and VM load-balancing in a cloud environment. First, the proposed approach proposes to allocate VMs based on resources, taking into account the availability of processor resources, memory, and network conditions. A threshold-based migration strategy is then used to identify the overloaded VMs and migrate them automatically to appropriate physical machines which have lesser usage. Furthermore, to reduce the migration overhead and enhance the computation efficiency, the cost estimation based on latency, communication delay and hop distance are introduced for migration. An optimization model is also formulated to optimize the use of the energy and minimize the migration cost. Up to 200 virtual machines, with various host utilization thresholds and 40 simulation runs, are used in experimental analysis. Results show that the proposed method is able to decrease the number of active hosts, energy consumption and the frequency of VM migration when compared to other approaches like FFO-EVM and ACO. From the results obtained, it is concluded that the proposed adaptive migration framework is effective in enhancing the performance of the cloud, utilization of resources and efficiency of task completion with low computational overhead.
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