Research

Joint Assortment and Cache Planning for Practical User Choice Model in Wireless Content Caching Networks

Y. Fu, X. Xu, H. Liu, Q. Yu, H. -N. Dai and T. Q. S. Quek, "Joint Assortment and Cache Planning for Practical User Choice Model in Wireless Content Caching Networks," in IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4709-4722, May 2024, doi: 10.1109/TMC.2023.3297987.

In wireless content caching networks (WCCNs), a user's content consumption crucially depends on the assortment offered. Here, the assortment refers to the recommendation list. An appropriate user choice model is essential for greater revenue. Therefore, in this paper, we propose a practical multinomial logit choice model to capture users’ content requests. Based on this model, we first derive the individual demand distribution per user and then investigate the effect of the interplay between the assortment decision and cache planning on WCCNs’ achievable revenue. A revenue maximization problem is formulated while incorporating the influences of the screen size constraints of users and the cache capacity budget of the base station (BS). The formulated optimization problem is a non-convex integer programming problem. For ease of analysis, we decompose it into two folds, i.e., the personalized assortment decision problem and the cache planning problem. By using structure-oriented geometric properties, we design an iterative algorithm with examinable quadratic time complexity to solve the non-convex assortment problem in an optimal manner. The cache planning problem is proved to be a 0-1 Knapsack problem and thus can be addressed by a dynamic programming approach with pseudo-polynomial time complexity. Afterwards, an alternating optimization method is used to optimize the two types of variables until convergence. It is shown by simulations that the proposed scheme outperforms various existing benchmark schemes.

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Cloud within a Cloud

(In progress)

The rising demand for AI specialty chips has prompted hardware suppliers to expand into cloud service operations to tap into a broader market. This shift has given rise to an innovative business model, termed "cloud-within-a-cloud," where hardware suppliers lease servers from cloud providers’ data centers and rent them to customers with AI-specific needs. In this paper, we develop a game-theoretic model to study how the emergence of cloud-within-a-cloud will shape strategic interactions between hardware suppliers and cloud providers, uncovering both its advantages and pitfalls. We find that the cloud provider could offer rentback discount to facilitate the Machine Learning-as-a-Service (MLaaS) solution, which leads to win-win-win outcomes for the hardware supplier, the cloud provider, and customers, when production costs of AI chips are sufficiently high. Contrary to the common belief, we show that the cloud provider offers rentback discount not for wholesale discount but for balanced downstream competition. However, when production costs are low, cloud-within-a-cloud could allow the cloud provider to overcharge the rentback price and lead to lose-lose-lose outcomes. Our results  provide actionable insights for hardware suppliers, cloud providers, and policymakers navigating the evolving cloud computing value chain.