Per-flow size measurement is key to many streaming applications and management systems, particularly in high-speed networks. Performing such measurement on the data plane of a network device at the line rate requires on-chip memory and computing resources that are shared by other key network functions. It leads to the need for very compact and fast data structures, called sketches, which trade off space for accuracy. Such a need also arises in other application context for extremely large data sets. The goal of sketch design is two-fold: to measure flow size as accurately as possible and to do so as efficiently as possible (for low overhead and thus high processing throughput). The existing sketches can be broadly categorized to multi-update sketches and single update sketches. The former are more accurate but carry larger overhead. The latter incur small overhead but their accuracy is poor. This paper proposes a Single update Sketch with a Variable counter Structure (SSVS), a new sketch design which is several times faster than the existing multi-update sketches with comparable accuracy, and is several times more accurate than the existing single update sketches with comparable overhead. The new sketch design embodies several technical contributions that integrate the enabling properties from both multi-update sketches and single update sketches in a novel structure that effectively controls the measurement error with minimum processing overhead.

Sampling is key to handling mismatch between the line rate and the throughput of a network traffic measurement module. Flow-spread measurement requires non-duplicate sampling, which only samples the elements (carried in packet header or payload) in each flow when they appear for the first time and blocks them for subsequent appearances. The only prior work for non-duplicate sampling incurs considerable overhead, and has two practical limitations: It lacks a mechanism to set an appropriate sampling probability under dynamic traffic conditions, and it cannot efficiently handle multiple concurrent sampling tasks. This paper proposes a virtual filter design for non-duplicate sampling, which reduces the processing overhead by about half and reduces the memory overhead by an order of magnitude or more under some practical settings. It has a mechanism to automatically adapt its sampling probability to the traffic dynamics. It can be modified to handle sampling for multiple independent tasks with different probabilities. We also enhance the virtual filter for flow spread measurement and super spreader detection with a large measurement period.

Internet-connected devices have been surging rapidly during the past years. Many important applications based upon such devices have emerged, such as vehicle tracking systems. These applications often require real-time execution of a large number of computation tasks. Edge computing has shown great potential in processing frequent but less-demanding tasks. Additionally, cloud computing allows for great scalability when substantial computing resources are needed. Edge-cloud computing is a paradigm that combines edge computing and cloud computing. A key problem in edge-cloud computing is how to determine the execution location for each computation task. We propose a dynamic edge-twin computing model in the context of edge-cloud computing. It uses an evaluation mechanism to predict the completion times of the task at both an edge device and a cloud server. The completion time includes data transfer time and computing time and it is determined based on real-time information about the task and the computing environment. The task will be executed by the device with a shorter completion time. We have implemented a vehicle tracking system under the edge-twin model. The experimental results show that the edge-twin model outperforms edge-alone computing and cloud-alone computing.

Machine learning and artificial neural networks (ANNs) have been at the forefront of medical research in the last few years. It is well known that ANNs benefit from big data and the collection of the data is often decentralized, meaning that it is stored in different computer systems. There is a practical need to bring the distributed data together with the purpose of training a more accurate ANN. However, the privacy concern prevents medical institutes from sharing patient data freely. Federated learning and multi-party computation have been proposed to address this concern. However, they require the medical data collectors to participate in the deep-learning computations of the data users, which is inconvenient or even infeasible in practice. In this paper, we propose to use matrix masking for privacy protection of patient data. It allows the data collectors to outsource privacy-sensitive medical data to the cloud in a masked form, and allows the data users to outsource deep learning to the cloud as well, where the ANN models can be trained directly from the masked data. Our experimental results on deep-learning models for diagnosis of Alzheimer's disease and Parkinson's disease show that the diagnosis accuracy of the models trained from the masked data is similar to that of the models from the original patient data.