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This paper evaluates a mechanism for applying machine learning (ML) to identify over-constrained IaaS virtual machines (VMs). Herein, over-constrained VMs are defined as those who are not given sufficient system resources to meet their workload specific objective functions. To validate our approach, a variety of workload-specific benchmarks inspired by common Infrastructure-as-a-Service (IaaS) cloud...
This paper evaluates a mechanism for applying machine learning (ML) to identify over-constrained IaaS virtual machines (VMs). Herein, over-constrained VMs are defined as those who are not given sufficient system resources to meet their workload specific objective functions. To validate our approach, a variety of workload-specific benchmarks inspired by common Infrastructure-as-a-Service (IaaS) cloud...
Modern virtual machine (VM) management software enables consolidation of VMs for power savings or load-balancing for performance. While existing literature provides various methods for computing a better load-balanced, or consolidated goal state, it fails to adequately suggest the best path from the system’s current state to the desired goal allocation. This paper discusses an approach to efficient...
Modern virtual machine (VM) management software includes components that enable consolidation of VMs for power savings or load-balancing for performance. While the existing literature provides various methods for computing a better load-balanced, or consolidated goal state, it fails to adequately suggest the best path from the system's current state to the desired goal allocation. This paper discusses...
In order to produce quality products, companies require new engineering students that have good problem solving, debugging, and analysis skills. Many graduates enter the work force with exceptional development skills, but lack proficiency in test, debugging, and analysis skills. This is in part because academic curricula emphasize development at the expense of teaching software testing as a formal...
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