Papers by Haeder Talib Mahde Alahmar
Today's real-time systems are the core of most ICT applications. The rapid development of such sy... more Today's real-time systems are the core of most ICT applications. The rapid development of such systems has attracted researchers' attention to optimize performance and to minimize as much as possible the problems and disadvantages they suffer in order to improve their performance in proportion to the volume of tasks entrusted to them. There are many major challenges facing real-time systems, which are mainly the problem of task scheduling on processor nuclei in the quantity of multi-core processors. Several methods have been proposed, including the general method, where any task can be executed on any kernel, the split method depends on the allocation of a specific kernel for each specific set of tasks. There is also the semi-fragmented method, which is a hybrid of the two previous methods, where a set of tasks is assigned to be executed on a particular kernel, while other functions are allowed to execute on any nucleus of the nucleus Treatment. In this paper we compare the performance of random task scheduling algorithms on a multi-core platform in order to determine the best algorithm in terms of a set of parameters adopted by researchers in this field, which in turn gives us precise details about the quality of such algorithms when applied to a set of distributed random tasks The unified logarithmic probability. The simso simulator, which has proven the reliability of high performance by many researchers in this field as well as provides the possibility of generating tasks according to specific probability distributions, and simulates accurate details in-depth characteristics of random tasks.
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Despite many developments and improvements in mobile devices, it still suffers from its limited c... more Despite many developments and improvements in mobile devices, it still suffers from its limited capabilities such as short battery life, limited processing capabilities, and small storage spaces that can be overcome by canceling the implementation locally and attributing it to the cloud, in this research all cases have been applied to methods Implementation in mobile applications, which can be either Java methods, (C++) methods, or CUDA methods that require calculations to be performed on the cloud server's graphics processor (GPU).We have been working on proposing and developing an algorithm for decision-making in attributing implementation before starting it, according to the network status, as well as developing an application using (Android Studio) accompanying this research, through which the best methods of implementation can be determined and thus it becomes possible to make the final decision in determining a clear mechanism for implementation and choosing between two states, either the implementation is local or on the cloud server. The results are set for each possible implementation case (locally-remotely), based on the decision-making algorithm by determining the implementation time taking into account the mobile device CPU speed, network performance, application program characteristics, and cloud server efficiency. It is important to rely in this research on taking into account the CPU speed of the mobile device, network performance, application program characteristics, and the efficiency of the cloud server to reach the best results that were reviewed in this research with the use of different platforms to choose the best one in accomplishing our work. The actual results of the applied algorithm proved a significant saving in implementation time with the increase in the request to perform tasks on the cloud server and with the increasing complexity of the issue.
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Papers by Haeder Talib Mahde Alahmar