Otec

Video transcoding refers to the process of converting a digital video from one format into another format. It is a compute-intensive operation. Therefore, transcoding of a large number of simultaneous video streams requires a large amount of computing resources. Moreover, to handle different load conditions in a cost-efficient manner, the video transcoding service should be dynamically scalable. Infrastructure as a Service Clouds currently offer computing resources, such as virtual machines, under the pay-per-use business model. Thus the IaaS Clouds can be leveraged to provide a costefficient, dynamically scalable video transcoding service.

2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)

Video streaming is the dominating traffic in today's data-sharing world. Media service providers stream video content for their viewers, while worldwide users create and distribute videos using mobile or video system applications that significantly increase the traffic share. We propose a multilayer and pipeline encoding on the computing continuum (MPEC2) method that addresses the key technical challenge of high-price and computational complexity of video encoding. MPEC2 splits the video encoding into several tasks scheduled on appropriately selected Cloud and Fog computing instance types that satisfy the media service provider and user priorities in terms of time and cost. In the first phase, MPEC2 uses a multilayer resource partitioning method to explore the instance types for encoding a video segment. In the second phase, it distributes the independent segment encoding tasks in a pipeline model on the underlying instances. We evaluate MPEC2 on a federated computing continuum encompassing Amazon Web Services (AWS) EC2 Cloud and Exoscale Fog instances distributed on seven geographical locations. Experimental results show that MPEC2 achieves 24 % faster completion time and 60 % lower cost for video encoding compared to resource allocation related methods. When compared with baseline methods, MPEC2 yields 40 %-50 % lower completion time and 5 %-60 % reduced total cost.

International Conference on Automation, Computing and Renewable Systems (ICACRS 2022)-IEEE=XPLORE, 2022

Power and other computing resources can be stored and processed in cloud computing environments. Depending on the features of the clients' devices, video streams, whether they are live or on-demand, often need to be transcoded or converted (such as supported formats, bandwidth, and spatial resolution, for example). Currently, streaming service providers maintain multiple transcoded versions of the same video to serve various client devices because transcoding is a computationally expensive and time-consuming process. To eliminate jitters in accepted streams while transcoding, a task scheduling mechanism is included. To ensure that the consumer receives continuous video content delivery, this technique involves cutting a tiny number of video frames from a video segment. In this evaluation, admission control and scheduling based on QoS-aware video streams is the new task scheduling method for video recording that is suggested. With the help of this framework, streaming platform services have made efficient use of cloud resources while following to the Quality of Service (QoS) standards for video transmissions. The technologies are advancing a scheduling technique that is QoS-aware to effectively map video streams to cloud resources in order to deliver high QoS. The performance of this analysis is calculated on different aspects such as Accuracy, Qos and Recall. In this approach the QoS-aware video streaming based admission control and scheduling for video transcoding in cloud computing will give the best outcomes.

IEEE Access

For a multimedia cloud computing platform, it needs to perform video transcoding to provide bandwidth-compatible bit-streams for users. In configuring a MapReduce system, it allocates slots to workers with the assumption of homogeneous worker power and performs task scheduling by assuming equal task time complexity. However, the computing power of a practical cluster of workers and the time complexity of tasks is time-varying in any case. The task-scheduling algorithm has to well manipulate these heterogeneous cloud resources and task complexity. We first find a good partition size for a video transcoding job for efficient processing. We proposed a Complexity-Aware Scheduling (CAS) algorithm that reorders task assignment priorities according to task complexity to maintain load-balancing operations. By utilizing a Neural Network model to refine the task complexity estimation for the CAS(CASNN), the scheduling and transcoding speedup performances can further be improved. Based on the CASNN, we proposed a Dynamically Adjusting Slot number allocation (DAS) method, DASCASNN, to adjust the slots according to resource utilization status to improve the processing performance. Experimental results show the proposed DASCASNN can help reduce 30% of the transcoding time on average as compared to available schedulers and increase the resource utilization rates from 82.7% to 98%.

Proceedings of the First International Workshop on Green Multimedia Systems

The considerable surge in energy consumption within data centers can be attributed to the exponential rise in demand for complex computing workflows and storage resources. Video streaming applications are both compute and storage-intensive and account for the majority of today's internet services. In this work, we designed a video encoding application consisting of codec, bitrate, and resolution set for encoding a video segment. Then, we propose VE-Match, a matching-based method to schedule video encoding applications on both Cloud and Edge resources to optimize costs and energy consumption. Evaluation results on a real computing testbed federated between Amazon Web Services (AWS) EC2 Cloud instances and the Alpen-Adria University (AAU) Edge server reveal that VE-Match achieves lower costs by 17 %-78 % in the cost-optimized scenarios compared to the energy-optimized and tradeoff between cost and energy. Moreover, VE-Match improves the video encoding energy consumption by 38 %-45 % and gCO 2 emission by up to 80 % in the energy-optimized scenarios compared to the cost-optimized and tradeoff between cost and energy.

