Abstract
Data centers are the main nerve of the Internet because of its hosting, storage, cloud computing and other services. All these services require a lot of work and resources, such as energy and cooling. The main problem is how to improve the work of data centers through increased resource utilization by using virtual host simulations and exploiting all server resources. In this paper, we have considered memory resources, where Virtual machines were distributed to hosts after comparing the virtual machines with the host from where the memory and putting the virtual machine on the appropriate host, this will reduce the host machines in the data centers and this will improve the performance of the data centers, in terms of power consumption and the number of servers used and cost.
Highlights
Face recognition techniques are very important, especially in security systems
This work proposes a technique to recognize faces using moment shape descriptors applied on the global whole face and different face parts (Right eye, left eye, mouth, and nose), a feature selection process based on Sequential Forward Feature Selection (SFFS) and Feed Forward Neural Network to test this technique on face images of CARL database
RELATED WORK: Extensive studies have been proposed for face recognition in [11], [12], some of these studies used the moment invariant techniques as a robust statistical shape descriptor [7], [13]
Summary
Face recognition techniques are very important, especially in security systems. To identify a face shape, the face should be represented with specific features [1]. The most utilized descriptors are the moment invariants which go about as "The first choice descriptors" [3] which is a standout amongst the critical strategies for making an estimate of the implementation about different descriptor types. Regardless of those distributed studies, a considerable measure of issues even must be solved [3]-[6]. This work proposes a technique to recognize faces using moment shape descriptors applied on the global whole face and different face parts (Right eye, left eye, mouth, and nose), a feature selection process based on SFFS and Feed Forward Neural Network to test this technique on face images of CARL database
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