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

A conceptual framework for measuring acceptance of contactless payment methods

Information Technology has been effective in every filed around the world, and ICT acceptance in any filed has produced new developments which has significantly contributed in that particular context. Driven by this impulse the Higher Education Institutes (HEI) around the world are incorporating ICT approaches and using the most recent advances to give world class education to inhabitants. In such manner Ministry of Education (MOE) Kuwait has propelled numerous plans and put some considerable measures in the execution of innovation in HEIs. HEI for the most part comprises of Universities. While there is no comprehensive study conducted in measuring the success and obstacles in the State of Kuwait, therefore, this paper focuses to measure and identify the success factors in acceptance of HEIs in the State of Kuwait. In the interim keeping in mind the end goal to evaluate the adoption and use of ICT by studnets and academicians in their day by day work, the UTAUT will be used as baseline theory to measure the ICT acceptance by students and academicians. This investigation likewise intents to discover the variables and issues keeping in mind the end goal to make a model for better usage of ICT by the HEIs. The effect of this investigation will be on HEI(s) to enhance the policy of learning methods through successful use of ICT and enhance the ability of students and academicians to be more viable and qy=quality-situated people

Determination of genotype differences through restriction endonuclease in Camels (Camelus dromedarius)

Tyrosinase gene or C locus has long been implicated in the coat colour determination. This gene a copper-containing enzyme located on chromosome 11q14.3 is expressed in melanocytes and controls the major steps in pigment production. In camel, C locus a restriction site provoked by the T variant of the mutation was used in a special restriction fragment length polymorphism analysis (PCR-RFLP) for genotyping of camels from six different Pakistani camel breeds (Marecha, Dhatti, Larri, Kohi, Campbelpuri and Sakrai). Significant differences in the genotype frequency between the breeds were estimated. The Sakrai breed showed in comparison to other studied breeds a distinctly higher frequency of the homozygous with restriction genotype. The objective of the present study was to screen the camel breeds using modern genetic technique that have been so far classified on the basis of performance and tribal ownership.Keywords: Camel, genotype, restriction endonucleas

A review of programming code assessment approaches

Learning computer programming language in harmony with practical coding actively while ensuring proper content progression is critical in introductory programming cources. Novice programmers usually face difficulty in acquiring the foundation level programming concepts adequately that usually lead to disappointmenet and ultimately back off. Bloom's Taxonomy has been generally adopted by the educators as a standard for assessing learning progression of students. In past there have been lot of research work on adopting Bloom's Taxonomy and its variants for computer programming languages, however, none has specially looked at an automatic mechanism to evaluate the six levels of Bloom's taxonomy on code level directly. In this paper we reviewed different approaches for assessment of programming code and discuss the challenges involved to implement the Bloom's taxonomy in programming languages directly on code level

Non-Standard Analysis for Regularization of Geometric-Zeno Behaviour in Hybrid Systems

Geometric-Zeno behaviour is a highly challenging problem in the analysis (including simulation) of hybrid systems. Geometric-Zeno can be defined as an infinite number of discrete mode switches in a finite time interval. Typically, for hybrid models exhibiting geometric-Zeno, the numerical simulation either halts or produces false results, because an infinite number of discrete events occur in a given simulation time-step. In this paper, we provide formal methods for regularization of geometric-Zeno behaviour by using a non-standard analysis. In particular, we provide formal conditions for the existence of geometric-Zeno in hybrid systems, and we propose methods to allow geometric-Zeno executions to be continued beyond geometric-Zeno limit points. The concepts are illustrated with a case study throughout the paper

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