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Hyper-V Server 2008 was released on October 1, 2008. It consists of Windows Server 2008 Server Core and Hyper-V role; other Windows Server 2008 roles are disabled, and there are limited Windows services. [9] Hyper-V Server 2008 is limited to a command-line interface used to configure the host OS, physical hardware, and software. A menu driven ...
SCVMM is designed for management of large numbers of Virtual Servers based on Microsoft Virtual Server and Hyper-V, and was released for enterprise customers in October 2007. [1] A standalone version for small and medium business customers is available.
Cluster Shared Volumes (CSV) is a feature of Failover Clustering first introduced in Windows Server 2008 R2 for use with the Hyper-V role. A Cluster Shared Volume is a shared disk containing an NTFS or ReFS (ReFS: Windows Server 2012 R2 or newer) volume that is made accessible for read and write operations by all nodes within a Windows Server Failover Cluster.
A batch file may contain any command the interpreter accepts interactively and use constructs that enable conditional branching and looping within the batch file, such as IF, FOR, and GOTO labels. The term "batch" is from batch processing, meaning "non-interactive execution", though a batch file might not process a batch of multiple data.
The App-V stack sandboxes the execution environment so that an application does not make changes directly to the underlying operating system's file system and/or Windows Registry, but rather is contained in an application-specific "bubble". App-V applications are also sandboxed from each other, so that different versions of the same application ...
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The concept behind a fork bomb — the processes continually replicate themselves, potentially causing a denial of service. In computing, a fork bomb (also called rabbit virus) is a denial-of-service (DoS) attack wherein a process continually replicates itself to deplete available system resources, slowing down or crashing the system due to resource starvation.
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).