A software-based approach to visual cloud processing offers flexibility and the opportunity for new revenue channels without having to rework the physical architecture or update the hardware. Running visual compute workloads on pure software architectures gives companies the flexibility of making improvements to media processing and delivery, as well as customizations to the visual quality of processed data. To maximize the business value of infrastructure investments, companies need servers flexible enough to accommodate a diverse range of both compute and visual workloads.

By using available open source software components with highly permissive licensing terms, developers are able to achieve rapid innovation and circumvent having to use a proprietary hardware platform subject to vendor lock-in and a greatly reduced range of supported applications. With a modular plug-in approach, the Open Visual Cloud will support hardware accelerated functionality and seamless migration, if needed, using the same software architecture. 

The Open Visual Cloud, an open source project, offers a set of pre-defined reference pipelines for various target visual cloud use cases. These reference pipelines are based on optimized open source ingredients across four core building blocks (encode, decode, inference, and render), which are required for deployment of visual cloud services. The goal of the Open Visual Cloud is to unleash innovation, simplify development, and accelerate time to market for visual cloud services by providing open source, interoperable, high-performance building blocks and containerized reference pipelines.

Building Blocks

There are 4 groups of open source building blocks for most visual cloud workloads: encode, decode, inference, and render. Visual cloud services are defined by the order in which these open source building blocks are placed in a workflow pipeline. For example, a simple “media processing and delivery service” can be thought of as consisting of decode + encode. Insertion of an inference building block such as OpenVINO™ (decode + inference + encode) would result in a “media analytics” service relevant for such things as digital security and surveillance or user generated content ad-insertion use cases where intelligent content analysis is required. 

 

The Open Visual Cloud Building Blocks support industry standard open source frameworks such as FFMPEG, GStreamer, OpenVINO™ Toolkit, TensorFlow*, Caffe*, Apache MXNet*, and more. 

 

With the ease of interoperability between software and hardware building blocks, it is easy for developers to create flexible architecture designs for their applications or services.

 

By using pre-defined server configuration files and open source building blocks, developers can easily create pipelines for Open Visual Cloud Services.

 

The Open Visual Cloud enables five major services, each providing a set of related visual cloud use cases. The Open Visual Cloud services are: Media Processing and Delivery, Media Analytics, Immersive Media, Cloud Graphics, and Cloud Gaming. 

Contending with the onslaught of new visual workloads will require more nimble, scalable, virtualized infrastructures. The visual cloud requires companies to have the capability of shifting workloads to the network edge when appropriate; and a collection of tools, software, and hardware components to support individual use cases fluidly.

 

• Increasing flexibility and optimizing processing: Virtualization and software-defined infrastructure (SDI) make it easier to balance workloads on available resources. The open source Scalable Video Technology is an example of using software to optimize processing on x86 machines.
• Scaling compute, storage, and network resources: Dynamic elasticity is a major advantage when contending with visual cloud workloads. Adopting a modern cloud infrastructure powered by a new generation of scalable processors increases resource availability substantially.
• Enhancing development processes: High performance, open sourced media, AI, and graphics software components, along with sample reference pipelines that demonstrate how to construct new Visual Cloud services quickly and easily support rapid application development and help reduce time to market for new revenue. 
• Deploying purpose-built solutions for select use cases and edge computing: Specialized hardware—including discrete graphics processing units (GPUs), integrated GPUs, field programmable gate arrays (FPGAs), and video processing units (VPUs)—can boost performance for select applications in which a single, targeted workload must be handled on a large-scale basis.   
• Implementing modern cloud architectures: For performing operations on large, complex data files and delivering elastic processing power to efficiently handle ebbs and flows in intensive computing operations, modern cloud architectures hold the key to effective workload distribution.