2000 character limit reached
LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication (2401.02675v1)
Published 5 Jan 2024 in cs.NI, cs.GT, and cs.LG
Abstract: The next generation of communication is envisioned to be intelligent communication, that can replace traditional symbolic communication, where highly condensed semantic information considering both source and channel will be extracted and transmitted with high efficiency. The recent popular large models such as GPT4 and the boosting learning techniques lay a solid foundation for the intelligent communication, and prompt the practical deployment of it in the near future. Given the characteristics of "training once and widely use" of those multimodal LLMs, we argue that a pay-as-you-go service mode will be suitable in this context, referred to as Large Model as a Service (LMaaS). However, the trading and pricing problem is quite complex with heterogeneous and dynamic customer environments, making the pricing optimization problem challenging in seeking on-hand solutions. In this paper, we aim to fill this gap and formulate the LMaaS market trading as a Stackelberg game with two steps. In the first step, we optimize the seller's pricing decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes the prices of large models iteratively by reasoning customers' future rental decisions, which is able to achieve a near-optimal pricing solution. In the second step, we optimize customers' selection decisions by designing a robust selecting and renting (RSR) algorithm, which is guaranteed to be optimal with rigorous theoretical proof. Extensive experiments confirm the effectiveness and robustness of our algorithms.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of Advances in neural information processing systems (NIPS), vol. 30, 2017.
- Z. Qin, X. Tao, J. Lu, W. Tong, and G. Y. Li, “Semantic communications: Principles and challenges,” arXiv preprint arXiv:2201.01389, 2021.
- J. Dai, S. Wang, K. Tan, Z. Si, X. Qin, K. Niu, and P. Zhang, “Nonlinear transform source-channel coding for semantic communications,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 8, pp. 2300–2316, 2022.
- C. Dong, H. Liang, X. Xu, S. Han, B. Wang, and P. Zhang, “Semantic communication system based on semantic slice models propagation,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 202–213, 2022.
- H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Transactions on Signal Processing, vol. 69, pp. 2663–2675, 2021.
- G. S. Aujla, M. Singh, N. Kumar, and A. Y. Zomaya, “Stackelberg game for energy-aware resource allocation to sustain data centers using res,” IEEE Transactions on Cloud Computing, vol. 7, no. 4, pp. 1109–1123, 2017.
- Y. Su, W. Fan, Y. Liu, and F. Wu, “Game-based pricing and task offloading in mobile edge computing enabled edge-cloud systems,” arXiv preprint arXiv:2101.05628, 2021.
- F. Li, H. Yao, J. Du, C. Jiang, and Y. Qian, “Stackelberg game-based computation offloading in social and cognitive industrial internet of things,” IEEE Transactions on Industrial Informatics, vol. 16, no. 8, pp. 5444–5455, 2019.
- N. Ding, L. Gao, and J. Huang, “Optimal pricing design for coordinated and uncoordinated iot networks,” IEEE Transactions on Mobile Computing, 2022.
- J. He, Q. Ma, M. Zhang, and J. Huang, “Optimal fresh data sampling and trading,” in Proceedings of International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt). IEEE, 2021, pp. 1–8.
- J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,” 2020.
- M. Chen, W. Saad, and C. Yin, “Virtual reality over wireless networks: Quality-of-service model and learning-based resource management,” IEEE Transactions on Communications, vol. 66, no. 11, pp. 5621–5635, 2018.
- Y. Hao, M. Chen, L. Hu, M. S. Hossain, and A. Ghoneim, “Energy efficient task caching and offloading for mobile edge computing,” IEEE Access, vol. 6, pp. 11 365–11 373, 2018.
- S. Wang, Y.-C. Wu, M. Xia, R. Wang, and H. V. Poor, “Machine intelligence at the edge with learning centric power allocation,” IEEE Transactions on Wireless Communications, vol. 19, no. 11, pp. 7293–7308, 2020.
