Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Aims & Scope Peer-to-Peer Networking and Applications (P2PNA) has received significant attention from both academia and industry in recent years. The aim of the Peer-to-Peer Networking and Applications journal is to disseminate state-of-the-art research and development results in this rapidly growing research area, to facilitate the deployment of P2P networking and applications, and to bring together the academic and industry communities, with the goal of fostering interaction to promote further research interests and activities, thus enabling new P2P applications and services. The journal not only will address research topics related to networking and communications theory, but will also consider the standardization, economic, and engineering aspects of P2P technologies, and their impacts on software engineering, computer engineering, networked communication, and security.

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Peer-to-Peer Networking and Applications

High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (i.e., transcoding) video streams to match the characteristics of viewers’ devices (e.g., in terms of spatial resolution or supported formats). Considering the computational cost of the transcoding operation and the surge in video streaming demands, Streaming Service Providers (SSPs) are becoming reliant on cloud services to guarantee Quality of Service (QoS) of streaming for their viewers. Cloud providers offer heterogeneous computational services in form of different types of Virtual Machines (VMs) with diverse prices. Effective utilization of cloud services for video transcoding requires detailed performance analysis of different video transcoding operations on the heterogeneous cloud VMs. In this research, for the first time, we provide a thorough analysis of the performance of the video stream transcoding on heterogeneous cloud VMs. Providing such analysis is crucial for efficient prediction of transcoding time on heterogeneous VMs and for the functionality of any scheduling methods tailored for video transcoding. Based upon the findings of this analysis and by considering the cost difference of heterogeneous cloud VMs, in this research, we also provide a model to quantify the degree of suitability of each cloud VM type for various transcoding tasks. The provided model can supply resource (VM) provisioning methods with accurate performance and cost trade-offs to efficiently utilize cloud services for video streaming.

Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

Internet of Things (IoT) is proliferating in our real world, and it is a promising technology that serves a very comfortable service to the users. IoT’s underlying technology is to connect to central Cloud Computing (CC), which is a huge data center collecting the generated data by IoT devices, and it is located in different areas on demand. However, cloud computing lacks data transmission because of the infrastructure and limitations of networks which enormously decrease its performance. Therefore, a new paradigm has been founded to act as a middleware between the Cloud and IoT, termed Fog Computing (FC) Technology. Considering Fog as a cloud extension that provides computing service at the edge of the network, Fog Computing placement enables this technology to deal with numerous data locally. In this study, we surveyed Fog computing with in-depth analysis and covered the latest studies to address and overcome the existing challenges in FC. We reviewed fog computing technology conceptually and defined it based on the existing studies in the literature, together with its architecture, applications, advantages, and open issues with optimization methods being performed to obtain the optimal services.

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Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues

High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (ie, transcoding) video streams to match the characteristics of viewers' devices (eg, in terms of spatial resolution or supported formats). Considering the computational cost of the transcoding operation and the surge in video streaming demands, Streaming Service Providers (SSPs) are becoming reliant on cloud services to guarantee Quality of Service (QoS) of streaming for their viewers. Cloud providers offer heterogeneous computational services in form of different types of Virtual Machines (VMs) with diverse prices. Effective utilization of cloud services for video transcoding requires detailed performance analysis of different video transcoding operations on the heterogeneous cloud VMs. In this research, for the first time, we provide a thorough analysis of the performance of the video stream transcoding on heterogeneous cloud VMs. Providing such analysis is crucial for efficient prediction of transcoding time on heterogeneous VMs and for the functionality of any scheduling methods tailored for video transcoding. Based upon the findings of this analysis and by considering the cost difference of heterogeneous cloud VMs, in this research, we also provide a model to quantify the degree of suitability of each cloud VM type for various transcoding tasks. The provided model can supply resource (VM) provisioning methods with accurate performance and cost trade-offs to efficiently utilize cloud services for video streaming.

Video transcoding is the process of converting a video to the format supported by the viewer's device. Video transcoding requires huge storage and computational resources, thus, many video stream providers choose to carry it out on the cloud. Video streaming providers generally need to prepare several formats of the same video (termed pre-transcoding) and stream the appropriate format to the viewer. However, pre-transcoding requires enormous storage space and imposes a significant cost to the stream provider. More importantly, pre-transcoding proven to be inefficient due to the long-tail access pattern to video streams in a repository. To reduce the incurred cost, in this research, we propose a method to partially pre-transcode video streams and re-transcode the rest of it in an on-demand manner. We will develop a method to strike a trade-off between pre-transcoding and on-demand transcoding of video streams to reduce the overall cost. Experimental results show the efficiency of our approach, particularly, when a high percentage of videos are accessed frequently. In such repositories, the proposed approach reduces the incurred cost by up to 70\%.

Cost Efficient Repository Management for Cloud-Based On-Demand Video Streaming

Video streaming providers generally have to store several formats of the same video and stream the appropriate format based on the characteristics of the viewer’s device. This approach, called pre-transcoding, incurs a significant cost to the stream providers that rely on cloud services. Furthermore, pretranscoding proven to be inefficient due to the long-tail access pattern to video streams. To reduce the incurred cost, we propose to pre-transcode only frequentlyaccessed videos (called hot videos) and partially pre-transcode others, depending on their hotness degree. Therefore, we need to measure video stream hotness. Accordingly, we first provide a model to measure the hotness of video streams. Then, we develop methods that operate based on the hotness measure and determine how to pre-transcode videos to minimize the cost of stream providers. The partial pre-transcoding methods operate at different granularity levels to capture different patterns in accessing videos. Particularly, one of the methods operates faster but cannot partially pre-transcode videos with the non-long-tail access pattern. Experimental results show the efficacy of our proposed methods, specifically, when a video stream repository includes a high percentage of the Frequently Accessed Video Streams and a high percentage of videos with the non-long-tail accesses pattern.

Cost-Efficient Cloud-Based Video Streaming Through Measuring Hotness

Video transcoding is the process of converting a video to the format supported by the viewer's device. Video transcoding requires a huge storage and computational resources, thus, many video stream providers choose to carry it out on the cloud. Video streaming providers generally need to prepare several formats of the same video (termed pre-transcoding) and stream the appropriate format to the viewer. However, pre-transcoding requires enormous storage space and imposes a significant cost to the stream provider. More importantly, pre-transcoding proven to be inefficient due to long-tail access pattern to video streams in a repository. To reduce the incurred cost, in this research, we propose a method to partially pre-transcode video streams and re-transcode the rest of it in an on-demand manner. We will develop a method to strike a trade-off between pre-transcoding and on-demand transcoding of video streams to reduce the overall cost. Experimental results show the efficiency of our approach, particularly, when a high percentage of videos are accessed frequently. In such repositories, the proposed approach reduces the incurred cost by up to 70%.

Mahmoud Darwich

Papers

Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

Cost Efficient Repository Management for Cloud-Based On-Demand Video Streaming

Cost-Efficient Cloud-Based Video Streaming Through Measuring Hotness

Cost Efficient Repository Management for Cloud-Based On-demand Video Streaming