The Video requests can be streamed in two forms. They are the live streaming and the on-demand streaming. Both of them should be adapted (I.e., transcoded) to fit the characteristics (e.g., spatial resolution, bit rate… and the supported formats) of client devices. Therefore, many streaming service providers are presented the cloud services to be utilized in the video transcoding. But, the introducing of the cloud services for video transcoding is encountered by the contradiction between the deploying cloud resources in a cost-efficient without any major influence on the quality of video streams. In order to address this problem, this paper presents an Enchantment Cloud-based Video Streaming using the Heterogeneous Resource Allocation (CVSHR) to transcode the video streams on cloud resources in an efficient manner with the QoS of the requested video stream. The system architecture is elastic and based on multiple heterogeneous clusters that provide a great flexible resource allocation and De-allocation strategy. This strategy aims to assign a suitable VM with adequate resources based on the GOPs characteristic. Also, it can reassign the unused resources. In addition, the number of VMs can be extended as the system necessity. Finally, The CVSHR is simulated and evaluated on truthful cloud resources and various workload circumstances.

Cloud computing is an innovative technology which is based on the internet to preserve large applications. It is warehoused as a shared data over one platform. In addition, it offers better services to clients who belong to different organizations. In spite of the maximum utilization of computational resources provided by the cloud computing with lower cost, it suffers from specific restrictions. These restrictions are encountered through the load balancing of data in the cloud data centers. These restrictions are represented in the less bandwidth utilization, resource limitations, fault tolerance and security etc. In order to overcome these limitations, new computing model called Fog Computing is presented. It aims to offer the required service of the sensitive data to end users without delaying. The function of the fog computing is similar to the cloud computing with two preferred advantages. The first one is that it is placed more near to the end users to introduce its service in less time. Secondly, it is more valuable for streaming the real time applications, sensor networks, IOT which need high speed and reliable internet connection. In this paper, a novel load balancing algorithm has been proposed over a novel architectural model in the Fog Computing environment. The proposed model aims to serve the real-time tasks within their deadline. In addition, it serves the different soft tasks without starving. The soft tasks are classified according to the execution time and the priority levels. In addition, they are served according to their waiting time and priority-level. Furthermore, the proposed algorithm is employed to maximize the throughput, the resources and the network utilization and preserving the data consistency with less complexity to accomplish the end users demand.

No abstract

Internet of Things (IoT) has been industrially investigated as Platforms as a Services (PaaS). The naive design of these types of services is to join the classic centralized Cloud computing infrastructure with IoT services. This joining is also called CoT (Cloud of Things). In spite of the increasing resource utilization of cloud computing, but it faces different challenges such as high latency, network failure, resource limitations, fault tolerance and security etc. In order to address these challenges, fog computing is used. Fog computing is an extension of the cloud system, which provides closer resources to IoT devices. It is worth mentioning that the scheduling mechanisms of IoT services work as a pivotal function in resource allocation for the cloud, or fog computing. The scheduling methods guarantee the high availability and maximize utilization of the system resources. Most of the previous scheduling methods are based on centralized scheduling node, which represents a bottleneck for the system. In this paper, we propose a new scheduling model for manage real time and soft service requests in Fog systems, which is called Decentralize Load-Balance Scheduling (DLBS). The proposed model provides decentralized load balancing control algorithm. This model distributes the load based on the type of the service requests and the load status of each fog node. Moreover, this model spreads the load between system nodes like wind flow, it migrates the tasks from the high load node to the closest low load node. Hence the load is expanded overall the system dynamically. Finally, The DLBS is simulated and evaluated on truthful fog environment.

Cardiovascular Diseases (CVDs) diagnosis requires an expert interpretation of ECG (Electrocardiogram). The ECG is an essential tool that is used to diagnose CVDs for medical treatment to take place. The ECG represents the electrical events of the cardiac cycle which coordinates the contraction and relaxation of the heart chambers to circulate oxygenated and deoxygenated blood. Automation of ECG classification is considered recently to accelerate the diagnoses process and enable continuous monitoring to detect abnormalities in heart functions. ECG classification problem comes with some challenges that need to be considered such as noise, feature extraction, segmentation, and classification. This review article discusses various techniques of classification in a machine, deep, and transfer learning context as well as it considers various denoising methods to enhance the performance of different classifiers. These different classifiers are trained and tested by various and different data sets which may affect their performance as well as the number of classification classes.