“Risk Management: A comparative study of regulations and practices in one conventional and one Islamic bank in Pakistan”

Masteroppgave i økonomistyring - Nord universitet 201

Prediction-based resource allocation model for real time tasks

High performance computing (HPC) platforms provides computing, storage and communication facilities to process real-time applications efficiently. Such applications produce less important results if the deadlines are missed. Most of the real-time algorithms decently schedule applications tasks offline, but they usually take longer in processing which results in deadlines miss when tasks need some data from remote storage locations. In this paper, we propose a prediction-based model which analyze task feasibility before scheduling on the HPC resources when tasks have data-intensive constraints. The main advantage of the prediction analysis modules is to save time by refraining further analysis on non-scheduled tasks. The model helps in searching suitable resources and improved resource utilization by considering task workload in advance

Time and cost efficient cloud resource allocation for real-time data-intensive smart systems

Cloud computing is the de facto platform for deploying resource-and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time-and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts

A theoretical model of healthcare monitoring surveillance system for patients with severe allergies

Allergies influenced by some unwanted foods, dust, and pollens create quick reactions, which may be basic skin diseases, life-threatening disorders, and serious breathing problems such as asthma. The most severe allergic reaction data is quickly and efficiently handled by the healthcare monitoring systems when merged with the information and communication technologies (ICT). A four-month study was conducted with the existing healthcare monitoring systems to know how quickly they provide solutions to the patients suffering from severe allergies. The study comprises collecting patients data, especially children under the age of five years, which include regular and specific food they eat, hygienic or dusty surrounding, environmental conditions influenced by allergy creation actors and quality of life before and after the allergic reactions. Accuracy of the data depends on the efficiency of the Healthcare Monitoring Surveillance System (HMSS), which employs the Multiple-Input Multiple-Output (MIMO) scheme as a new mechanism. In this research work, HMSS with MIMO technology provides not only better accuracy, quality of life, and quickness as compared to existing state-of-the-art HMSSs, but also improves the lifetime of the monitoring system with reliability, maintainability, and availability. The produced results show the supremacy of the proposed mechanism when accuracy is the main concern

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

An efficient resource allocation scheme plays a vital role in scheduling applications on high-performance computing resources in order to achieve desired level of service. The major part of the existing literature on resource allocation is covered by the real-time services having timing constraints as primary parameter. Resource allocation schemes for the real-time services have been designed with various architectures (static, dynamic, centralized, or distributed) and quality of service criteria (cost efficiency, completion time minimization, energy efficiency, and memory optimization). In this analysis, numerous resource allocation schemes for real-time services in various high-performance computing (distributed and non-distributed) domains have been studied and compared on the basis of common parameters such as application type, operational environment, optimization goal, architecture, system size, resource type, optimality, simulation tool, comparison technique, and input data. The basic aim of this study is to provide a consolidated platform to the researchers working on scheduling and allocating high-performance computing resources to the real-time services. This work comprehensively discusses, integrates, analysis, and categorizes all resource allocation schemes for real-time services into five high-performance computing classes: grid, cloud, edge, fog, and multicore computing systems. The workflow representations of the studied schemes help the readers in understanding basic working and architectures of these mechanisms in order to investigate further research gap

Patterns of skeletal class II in patients reporting to Ayub Dental Section

Researchers have used a variety of techniques to distinguish between the distinct elements of malocclusion. While some academics have used various indices to determine prevalence, the majority of researchers have followed Angle's categorization. Individual malocclusion studies, such as those looking at class II or class III malocclusion, have employed cephalometric analysis. Objectives: There is currently a lack of information in the literature about the prevalence of malocclusion in the Abbottabad region generally in the city among people of different ages that might benefit from orthodontic services. This study's goal was to assess local population skeletal class II patterns in order to better treat patients at the appropriate time by utilizing variations in skeletal class II patterns' growth periods. Methodology: From February 2022 to April 2023, the Department of Orthodontics at Ayub Dental Section, Abbottabad, conducted this retrospective (cross-sectional) study. The institutional ethical committee at Ayub Medical College in Abbottabad gave its approval to the plan. On a specially created proforma, data for 100 patients were collected using prior records. Patients of either gender who had skeletal class II jaw relationships with any dental relationships and were between the ages of 8 and 35 were required to meet the inclusion criteria