Video-on-demand (VoD) applications have become extensively used nowadays. YouTube is one of the most extensively used VoD application. These applications are used for various purposes like entertainment, education, media, etc., of all age groups. Earlier, these applications were supported by private data centers and application servers. Sufficient infrastructure had to be bought and maintained, to support the demand even during unexpected peak times. This approach caused huge loss of resources when the demand is normal as a large portion of the resources remained idle. To overcome this, VoD application providers moved to the cloud, to host their video content's. This approach reduced the wastage of resources and the maintenance cost of the VoD application provider. The problem is to determine the number of resources to handle the demand while maintaining QoS for every instance. We have designed two algorithms in this paper, namely the multiple cloud resource allocation (MCRA) algorithm and the hybrid MCRA algorithm. Most of the cloud service providers (CSPs) basically provide two types of resource allocation schemes: (i) the reservation scheme and (ii) the on-demand scheme. The reservation scheme provides time-based tariff prices, where the discount is provided for the resources depending on their quantity and reservation time. This scheme is used in the MCRA algorithm to reduce the cost of the VoD application provider. In Hybrid MCRA algorithm both the reservation scheme and on-demand scheme are implemented, to overcome the drawbacks of the MCRA algorithm which are under-subscription and over-subscription. We have analyzed both the algorithms in terms of cost and allocation of resources. These algorithms can help allocate resources in of cloud for VoD applications in a cost-effective way and at the same time not compromise on the QoS of the video content.

International Journal of Innovative Research in Computer and Communication Engineering, 2015

In this paper, we address the problem of selecting and composing video transcoding services in a distributed cloud environment. One of the challenging issues for video transcoding service composition is how to find the best transcoding path to route the data flow through while satisfying the viewer requirements and specifications. In a cloud environment, video transcoding service providers provide different video transcoding services that have similar functionality (i.e., format conversion), but with different Quality of Services (QoS) specifications. Since the combination of the QoS specifications, such as frame size, frame rate, video bit rate, and transcoding delay might affect the end user's experience in non-intuitive and subjective way and also might affect the delivering of a high quality video content over any type of network, we propose a QoS-aware model to select and compose the best video transcoding services to satisfy hard constraints on the input and output video formats and comes as close as possible to satisfying soft constraints on the QoS. This model uses an aggregate function to evaluate the QoS for each transcoding service and for each viewer request to explore the best composition path. In this paper, we adapt the Simulated Annealing (SA) algorithm and the Genetic Algorithm (GA) as candidate solutions to help in the composition process. The SA/GA algorithms provide multi-constraints QoS assurance for video transcoding service composition. They also support directed acyclic graph composition topology. We have implemented a prototype of the proposed algorithms and conducted experiments using small-, medium-, and large-scale graphs of video transcoders and sample viewer requests to measure the performance and the quality of the results. The experimental results show that the SA outperforms the GA in terms of performance and success ratio for small-scale graph, while GA outperforms the SA algorithm in terms of performance for medium-and large-scale graphs. The success ratio for the SA and GA algorithms are close to each other for medium-and large-scale graphs. At the end, we provide several directions and suggestions for future work.

2017 International Conference on Computer Science and Engineering (UBMK), 2017

With the technology improvements, managing a large amount of multimedia objects such as audio, video, picture or a combination of these has become possible. Multimedia data needs more real time storage and high data transfer than traditional textual and numeric data. In addition to these requirements, significant amount of computation is demanded for multimedia applications to serve many users at the same time. Due to these reasons, cloud computing is the emerging technology necessary for the multimedia information systems as well as multimedia objects. Cloud computing provides organizations to emphasize their own business values while freeing them from setting up hardware and software infrastructures to deploy the applications. Because there are several cloud providers with different quality of service requirements, different service level agreements (SLA) and uncertainties in demand, price and availability, optimization of these issues bring out new challenges. The aim of this stu...

2017

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 alloca...

This paper presents a novel approach for stream-based admission control and job scheduling for video transcoding called SBACS (Stream-Based Admission Control and Scheduling). SBACS uses queue waiting time of transcoding servers to make admission control decisions for incoming video streams. It implements stream-based admission control with per stream admission. To ensure efficient utilization of the transcoding servers, video streams are segmented at the Group of Pictures level. In addition to the traditional rejection policy, SBACS also provides a stream deferment policy, which exploits cloud elasticity to allow temporary deferment of the incoming video streams. In other words, the admission controller can decide to admit, defer, or reject an incoming stream and hence reduce rejection rate. In order to prevent transcoding jitters in the admitted streams, we introduce a job scheduling mechanism, which drops a small proportion of video frames from a video segment to ensure continued delivery of video contents to the user. The approach is demonstrated in a discrete-event simulation with a series of experiments involving different load patterns and stream arrival rates.