This paper introduces a grammar for Arabic nominal sentences written in the formalism of Head-Driven Phrase Structure Grammar (HPSG). This grammar covers simple Arabic nominal sentences, though the same approach can be extended to cover other types of Arabic nominal sentences. The formalization has been implemented using the Linguistic Knowledge Building (LKB) system.
Cloud computing providers in the infrastructure as a service (IaaS) layer provide their utility computing and IT services as virtual machines to customers, who then pay for resources based on time usage. One of the most subtle challenges is pricing stagnant resources dynamically, which combines the static pricing strategy of active resources to maximize cloud computing profits. This paper investigates cloud dynamic pricing and proposes an efficient model that manages virtual machines in regards to revenue management, formulating the maximum expected reward under discrete finite horizon Markovian decisions, characterizing model properties under optimum controlling conditions, approximating optimal dynamic programming policy using a linear programming approach, developing a new algorithm based on this approximation, and finally presenting evaluation results. Our results provide fundamental insights into cloud computing revenue.
Resource management for cloud computing environments that are characterized by many layers emerges as a critical task for cloud computing providers. Such providers are compelled by the demands and strategies of stochastic customers to adopt dynamic resource management for the top-bottom scaling of the cloud resources on the basis of variable needs. Resource management in the infrastructure as a service layer relies on virtual machine (VM) characteristics, such as estimated VM classes. Given that a cloud provider offers a variety of VM classes that differ as regards the size of computing resources (e.g., central processing unit, memory, and input/output devices), optimizing cloud resources to maximize cloud revenue is a challenging dilemma. More specifically, the dynamic management of resources in cloud spot markets is confronted with various severe obstacles. In consideration of these issues, this study investigated a dynamic resource management model for cloud spot markets and put forward an efficient model that manages spare resources for the purpose of expanding cloud revenue. The model estimates the available spare capacity of a spot market, evaluates the maximum expected revenue of stagnant VMs on the basis estimated cumulative capacity, and locates the optimum VM combinations that bear complementary workloads and capacities and can coexist in a certain host. Our model also improves the understanding of cloud resource scaling and generates inferences that can be adopted in managing cloud resources for all layers as well as Reserved and On-Demand markets.
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