Abstract
Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the distributed parallel processing system, the orchestration of technology has become crucial for proper management of computing resources, in terms of allocating computing resources, setting up a programming environment, and deploying user applications. In this paper, we present a new distributed parallel processing platform for real-time large-scale image processing based on deep learning model inference, called DiPLIP. It provides a scheme for large-scale real-time image inference using buffer layer and a scalable parallel processing environment according to the size of the stream image. It allows users to easily process trained deep learning models for processing real-time images in a distributed parallel processing environment at high speeds, through the distribution of the virtual machine container.
Highlights
Today, video equipment such as CCTV, mobile phones, and drones are highly advanced, and their usage has increased tremendously
We propose a new system called DiPLIP (Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference) to process real-time streaming data by deploying a distributed processing environment using a virtual machine and a distributed deep learning model and virtual environment to run it distributed nodes
We presented a new distributed parallel processing platform for large-scale streaming
Summary
Video equipment such as CCTV, mobile phones, and drones are highly advanced, and their usage has increased tremendously. For real-time processing, a pipeline method was applied to map and reduce models on distributed nodes to process incoming data continuously [15]. We propose a new system called DiPLIP (Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference) to process real-time streaming data by deploying a distributed processing environment using a virtual machine and a distributed deep learning model and virtual environment to run it distributed nodes. DiPLIP provides orchestration techniques such as resource allocation, resource extension, virtual programming environment deployment, trained deep learning model application deployment, and the provision of an automated real-time processing environment This is an extended and modified system to infer deep learning models based on our previous work [19].
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