The integration of innovative data mining and decision-making techniques in the context of higher education is a bold initiative towards enhanced performance. Predictive and descriptive analytics add interesting insights for significant aspects the education. The purpose of this article is to summarize a novel approach for the adoption of artificial intelligence (AI) techniques towards forecasting of academic performance. The added value of applying AI techniques for advanced decision making in education is the realization that the scientific approach to standard problems in academia, like the enhancement of academic performance is feasible. For the purpose of this research the authors promote a research in Saudi Arabia. The vision of the Knowledge Society in the Kingdom of Saudi Arabia is a critical milestone towards digital transformation. The human capital and the integration of industry and academia has to be based on holistic approaches to skills and competencies management. One of the main objectives of an academic decision maker is to ensure that academic resources are adequately planned and that students are properly advised. To achieve such an objective, an extensive analysis of large volumes of data may be required. This research develops a decision support system (DSS) that is based on an artificial neural network (ANN) model that can be deployed for effective academic planning and advising. The system is based on evaluating academic metrics against academic performance for students. The model integrates inputs from relevant academic data sources into an autonomous ANN. A simulation of real data on an ANN is conducted to validate the system's accuracy. Moreover, an ANN is compared with different mathematical approaches. The system enables the quality assurance of planning, advising, and the monitoring of academic decisions. The overall contribution of this work is a novel approach to the deployment of Artificial Intelligent for advanced decision making in higher education. In future work this model is integrated with big data and analytics research for advanced visualizations

Based on Petri net (PN) models of automated manufacturing systems, this paper proposes a deadlock prevention method to obtain a maximally permissive (optimal) supervisor while minimizing its structure. The optimal supervisor can be achieved by forbidding all first-met bad markings (FBMs) and permitting all legal markings in a PN model. An FBM obtained via a single transition's firing at a legal marking is a deadlock or marking that inevitably evolves into a deadlock. A lexicographic multiobjective integer programming problem with multiple objectives to be achieved sequentially is formulated to design such an optimal and structurally minimal supervisor. As a nonlinear function, the quantity of its directed arcs is minimized. A conversion method is proposed to convert the nonlinear model into a linear one. With the premise that each place in the supervisor is associated with a nonnegative place invariant, the controlled net holds all legal markings of the net model, and the supervisor has the minimal structure. Finally, some examples are used to illustrate the application of the proposed approach.

Data centers connect a larger number of servers requiring IO and switches with low power and delay. Virtualization of IO and network is crucial for these servers, which run virtual processes for computing, storage, and applications. We propose using the PCI Express (PCIe) protocol and a new PCIe switch fabric for IO and switch virtualization. The switch fabric has little data buffering, allowing up to 512 physical 10 Gb/s PCIe 2.0 lanes to be connected via a switch fabric on a chip. The switch is scalable with adapters running multiple adaptation protocols, such as Ethernet over PCIe, PCIe over Internet, or FibreChannel over Ethernet. Such adaptation protocols allow integration of IO often required for disjoint data center applications such as storage and networking. The novel switch fabric based on space-time carrier sensing facilitates high bandwidth, low power, and low delay multiprotocol switching. To achieve Terabit switching, both time (high transmission speed) and space (multi-stage interconnection network) technologies are required. In this paper, we present the design of an up to 256 lanes Close-network of multistage crossbar switch fabric for PCIe system. The switch core consists of 32 16x16 crossbar sub-switches. We also propose a new output contention resolution algorithm utilizing an out-of-band protocol of Request-To-Send (RTS), Clear-To-Send (CTS) before sending PCIe packets through the switch fabric. Preliminary power and delay estimates are provided.

Cloud computing recently emerged as a new paradigm that aims to deploy services via Internet. Under hybrid cloud environment, cloud bursting is a technique that combines local (organizations or in-house) resources with public cloud resources, these resources are leased based on a pay-per-use basis. It used to process the overload work within local resource or to accelerate the execution time of distributed applications with respect of the required level of QoS, also to achieve the efficient use of private resources. When Cloud bursting is applied, the important issues are determining how many and which type of resources will be provisioned. Also, the important issues that should be considered before cloud bursting decision are which workload will be burst to public cloud and when these resources will be released. These issues attract the attention of researchers to tackle them. In this paper we intend to review the recent researches that concern on cloud bursting and resource provisioning. We will explore how each study address the problem, what are the proposed solutions and what are the differences between them, the main researches are compared interms of several criteria such as type of application, which was targeted by the research, environment that used to implement the experiment, results, and limitations.

A performance model is presented for an optical packet switch architecture in which the wavelength converters are shared per output link and each output link consists of multiple fibers. Symmetry of the switch is exploited to derive the packet loss probability for the case where traffic is destined to different output ports with equal probability. The architecture performance is evaluated by means of an analytical model and confirmed by simulations under different switch parameter configurations. Wavelength converters are shown to improve the packet loss probability of the switch. The study shows that synchronous switches equipped with full conversion would have the least conversion utilization rate indicating that the use of a switch with less converter count, i.e., partial conversion, would offer better switch resources utilization and comparable packet loss performance

The performance of an optical switch that handles contention resolution using a fiber delay line (FDL) is modeled and evaluated. We propose using a simple optical buffer that contains only a single FDL for contention resolution. The analytical model derived for the performance of an optical switch based on this buffer is verified using simulations. The analytical model can be utilized with both packet and burst switching schemes to characterize the performance of switches augmented with this FDL architecture.

Multihop networks such as the Manhattan Street Network (MSN) are strong contenders for implementing high-speed networks, ranging from back-plane networks for cluster computing to metropolitan-area networks. Optical buffering may be used to augment optical multihop networks, and to reduce or even eliminate the need for optical-electrical conversions. This paper evaluates the performance enhancements obtainable by augmenting traditional MSN with optical buffering, thus combining optical buffering and deflection routing for contention resolution. The network is based on a novel 6/spl times/6 optical switching node. A great improvement in performance over the traditional MSN is obtained for a large range of network parameters.