As a cloud-hosted virtual desktop service, cloud desktop supports various fields such as telecommuting, collaborative development, while enabling real-time user interaction through video stream. The stability of this process is determined by bandwidth, which significantly influences the user experience. Therefore, precise bandwidth prediction of video streams is essential in cloud desktops. This work proposes DeskPred for video stream transmission in cloud desktops, focusing on dynamic bandwidth prediction. In the startup stage, the limited data amount poses a challenge for achieving precise bandwidth predictions. We propose an Affinity-based Federated Learning algorithm, which leverages the historical records of high-affinity users for assisted training, all while protecting user privacy. During the long-term adjustment stage, we propose a Fluctuation-based Adaptive Incremental Prediction algorithm for independent training to address the issue of pattern forgetting. The algorithm considers both periodic features and instantaneous features, incorporating new patterns while revisiting previous knowledge through the memory module and Adversarial Elastic Weight Consolidation. We have verified DeskPred through an actual cloud desktop project supported by Lenovo Research. Through experiments conducted on a total of over 18 million data items (approximately 10 GB), DeskPred achieves the highest total score of 71.11%, making it highly suitable for cloud desktop environments.

With the widespread application of big data and intelligent information systems, the tenant has become the main form of most scenarios. As a data mining technique, the portrait has been widely used to provide targeted services. Therefore, we transfer the traditional user-driven portrait into tenant driven for churn prediction. To achieve it, this paper first proposes a three-layer architecture and defines the fine-grained features for creating portraits from the perspective of tenants. In a large-scale telecommunication industry dataset of 100,000 tenants, we construct the tenant portrait through the proposed framework, and analyze the influences of the defined features on churn possibility. Then, considering the information missing caused by privacy concerns, we come up with theCrossMatch, a portrait completion model based on semi-supervised and graph convolution, which combines the relation characteristics among tenants for recovering missing information. On this basis, we design the tenant churn prediction method based on a directed attention network. Moreover, we recover missing information on three public node datasets withCrossMatch, achieving around 1-2$\%$improvement. We then apply the directed attention network for churn prediction and achieve an Accuracy of 75.06$\%$, Precision of 77.78$\%$, and F1-score of 71.43$\%$, which outperforms all the baselines.

Accurately perceiving service states of the Internet of Things (IoT) is crucial for maintaining system stability and long-term sustainability. Recently, Large Language Models (LLMs) have emerged as a novel technological advancement, offering unprecedented possibilities in various domains due to their advanced comprehension capabilities. However, the limited data availability problem poses a challenge to the integration of domain knowledge into LLMs and the enhancement of their ability to perceive service states. In this article, a novel adversarial augmentation-based domain LLM framework for IoT services is proposed to solve this problem. We first construct an LLM fine-tuning dataset through an elaborate hierarchical prompt template, which integrates task-specific instructions, heterogeneous service data, and statistical features to the service contextual description. Then, inspired by using specialized compact small-models to enhance the capabilities of LLMs, we design two dedicated domain-specific small-models, including a service topology prediction model and a running status prediction model, to capture evolution patterns of services from different perspectives. On this basis, we design an LLM fine-tuning framework based on the domain adversarial distillation, which comprises an LLM parameter tuning module and a small-model adversarial distillation module. The former leverages the Low-Rank Adaptation (LoRA) algorithm to fine-tune the LLM. The latter distills the domain knowledge of small-models into the LLM through aligning the latent vector distributions of the LLM with those of small-models. Following the adversarial training, the LLM will be equipped with domain capabilities of small-models. Experimental results on two datasets demonstrate the effectiveness of our LLM framework.

The popularity of cloud services has provided a new medium for video streams transmission. Cloud desktops, as a representative multimedia application, facilitate interaction between users and cloud via video streams, garnering widespread adoption in various fields. The network condition directly affects the transmission. Therefore, accurate throughput prediction helps guide the allocation of network resources, avoiding a decline in user experience due to insufficient resources and waste caused by excessive resources. Recent works focus more on the temporal characteristics of throughput. However, we believe that throughput of video streaming is significantly influenced by usage scenario. In this paper, we propose a transfer-based autoencoder framework DeskTransfer for throughput prediction in frequent switching cloud desktop scenarios. Specifically, we construct the Scenario Autoencoder and Throughput Autoencoder to respectively learn the scenario and throughput features from historical usage records. By adopting an adversarial mechanism, we design transfer algorithm using latent vectors, enabling the model suitable for multiple scenarios. We collect real-world data from a project cooperated with Lenovo Research for experiment and compare our solution with leading methods on public datasets to validate its effectiveness.

Due to the unrestricted location of cloud resources, an increasing number of users are opting to apply for them. However, determining the appropriate resource quota has always been a challenge for applicants. Excessive quotas can result in resource wastage, while insufficient quotas can pose stability risks. Therefore, it's necessary to propose an adaptive quota scheme for cloud resource. Most existing researches have designed fixed quota schemes for all users, without considering the differences among users. To solve this, we propose an adaptive cloud quota scheme through dynamic portraits and task-resource optimal matching. Specifically, we first aggregate information from text, statistical, and fractal three dimensions to establish dynamic portraits. On this basis, the bidirectional mixture of experts (Bi-MoE) model is designed to match the most suitable resource combinations for tasks. Moreover, we define the time-varying rewards and utilize portrait-based reinforcement learning (PRL) to obtain the optimal quotas, which ensures stability and reduces waste. Extensive simulation results demonstrate that the proposed scheme achieves a memory utilization rate of around 70%. Additionally, it shows improvements in task execution stability, throughput, and the percentage of effective execution time.