Guanyu Gao

Towards Cost-Efficient Video Transcoding in Media Cloud: Insights Learned From User Viewing Patterns

Video transcoding in an adaptive bitrate streaming (ABR) system is demanded to support video streaming over heterogenous devices and varying networks. However, it could incur a tremendous cost. Meanwhile, most viewers terminate viewing sessions within 20% of their durations; only a small fraction of each video is consumed. Built upon this user viewing pattern, we propose a Partial Transcoding Scheme for content management in media clouds. Particularly, each content is encoded into different bitrates and split into segments. Some of the segments are stored in cache, resulting in storage cost; others are transcoded online in the case of cache miss, resulting in computing cost. We aim to minimize the long-term overall cost by determining whether a segment should be cached or transcoded online. We formulate it as a constrained stochastic optimization problem. Leveraging Lyapunov optimization framework and Lagrangian relaxation, we design an online algorithm which can achieve the optimal solution within provable upper bounds. Experiments demonstrate that our proposed method can reduce 30% of operational cost, compared with the scheme of caching all the segments.

Cost optimal video transcoding in media cloud: Insights from user viewing pattern

Video transcoding has been touted as an enabling technology to support growing media consumption over heterogenous devices. However, on-line transcoding could incur tremendous, if not prohibitive, cost in deploying or renting resources. In this research, we leverage an insight into the viewing pattern of video consumers to reduce the operating cost of video transcoding services. Specifically, it has been reported that viewers tend to terminate their session before the whole video is watched. As such, it is not cost-efficient for service providers to store or transcode all segments of the videos. Built upon this insight, we propose a partial transcoding scheme for content management in a media cloud to reduce the operating cost. Particularly, each content is split into multiple segments and stored in different files of varying playback rates. Some of the segments are stored in cache, resulting in storage cost; while some are transcoded in real-time in case of cache miss, resulting in computing cost. We aim to minimize the long-term operational cost by determining the number of segments for each playback rate to be cached or transcoded in real-time. We formulate this partial transcoding scheme as a constrained integer optimization problem. Leveraging Lagrangian relaxation and a subgradient method, we obtain the approximate solution to the integer program. Numerical results indicate that our proposed partial transcoding scheme can save more than 30% of operational cost, compared with a brute-force scheme of caching all the segments.

Dynamic Resource Provisioning with QoS Guarantee for Video Transcoding in Online Video Sharing Service

Video transcoding is widely adopted in online video sharing services to encode video content into multiple representations. This solution, however, could consume huge amount of computing resource and incur excessive processing delays. Moreover, content has heterogeneous QoS requirements for transcoding. Some content must be transcoded in real time, while some are deferrable for transcoding. It needs to determine the strategy for intelligently provisioning the right amount of resource under dynamic workload to meet the heterogeneous QoS requirements. To this end, this paper develops a robust dynamic resource provisioning scheme for transcoding with heterogeneous QoS criteria. We adopt the Preemptive Resume Priority discipline for scheduling, so that the transcoding-deferrable content can utilize idle resources for transcoding to maximize resource utilization while remain transparent to delay-sensitive content. We leverage Model Predictive Control to design the online algorithm for dynamic resource provisioning using predictions to accommodate time-varying workload. To seek robustness of system performance against prediction noises, we improve our online algorithm through Robust Design. The experiment results in a real environment demonstrate that our proposed framework can achieve the QoS requirements while reducing 50% of resource consumption on average.

Resource Provisioning and Profit Maximization for Transcoding in Clouds: A Two-Timescale Approach

Transcoding is widely adopted for content adaptation; however, it may incur excessive resource consumption and processing delays. Taking advantage of cloud infrastructure, cloud-based transcoding can elastically allocate resources under time-varying workloads and perform multiple transcodings in parallel to reduce delays. To provide transcoding as a cloud service, cloud transcoding systems require some intelligent mechanisms to provision resources and schedule tasks to satisfy user requirements while maximizing financial profit. To this end, we propose a two-timescale stochastic optimization framework for maximizing service profit while achieving performance requirements by jointly provisioning resources and scheduling tasks under a hierarchical control architecture. Our method analytically integrates service revenue, processing delay, and resource consumption in one optimization framework. We derive the offline exact solution and design some approximate online solutions for task scheduling and resource provisioning. We implement an open source cloud transcoding system, called Morph, and evaluate the performance of our method in a real environment. Empirical studies verify that our method can reduce resource consumption and achieve a higher profit compared with baseline schemes.

