On behalf of the technical program committee, we are delighted to invite you to participate in the 2025 IEEE 16th International Conference on Cloud Computing Technology and Science (IEEE CloudCom), to be held in Shenzhen, China on November 14-16, 2025.
Building automation is a representative AIoT-driven cyber-physical scenario, where intelligent systems interact with physical devices to manage lighting, climate, and appliances in real time. Traditional machine learning struggles with ambiguous, multilingual, and colloquial user inputs, limiting effectiveness in dynamic building environments. Recent advances in large language models (LLMs) enable more natural command interpretation, but high resource demands challenge their sustainable deployment on edge nodes. This paper proposes a serverless architecture based on event-driven microservices and container orchestration that dynamically manages the deployment, execution, and scaling of compact, fine-tuned LLMs across distributed edge nodes for building automation. We fine-tune compact LLMs with ambiguous and colloquial command examples to enhance robustness and enable context-aware deployment at the edge. Platform elasticity, enabled by Knative, allows rapid model adaptation without persistent resource allocation. We evaluate the system on a multilingual building automation dataset (Chinese, English, French) with ambiguous and colloquial commands, using an automated framework to assess interpretation and execution. Results show that the fine-tuned model outperforms the baseline Qwen-2.5-14B on five of six metrics and performs comparably on output format compliance. It also generalizes well across languages, although fuzzy instructions remain challenging.
Mobile Personal Livestreaming (MPL), one of the most popular mobile applications recently, has been experiencing explosive growth thanks to ubiquitous smartphones and high-speed wireless connections nowadays, e.g., 4G-LTE/5G and WiFi 802.11ac/ad. Unlike traditional livestreaming services that are either limited to specific content providers or fixed content, MPL enables end-to-end live-stream communication of anything, at anytime and anywhere, with anybody in mobile networks. This paper presents our design and experience of Inke, a large-scale MPL system with 15 million daily active users since 2015, which provides a range of functionalities, including mobile livestreaming, real time communication, augmented reality (AR), and real time face recognition. We characterize its system design from three key aspects: push-streaming protocol, “Join Channel“ livestreaming, and AR features. For the push-streaming protocol, we introduce the bandwidth-aware design that incorporates buffer length control and frame-drop strategy to ensure upload consistency and reduce delay. We also design a mixed-stream upload model that is able to balance the delay and bandwidth consumption during “Join Channel” livestreaming. In addition, we show how the virtual gift value distribution can be used to im-prove the design of animation playback queues. We demonstrate the effectiveness of our system design that works across time, geo-location, and heterogeneous wireless networks for large-scale (more than 130 million registered users) applications. It achieves 126.5736ms average upload delay, 1.093 average rebuffering times during each broadcast at peak time.
The success of distributed machine learning and federated learning in cloud environments depends critically on efficient communication across heterogeneous and bandwidth-constrained networks. While the exchange of raw data is often avoided due to privacy and regulatory concerns, many distributed workflows still require large-scale data movement or model updates that demand predictable network performance. This paper investigates the trade-off between earliest completion time (ECT) and communication cost in data transfers that leverage bandwidth reservation on fixed paths with variable bandwidth in dedicated high-performance networks. We fo-cus on two representative scheduling objectives relevant to distributed learning and data-intensive cloud workflows: (i) minimizing communication cost while meeting a transfer dead-line, and (ii) achieving the ECT while respecting a maximum cost budget. We prove that both scheduling problems are NP-complete and propose heuristic algorithms to address them effectively. Extensive simulations demonstrate that our methods can substantially reduce both latency and cost, providing a prac-tical foundation for communication-efficient and resource-aware distributed machine learning in cloud computing environments.
Satellite communication plays a crucial role in future networks, but traditional systems face significant challenges in interference management and energy efficiency. To address these issues, this paper proposes a green satellite communication framework that combines Lyapunov optimization with a one-dimensional golden-section search method. The framework builds a two-timescale frame-slot model that jointly captures fast-varying channel fading and slow-varying renewable energy dynamics. By introducing a drift-plus-penalty optimization method, the system ensures queue stability and minimizes long-term grid energy expenditure, while employing the golden-section search to optimize beamforming parameters with reduced computational complexity. Simulation results show that the proposed framework effectively balances energy efficiency and communication performance, demonstrating good scalability for large-scale satellite networks.
