The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduced substantial training challenges. Graph dataset condensation techniques have emerged to compress large datasets into smaller yet information-rich datasets, while maintaining similar test performance. However, these methods strictly require downstream applications to match the original dataset and task, which often fails in cross-task and cross-domain scenarios. To address these challenges, we propose a novel causal-invariance-based and transferable graph dataset condensation method, named TGCC, providing effective and transferable condensed datasets. Specifically, to preserve domain-invariant knowledge, we first extract domain causal-invariant features from the spatial domain of the graph using causal interventions. Then, to fully capture the structural and feature information of the original graph, we perform enhanced condensation operations. Finally, through spectral-domain Enhanced contrastive learning, we inject the causal-invariant features into the condensed graph, ensuring that the compressed graph retains the causal information of the original graph. Experimental results on five public datasets and our novel FinReport dataset demonstrate that TGCC achieves up to a 13.41% improvement in cross-task and cross-domain complex scenarios compared to existing methods, and achieves state-of-the-art performance on 5 out of 6 datasets in the single dataset and task scenario.

Live migration of virtual network functions (VNF) is a powerful technique with benefits of server maintenance, resource management and dynamic workload re-balance, among others. Downtime and total migration time are mainly two vital indicators to describe the performance of the VNF live migration (VLM). Modern research has effectively reduced the downtime to zero for some specific VNFs (eg. virtual router). However, for general VNFs predominantly leveraging pre-copy approach, such as firewalls, network address translators (NAT), load balancers, etc., there still remain some intractable problems: inevitable service downtime and long migration time on account of large amount of data transferred during migration, both of which result in a severe performance degradation of VNF services. To resolve these issues, we present a solution called PV-QAT to accelerate the migration process for these general VNFs. The PV-QAT creatively exploits the Para-Virtualization (PV) driver to filter out the useless memory pages in the process of migration, and unprecedentedly applies QuickAssist Technology (QAT) to provide fast compression of memory pages with low overhead, The experimental results show that PV-QAT can significantly reduce 77.5% of downtime and 80.5% of total migration time on average when compared with original pre-copy migration of KVM.

Innovative Human Machine Interface technologies are fundamentally reshaping the way people live, entertain and work. Passive RFID tags, benefiting from its wireless, inexpensive and battery-free sensing ability, are gradually being applied in new-style interaction interfaces, ranging from virtual touch screen to 3D mouse. This paper presents TagController, a universal wireless and battery-free remote controller with two types of interactive actions. The key insight is that the fine-grained phase information extracted from RF signals is capable of perceiving various actions. TagController can recognize 10 actions without any training or prestored profiles by executing a sequence of functional components, i.e. preprocessor, action detector and action recognizer. We have implemented TagController with COTS RFID devices and conducted substantial experiments in different scenarios. The results demonstrate that TagController can achieve an average recognition accuracy of 95.8% and 94.3% in the scenarios of one and two remote controllers, respectively, which promises its feasibility and robustness.

Workflow recognition is a key technique in the field of activity recognition with benefits of monitoring the step being performed in the workflow, detecting the missing step, and providing assistance to the performer of the workflow, among others. In this paper, we present an unobtrusive workflow recognition system called RFlow-ID, which is the first device-free, battery-free and privacy-preserving workflow recognition system based on RFID technique. RFlow-ID perceives the use and movement of associated objects in the workflow using fine-grained phase information extracted from low-level RF signal, and infers the most likely sequence of workflow activities via a VQ-HMM model. We implement RFlow-ID on COTS RFID devices and evaluate it through a common biomedical experiment. The results validate the high recognition accuracy and robustness of our system.

Live migration of a virtual machine (VM) is a powerful technique with benefits of server maintenance, resource management, dynamic workload re-balance, etc. Modern research has effectively reduced the VM live migration (VMLM) time to dozens of milliseconds, but live migration still exhibits failures if it cannot terminate within the given time constraint. The ability to predict this type of failure can avoid wasting networking and computing resources on the VM migration, and the associated system performance degradation caused by wasting these resources. The cost of VM live migration highly depends on the application workload of the VM, which may undergo frequent changes. At the same time, the available system resources for VM migration can also change substantially and frequently. To account for these issues, we present a solution called MigVisor, which can accurately predict the behaviour of VM migration using working-set model. This can enable system managers to predict the migration cost and enhance the system management efficacy. The experimental results prove the design suitability and show that the MigVisor has a high prediction accuracy since the average relative error between the predicted value and the measured value is only 6.2%~9%.