Cost-efficient and QoS-aware content management in media cloud: Implementation and evaluation

Adaptive bitrate streaming has been proposed to encode video contents into multiple versions for device heterogeneity and changing network conditions. This solution, however, could consume enormous computing and storage resource. In fact, only a small fraction of videos are frequently requested. Thus, caching multiple versions for unpopular contents is not cost efficient. In this paper, we design a cost-efficient and QoS-aware content management system for video streaming. The system consists of a set of streaming servers and a computing cluster, where streaming servers can cache video contents or transcode them in real time, and the computing cluster can perform transcoding tasks on behalf of streaming servers. Based on this architecture, to provide cost-efficient and QoS-aware video service, first, we design a cost-efficient content cache management module to minimize the operational cost, by dynamically determining whether a segment should be cached or transcoded on fly according to their popularity. Second, to reduce transcoding latency, we design a QoS-aware transcoding task delegation module to determine whether a transcoding task in streaming server should be delegated to the computing cluster according to the streaming servers workload. We implement the system and evaluate the performance in a real environment. The results demonstrate that our method can greatly reduce the operational cost and guarantee the QoS in providing video services.

Resource provisioning and profit maximization for transcoding in Information Centric Networking

Adaptive bitrate streaming (ABR) has been widely adopted to support video streaming services over heterogeneous devices and varying network conditions. With ABR, each video content is transcoded into multiple representations in different bitrates and resolutions. However, video transcoding is computing intensive, which requires the transcoding service providers to deploy a large number of servers for transcoding the video contents published by the content producers. As such, a natural question for the transcoding service provider is how to provision the computing resource for transcoding the video contents while maximizing service profit. To address this problem, we design a cloud video transcoding system by taking the advantage of cloud computing technology to elastically allocate computing resource. We propose a method for jointly considering the task scheduling and resource provisioning problem in two timescales, and formulate the service profit maximization as a two-timescale stochastic optimization problem. We derive some approximate policies for the task scheduling and resource provisioning. Based on our proposed methods, we implement our open source cloud video transcoding system Morph and evaluate its performance in a real environment. The experiment results demonstrate that our proposed method can reduce the resource consumption and achieve a higher profit compared with the baseline schemes.

Morph: A Fast and Scalable Cloud Transcoding System

Morph is an open source cloud transcoding system. It can leverage the scalability of the cloud infrastructure to encode and transcode video contents in fast speed, and dynamically provision the resources in cloud to accommodate the workload. The system is composed of a master node that performs the video file segmentation, concentration, and task scheduling operations; and multiple worker nodes that perform the transcoding for video blocks. Morph can transcode the video blocks of a video file on multiple workers in parallel to achieve fast speed, and automatically manage the data transfers and communications between the master node and the worker nodes. The worker nodes can join into or leave the transcoding cluster at any time for dynamic resource provisioning. The system is very modular, and all of the algorithms can be easily modified or replaced. We release the source code of Morph under MIT License, hoping that it can be shared among various research communities.

QDLCoding: QoS-differentiated low-cost video encoding scheme for online video service

Adaptive bitrate (ABR) streaming is the de facto solution in online video services to cope with heterogeneous devices and varying network connections. However, this solution is computation intensive, demanding a large number of servers for encoding videos. Moreover, due to the time-varying nature of video generation, intelligent strategies are required in order to determine the right amount of resources for encoding. The situation is further complicated by the fact that, the two types of co-existing video content, live content and Video-on-Demand (VoD) content, have different QoS requirements for encoding. These observations posit daunting challenges for meeting the heterogeneous QoS requirements with a minimum computing capacity. This paper proposes the QoS-differentiated low-cost video encoding (QDLCoding) scheme to address these challenges. We develop a framework for scheduling the encoding workloads of the two types of videos with statistical QoS guarantees. Each type of videos is specified with a QoS criterion and a QoS loss bound. The objective is to provision the minimum amount of resources while keeping the QoS loss probabilities within the prescribed bounds. We design an online algorithm that can determine the minimum required capacity by learning content arrival distributions. The experiment results demonstrate that our method can greatly reduce the required capacity for encoding online videos while controlling the likelihood of QoS loss precisely.