Emerging cloud-native applications challenge cloud computing to provide Ultra-Reliable Low Latency Communication (URLLC). To address this challenge, the integration of cloud computing with Time-Sensitive Networks (TSN) has been explored in recent studies. Despite these efforts, existing research lacks a comprehensive analysis of how dynamic cloud computing environments impact the performance of this integration. In this paper, we address research questions related to the performance of TSN in cloud computing, considering different scenarios. Our results demonstrate that TSN can enhance the performance of time-sensitive application (360 -Virtual Reality videos) under certain conditions, but also highlight challenges in maintaining performance as cloud environments change. Furthermore, the study emphasizes the critical role of the convergence of computing and networking resources in meeting the stringent performance requirements of dynamic applications. This work offers valuable insights into the potential and limitations of TSN in cloud environments, providing a foundation for future research in this area.

Cloud Gaming (CG) has become one of the most important cloud-based services in recent years by providing games to different end-network devices, such as personal computers (wired network) and smartphones/tablets (mobile network). CG services stand challenging for network operators since this service demands rigorous network Quality of Services (QoS). Nevertheless, ensuring proper Quality of Experience (QoE) keeps the end-users engaged in the CG services. However, several factors influence users’ experience, such as context (i.e., game type/players) and the end-network type (wired/mobile). In this case, Machine Learning (ML) models have achieved the state-of-the-art on the end-users’ QoE estimation. Despite that, traditional ML models demand a larger amount of data and assume that the training and test have the same distribution, which can make the ML models hard to generalize to other scenarios from what was trained. This work employs Transfer Learning (TL) techniques to create QoE estimation over different cloud gaming services (wired/mobile) and contexts (game type/players). We improved our previous work by performing a subjective QoE assessment with real users playing new games on a mobile cloud gaming testbed. Results show that transfer learning can decrease the average MSE error by at least 34.7% compared to the source model (wired) performance on the mobile cloud gaming and to 81.5% compared with the model trained from scratch.

Cloud Gaming renders game data in the cloud and forwards it to players over the network. While this reduces hardware costs for players, it introduces challenges in network management and delivering a good gaming experience. In this context, network providers are encouraged to implement QoE-aware management systems to guarantee a desired Quality of Experience (QoE), in which Machine Learning (ML) models achieve the state-of-the-art on QoE estimation/monitoring. However, it is hard to create ML models that generalize to different contexts, especially since QoE perception is subjective and varies among games and players. This paper employs transfer learning and fine-tuning to adjust a source model to different target domains. First, we performed a subjective QoE assessment with real users playing on a realistic testbed. Based on this, we derived four datasets, one being the source dataset (to create the source model) and three distinct target datasets. Experiments show that transfer learning can decrease the average MSE error by at least 41.6% compared to the source model performance on the target datasets while decreasing the demand for labeled data by at least 81.1%. Furthermore, the improvement is greater when compared to models trained from scratch for each target dataset.

Cloud gaming is a new paradigm that allows more cost-effective gaming for both users and game developers. The market is expected to grow 50-60% annually, reaching 22 billion USD by 2030. Gaming providers and ISPs require models of user satisfaction in order to improve their management of the cloud and network infrastructure. This paper analyses and proposes models that estimate the QoE of cloud gaming. Such models take as features network and game metrics. We assume an information sharing agreement among the cloud gaming platform and the ISP, allowing for a richer dataset. Data collection is performed with real users playing on a realistic testbed using similar protocols of the NVIDIA Geforce Now cloud gaming platform. We use stacking learning in order to improve the accuracy of the models, making a search for the best models and stacking them. We tested various improvements to the models, such as removing users with very low number of matches. Experiments show that models with more experienced players obtained a better precision, achieving 36.08%. When considering a range of plus or minus one within the estimated precision, the hit ratio was 86.56%. We also analyzed the model’ s sensitivity to inputs using feature importance analysis.

Existing Cloud deployments usually perform automated scheduling and rescheduling based on Quality of Service (QoS) objectives. Services are migrating towards Quality of Experience (QoE), which maps the user experience more effectively than QoS. This work proposes extensions to the Kubernetes scheduler in order to employ QoE objectives into the algorithm. For that, we created deep learning models (using LSTM) to estimate user’s QoE that the cloud can offer. The evaluation was performed on a testbed, and considered two QoE-aware applications (live classroom and video on demand). Experimental results in a testbed show that our scheduler improves the average QoE by at least 61.5% compared to other schedulers, while our proposed resource rescheduling improved the QoE by up to 119%, keeping the average QoE closer to the maximum.

Record Deduplication (RD) aims to identify instances that represent the same real-world entity in data repositories. In the government environment, the RD process facilitates the identification of irregularities and reduces the consumption of computing resources in data integration tasks. In this context, we propose a scalable, effective and efficient platform, called DedupeGov, for integrating large data repositories (i.e., with large volumes of data, in the order of millions of records) to unify duplicate entities from multiple and different sources. Our experimental results indicate a 21.8% of reduction in the number of records of the original repository with 99% of precision and 95% of recall when identifying duplicate records. In addition, our platform was capable of building more complete records, eliminating at least 32% of records with null attributes. Furthermore, our solution is very efficient and scalable for large volumes of data, deduplicating a repository of almost 400 million records in around one hour, besides being easy to generalize to different types of entity.

Quality of Experience (QoE) should be the driver for network orchestration in 4G networks. At the same time, the network must be able to cope with high bandwidth requirements from applications such as video streaming, while dealing with a large number of users. This paper proposes a network orchestrator that adjusts network parameters to improve QoE of video streaming. The orchestrator uses Device-to-Device (D2D) communication to improve user's QoE, also reducing the demand on 4G network. The use of D2D is triggered by a machine learning engine. Experiments made in a physical testbed show an improvement on the mean horizontal video resolution from 768 to 1280 pixels, as well as a decrease of around 90% at the impact on the QoE, considering the number of video resolution changes. Finally, the demand on the network backhaul is decreased by around 38%.

Many applications in Wireless Sensor Networks (WSNs) require collecting massive data in a coordinated approach. To that end, a many-to-one (convergecast) communication pattern is used in tree-based WSNs. However, traffic near the sink node usually becomes the network bottleneck. In this work, we propose an extension to the 802.15.4 standard for enabling wider bandwidth channels. Then, we measure the speed of data collection in a tree-based WSN, with radios operating in these wider bandwidth channels. Finally, we propose and implement Funneling Wider Bandwidth (FWB), an algorithm that minimizes schedule length in networks. We prove that the algorithm is optimal in regard to the number of time slots. In our simulations and experiments, we show that FWB achieves a higher average throughput and a smaller number of time slots. This new approach could be adapted for other relevant emerging standards, such as WirelessHART, ISA 100.11a and IEEE 802.15.4e TSCH.