In this paper, we introduce a new method for the task of interaction transfer. Given an example interaction between a source object and an agent, our method can automatically infer both surface and spatial relationships for the agent and target objects within the same category, yielding more accurate and valid transfers. Specifically, our method characterizes the example interaction using a combined spatial and surface representation. We correspond the agent points and object points related to the representation to the target object space using a learned spatial and surface correspondence field, which represents objects as deformed and rotated signed distance fields. With the corresponded points, an optimization is performed under the constraints of our spatial and surface interaction representation and additional regularization. Experiments conducted on human-chair and hand-mug interaction transfer tasks show that our approach can handle larger geometry and topology variations between source and target shapes, significantly outperforming state-of-the-art methods.

Provisioning cloud native services via containers has been regarded as a promising way to promote the cloud elasticity. A container may simultaneously sustain multiple services with a number of different communication sessions. It is of great importance to predict them for fine-grain system management. However, this is a non-trivial task as the session traffics are all invisible. The only thing we can get is the container network interface usage as the total traffic of all coexisting sessions. In this paper, we propose a machine learning based session level traffic prediction framework called X-Rayer, to predict respective session traffics from the network interface usage. Via a sliding-window based ensemble empirical mode decomposition algorithm, X-Rayer first accurately predicts the interface usage, which is then decomposed into session traffics by an invented ConvGRU formed by convolutional neural network and gated recurrent unit. Specially, the spatial-temporal correlations of the interface usages are abstracted via an attention strategy and explored for accurate session traffic decomposition. Through extensive trace-driven experiments, we show that our X-Rayer provides more accurate results by decreasing the average RMSE in the interface usage prediction by 33.25% and 33.71%, and session traffic estimation by 18.05%, 27.04%, 21.91%, and 16.43%, compared to state-of-the-art approaches.

Lightweight container-based microservice has been widely advocated to promote the elasticity of edge cloud. The inherent layered structure of containers offers a compelling way to cope with the resource scarcity of edge servers through layer sharing, which can significantly increase storage utilization and improve the edge throughput. Recent studies show that it is possible to share layers not only within the same server but also between servers, which microservice deployment can take full advantage of. In this paper, we investigate the problem of how to collaboratively deploy microservices by incorporating both intra-server and inter-server layer sharing to maximize the edge throughput. We formulate this problem into an integer linear programming form and prove it as NP-hard. We propose a randomized rounding based heuristic algorithm, and conduct formal analysis on the guaranteed approximation ratio. Through extensive experiments, we verify the efficiency of our proposed algorithm, and the results demonstrate that it can deploy 6× and 12× more microservice instances, and improve the edge throughput by 27.74% and 38.46% in comparison with state-of-the-art strategies.