Cloud computing is a groundbreaking technique that provides a whole lot of facilities such as storage, memory, and CPU as well as facilities such as servers and web service. It allows businesses and individuals to subcontract their computing needs as well as trust a network provider with its data warehousing and processing. The fact remains that cloud computing is a resource-finite domain where cloud users contend for available resources to carry out desired tasks. Resource management (RM) is a process that deals with the procurement and release of resources. The management of cloud resources is desirable for improved usage and service delivery. In this paper, we reviewed various resource management techniques embraced in literature. We concentrated majorly on investigating game-theoretic submission for the management of required resources, as a potential solution in modeling the resource allocation, scheduling, provisioning, and load balancing problems in cloud computing. This paper presents a survey of several game-theoretic techniques implemented in cloud computing resource management. Based on this survey, we presented a guideline to aid the adoption and utilization of game-theoretic resource management strategy.

With the wide use of facial verification and authentication systems, the performance evaluation of Spoofing Attack Detection (SAD) module in the systems is important, because poor performance leads to successful face spoofing attacks. Previous studies on face SAD used a pretrained Visual Geometry Group (VGG) -16 architecture to extract feature maps from face images using the convolutional layers, and trained a face SAD model to classify real and fake face images, obtaining poor performance for unseen face images. Therefore, this study aimed to evaluate the performance of VGG-19 face SAD model. Experimental approach was used to build the model. VGG-19 algorithm was used to extract Red Green Blue (RGB) and deep neural network features from the face datasets. Evaluation results showed that the performance of the VGG-19 face SAD model improved by 6% compared with the state-of-the-art approaches, with the lowest equal error rate (EER) of 0.4%. In addition, the model had strong generalization ability in top-1 accuracy, threshold operation, quality test, fake face test, equal error rate, and overall test standard evaluation metrics.