A design in system architecture based on mobile cloud computing for a virtual try-on solution

Abstract

Cloud computing is an emerging technology in this digital century. It provides an excellent but low-cost source for database storage, computing power, applications, and services through an internet-delivered cloud platform. Thanks to the cost savings in investing and maintaining physical data centers, as well as the stability of Quality of Service (QoS), it has no restrictions on company size or sector for enterprises to shift their operations to a cloud platform. The fashion industry, particularly the fashion e-commerce sector, is a case study in leveraging the cloud platform via a technology called "Virtual try-on"(VTO). VTO solution allows fashion brands to increase the shopping experience, however, requires installing and maintaining a bulky system for implementation. There are different methods and approaches to design architectures using cloud computing, however, there have not been many studies addressing tasks related to machine learning that uses the high Graphics Processing Unit (GPU) encountered in VTO solutions. To design a scheduler that could optimize the system performance while lowering operational expenses in VTO solutions, this research proposes a system to (1) handle synchronous model and asynchronous model separately and clearly, (2) perform multi-layered task processing architecture by hashing task ID and implementing a queue management system. This method would satisfy three major requirements: (1) Avoid complex hardware requirements for users, (2) Ensure the system stability and the ease of horizontal and vertical extension, and (3) Protect user information privacy.