Towards Deep Application-Network Integration: Architectures, Progress and Opportunities
<|reference_start|>Towards Deep Application-Network Integration: Architectures, Progress and Opportunities: With the rise of a new generation of applications (e.g., virtual and augmented reality, artificial intelligence, etc) demanding stringent performance requirements, the need for networking solutions and architectures that can enable a higher Quality of Experience (QoE) is becoming increasingly important.   While jointly optimizing application and network may increase the applications' QoE and simultaneously improve the utilization of network resources, such a paradigm has had limited success in real production networks. However, with the combination of revolutionary trends in (1) compute processing demands, (2) networking capabilities, and (3) sustainable business models, it is high time the community explores the full potential of deeper integration between application and network.   In this paper, recent trends observed over the past few years are systematically reviewed. These include the paradigm shift in modern communication services towards computing-driven applications, such as on-site AI training, advances in programmable network technologies like Software Defined Networking (SDN), and new business models incentivizing collaboration and cooperation between parties. Following this, successful scenarios that benefit from various forms of deeper network-application integration are reported, highlighting their considerable potential. A unified framework is then introduced, providing an overview of possible architecture paradigms for network-application integration and bringing awareness to existing abstractions, mechanisms, tools, and their potential combinations. The paper concludes with a discussion of several remaining challenges in building practical network-application integrated systems.<|reference_end|>
@article{serracanta2024towards,
    title={Towards Deep Application-Network Integration: Architectures, Progress
  and Opportunities},
    author={Berta Serracanta, Kai Gao, Jordi Ros-Giralt, Alberto Rodriguez-Natal,
  Luis M. Contreras, Richard Yang, and Albert Cabellos},
    journal={arXiv preprint arXiv:2406.12556},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12556},
    primaryClass={cs.NI}
}