- P. Dhabai and N. Tiwari, “Analysis of variation in power flows due to uncertain solar farm power output and its location in network,” in 2020 IEEE International Conference for Innovation in Technology (INOCON). IEEE, 2020, pp. 1–4.
- B. Zeng and L. Zhao, “Solving two-stage robust optimization problems using a column-and-constraint generation method,” Operations Research Letters, vol. 41, no. 5, pp. 457–461, 2013.
- T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al., “Language models are few-shot learners,” Advances in neural information processing systems (NeurlPS), vol. 33, pp. 1877–1901, 2020.
- E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al., “Lora: Low-rank adaptation of large language models,” in Proceedings of International Conference on Learning Representations (ICLR), 2021.
- X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang, “P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2022, pp. 61–68.
- J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017.
- S. Wang, R. Wang, Q. Hao, Y.-C. Wu, and H. V. Poor, “Learning centric power allocation for edge intelligence,” in ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020, pp. 1–6.
- N. Farsad, M. Rao, and A. Goldsmith, “Deep learning for joint source-channel coding of text,” in Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2018, pp. 2326–2330.
- Z. Weng and Z. Qin, “Semantic communication systems for speech transmission,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2434–2444, 2021.
- H. Xie and Z. Qin, “A lite distributed semantic communication system for internet of things,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 142–153, 2020.
- H. Xie, Z. Qin, and G. Y. Li, “Task-oriented multi-user semantic communications for vqa,” IEEE Wireless Communications Letters, vol. 11, no. 3, pp. 553–557, 2021.
- L. Yan, Z. Qin, R. Zhang, Y. Li, and G. Y. Li, “Resource allocation for text semantic communications,” IEEE Wireless Communications Letters, vol. 11, no. 7, pp. 1394–1398, 2022.
- L. Ismail, D. Niyato, S. Sun, D. I. Kim, M. Erol-Kantarci, and C. Miao, “Semantic information market for the metaverse: An auction based approach,” arXiv preprint arXiv:2204.04878, 2022.
- Z. Q. Liew, H. Du, W. Y. B. Lim, Z. Xiong, D. Niyato, C. Miao, and D. I. Kim, “Economics of semantic communication system: An auction approach,” IEEE Transactions on Vehicular Technology, 2023.
- Z. Q. Liew, Y. Cheng, W. Y. B. Lim, D. Niyato, C. Miao, and S. Sun, “Economics of semantic communication system in wireless powered internet of things,” in Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 8637–8641.
- N. C. Luong, Q.-V. Pham, T. Huynh-The, V.-D. Nguyen, D. W. K. Ng, and S. Chatzinotas, “Edge computing for semantic communication enabled metaverse: An incentive mechanism design,” arXiv preprint arXiv:2212.06463, 2022.
- Y. Li, C. A. Courcoubetis, L. Duan, and R. Weber, “Optimal pricing for peer-to-peer sharing with network externalities,” IEEE/ACM Transactions on Networking, vol. 29, no. 1, pp. 148–161, 2021.
- M. Zhang, L. Gao, J. Huang, and M. Honig, “Cooperative and competitive operator pricing for mobile crowdsourced internet access,” in Proceedings of IEEE International Conference on Computer Communications (INFOCOM). IEEE, 2017, pp. 1–9.
- H. Jin, H. Guo, L. Su, K. Nahrstedt, and X. Wang, “Dynamic task pricing in multi-requester mobile crowd sensing with markov correlated equilibrium,” in Proceedings of IEEE International Conference on Computer Communications (INFOCOM). IEEE, 2019, pp. 1063–1071.
- W. Mao, Z. Zheng, and F. Wu, “Pricing for revenue maximization in iot data markets: An information design perspective,” in Proceedings of IEEE International Conference on Computer Communications (INFOCOM). IEEE, 2019, pp. 1837–1845.
Collections
Sign up for free to add this paper to one or more collections.
