Cloud-Edge Continuum Computing Platform hosts real-time applications over edge and cloud resources. The execution time of real-time applications composed of micro-services is increased by the communication latency, which is the time between a packet entering and leaving the network. To keep the execution time small, a latency-guaranteed network, which determines an upper bound of the latency, will be effective. Motivated by this requirement, we have been enhancing Deterministic Networking (DetNet), a protocol suite that builds a latency-guaranteed network over Wide-Area Networks (WAN).

Federated learning trains a model on a centralized server using datasets distributed over a massive amount of edge devices. Since federated learning does not send local data from edge devices to the server, it preserves data privacy. It transfers the local models from edge devices instead of the local data. However, communication costs are frequently a problem in federated learning. This paper proposes a novel method to reduce the required communication cost for federated learning by transferring only top updated parameters in neural network models. The proposed method allows adjusting the criteria of updated parameters to trade-off the reduction of communication costs and the loss of model accuracy. We evaluated the proposed method using diverse models and datasets and found that it can achieve comparable performance to transfer original models for federated learning. As a result, the proposed method has achieved a reduction of the required communication costs around 90% when compared to the conventional method for VGG16. Furthermore, we found out that the proposed method is able to reduce the communication cost of a large model more than of a small model due to the different threshold of updated parameters in each model architecture.

Maintaining hand hygiene is the one of the most effective way to prevent the spread of germs during a pandemic. This paper focuses on encouraging people to use a hand sanitizer more frequently by applying the nudge theory to improve hand hygiene behavior in private organizations. We propose a system that recognizes hand hygiene behavior using face recognition and detects hand sanitizer use. The system responds to the user's personal hand hygiene behavior with animation of a virtual bonsai as an interactive agent. To preserve user privacy, we implemented the system on an edge device and conducted experiments for 4 case studies in 2 real-world organizations. The results showed that the system improved the hand hygiene behavior of people in a private organization.

Federated learning trains a model on a centralized server using datasets distributed over a large number of edge devices. Applying federated learning ensures data privacy because it does not transfer local data from edge devices to the server. Existing federated learning algorithms assume that all deployed models share the same structure. However, it is often infeasible to distribute the same model to every edge device because of hardware limitations such as computing performance and storage space. This paper proposes a novel federated learning algorithm to aggregate information from multiple heterogeneous models. The proposed method uses weighted average ensemble to combine the outputs from each model. The weight for the ensemble is optimized using black box optimization methods. We evaluated the proposed method using diverse models and datasets and found that it can achieve comparable performance to conventional training using centralized datasets. Furthermore, we compared six different optimization methods to tune the weights for the weighted average ensemble and found that tree parzen estimator achieves the highest accuracy among the alternatives.

Running neural network models on edge devices is attracting much attention by neural network researchers since edge computing technology is becoming more powerful than ever. However, deploying large neural network models on edge devices is challenging due to the limitation in available computing resources and storage space. Therefore, model compression techniques have been recently studied to reduce the model size and fit models on resource-limited edge devices. Compressing neural network models reduces the size of a model, but also degrades the accuracy of the model since it reduces the precision of weights in the model. Consequently, a retraining method is required to recover the accuracy of compressed models. Most existing retraining methods require the original labeled training datasets to retrain the models, but labeling is a time-consuming process. In particular, we cannot always access the original labeled datasets because of privacy policies and license limitations. In this paper, we propose a method to retrain a compressed neural network model with an unlabeled dataset that is different from the original labeled dataset. We compress the neural network model using quantization to decrease the size of the model. Subsequently, the compressed model is retrained by our proposed retraining method without using a labeled dataset to recover the accuracy of the model. We compared the proposed retraining method against the conventional retraining. The proposed method reduced the size of VGG-16 and ResNet-50 by 81.10% and 52.45%, respectively without significant accuracy loss. In addition, our proposed retraining method is clearly faster than the conventional retraining method.

In response to the Coronavirus 2019 (COVID-19) pandemic, the World Health Organization (WHO) has published preventive measures such as performing hand hygiene frequently, wearing a medical mask, trying to avoid touching face and so on. This paper presents a nudge-based system to promote hand hygiene in a private organization. The proposed system consists of a hand sanitizer station equipped with a magnetic sensor to sense user presses. We conducted 4 case studies to compare the effects of nudging on the frequency of hand sanitizer use: no nudging, traditional nudging, non-personalized nudging, and personalized nudging. The results reveal that using nudge-based methods offer a significant increase in the frequency hand sanitizer use.

Data centers are centralized facilities where computing and networking hardware are aggregated to handle large amounts of data and computation. In a data center, computing resources such as CPU and memory are usually managed by a resource manager. The resource manager accepts resource requests from users and allocates resources to their applications. A commonly known problem in resource management is that users often request more resources than their applications actually use. This leads to the degradation of overall resource utilization in a data center. This paper aims to improve resource utilization in data centers by predicting the required resource for each application. We designed and implemented a neural network model based on Long Short-Term Memory (LSTM) to predict more efficient resource allocation for a job based on historical data. Our model has two LSTM layers each of which learns the relationship between: (1) allocation and usage, and (2) CPU and memory. We used Googles cluster-usage trace, which contains a trace of resource allocation and usage for each job executed on a Google data center, to train our neural network. Googles cluster scheduler simulator was used to evaluate our proposed method. Our simulation indicated that the proposed method improved the CPU utilization and memory utilization by 10.71% and 47.36%, respectively, compared to a conventional resource manager. Moreover, we discovered that increasing the memory cell size of our LSTM model improves the accuracy of the prediction in return for longer training time.