The wide adoption of Docker containers for supporting agile and elastic enterprise applications has led to a broad proliferation of container images. The associated storage performance and capacity requirements place a high pressure on the infrastructure of container registries that store and distribute images and container storage systems on the Docker client side that manage image layers and store ephemeral data generated at container runtime. The storage demand is worsened by the large amount of duplicate data in images. Moreover, container storage systems that use Copy-on-Write (CoW) file systems as storage drivers exacerbate the redundancy. Exploiting the high file redundancy in real-world images is a promising approach to drastically reduce the growing storage requirements of container registries and improve the space efficiency of container storage systems. However, existing deduplication techniques significantly degrade the performance of both registries and container storage systems because of data reconstruction overhead as well as the deduplication cost. We propose DupHunter, an end-to-end deduplication scheme that deduplicates layers for both Docker registries and container storage systems while maintaining a high image distribution speed and container I/O performance. DupHunter is divided into three tiers: registry tier, middle tier, and client tier. Specifically, we first build a high-performance deduplication engine at the registry tier that not only natively deduplicates layers for space savings but also reduces layer restore overhead. Then, we use deduplication offloading at the middle tier to eliminate the redundant files from the client tier and avoid bringing deduplication overhead to the clients. To further reduce the data duplicates caused by CoWs and improve the container I/O performance, we utilize a container-aware storage system at the client tier that reserves space for each container and arranges the placement of files and their modifications on the disk to preserve locality. Under real workloads, DupHunter reduces storage space by up to 6.9× and reduces the GET layer latency up to 2.8× compared to the state-of-the-art. Moreover, DupHunter can improve the container I/O performance by up to 93% for reads and 64% for writes.

This study focuses on estimation and compensation on the unknown measurement noises of large-scale systems subject to nonlinear dynamics and interconnections. Interval type-2 T-S fuzzy model is used to describe each nonlinear subsystem. For the sake of attenuating adverse impacts from the unknown measurement noises, a combined method of reachable set bounding and estimation-compensation control is proposed. First, we introduce an augmented fuzzy observer that fulfills a simultaneous estimation for the system states and unknown measurement noises, which guarantees that the estimation error is bounded within the ellipsoidal boundary. Then, a fuzzy compensation controller is used to attenuate the impacts from the unknown measurement noises. Finally, simulation results show the effectiveness of our method against the unknown measurement noises.

Automatic manipulation and patterning of biological cells into an artificial scaffold is an imperative step in the production of high-quality tissue for tissue transplantation. This paper examines the incorporation of dielectrophoresis into a three-dimensional (3D) scaffold body for batch manipulation and patterning of cells. To facilitate dielectrophoresis-based manipulation, a multi-layer biocompatible scaffold structure utilizing its body as the integrated micro-electrodes was designed and fabricated using soft lithography. Voltage of opposite polarity was applied to the scaffold structure and the resultant electric field from the scaffold body polarized the cells in the culture medium and attracted them to migrate towards the scaffold body. Experiments were conducted and the results confirm that the proposed multi-layer scaffold is capable of generating dielectrophoretic forces to manipulate the cells to the scaffold surface, forming a three-dimensional cellular pattern automatically.