A Hybrid Scheduling Algorithm to improve the Performance using Ant Colony Optimization and Cuckoo Search Algorithm with K-means++ in a Virtualized Environment
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Abstract
Virtualization plays a vital role in cloud computing. It provides better manageability, availability, optimistic provisioning, scalability
and resource utilization in current cloud computing environments. However the performance isolation is a major concern in virtualization. The
performance of the application running inside the virtual machine gets affected by the interference of the co-located virtual machines. This
approach provides a novel task scheduling mechanism that provides the proper management of resource allocation among the virtual machines
running simultaneously. An interference prediction scheme is proposed to utilize the application characteristics collected from the VMs to
maintain a low system overhead. The Nelder-mead method is used to frame the relationship model from the observed response and control
variables. The hybrid algorithm: Ant Colony Optimization and Cuckoo search algorithm with K-means++ is adopted for task scheduling process.
This approach shows effective performance improvements in terms of throughput and execution time.
Keywords: Virtualization; Ant Colony optimization; Cuckoo search; K-means++ algorithm; Throughput; Performance Interference.
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