In order to schedule resources efficiently or maintain applications' continuity for mobile customers, edge platforms often need to adaptively migrate the applications on them. However, our measurement shows that existing migration solutions cannot solve the issue of migrating video analytics applications in edge computing because the memory states of video analytics applications have different characteristics from other applications. We conduct a breakdown analysis of the memory states of video analytics applications, and propose to treat three types of states separately with three different techniques,i.e., warm-up, sync, and replay, to minimize the negative influence of migrations on application performance. Based on this idea, we implement a prototype system in which two new components,i.e.,state storeandsidecar, are designed to achieve near-transparent live migration with minimal application code modifications. Evaluation experiments demonstrate that the time of application interruption caused by migrating a video analytics application with our solution is less than 405ms, and our solution does not consume much resources.

Distributed learning is widely used to accelerate the training of deep learning models, but it is known that communication efficiency limits the scalability of distributed learning systems. Current gradient compression techniques provide promising methods to reduce communication time, but the extra time incurred by compression is not negligible. After compression techniques are applied, the communication time is significantly reduced because the data size needed to communicate becomes much smaller, but compressing gradients is time-consuming and it becomes a new bottleneck. In this paper, we design and implement PipeCompress, a system to decouple compression and backpropagation operations into two processes and pipeline the two processes to hide compression time. We also propose a specialized inter-process communication mechanism based on the characteristics of DNN distributed training to improve the efficiency of passing messages between the two processes, which makes sure that the decoupling does not bring much extra inter-process communication time cost. As far as we know, this is the first work that notices the overhead of compression and pipelines backpropagation and compression operations to hide compression time in distributed learning. Experiments show that PipeCompress can significantly hide compression time, reduce iteration time, and accelerate the training process on various DNN models.

The existing solutions to the migration of container-based applications are not suitable for live video analytics applications because these solutions can result in excessive migration time. Intuitively, it is possible to exploit the layered structure of containers to improve the migration performance, but we still need a good understanding of the characteristics of the containers related to video analytics applications to justify the intuitive idea and design a high-performance solution. In this paper, we pull 3735 representative images from Docker Hub. We analyze these images and get three main findings: (1) the images of video analytics applications have more layers and larger sizes than that of general applications; (2) we can cache images in the destination servers and reuse the same layers cached in the destination server to reduce the migration time when an image is migrated from its source server to its destination server; (3) the size of the remaining data to be transferred during migrations is still large and a high-performance migration scheme is still necessary. Based on the above findings, we propose a pipelined migration scheme to optimize migration performance. Evaluations show that pipelined migration performs significantly better than other migration schemes.

In smart cities, more and more government departments will make use of live analytics of videos from surveillance cameras in their tasks, such as vehicle traffic monitoring and criminal detection. Obviously, it is costly for each individual department to deploy its own infrastructure,i.e., cameras and analytics system. In this paper, we consider a scenario in which a city deploys an infrastructure and departments submit requests to access and analyze videos for their own purposes. The live analytics of massive streams is computation-intensive and the tasks might be latency-critical, which makes scheduling massive streams to optimize all tasks an essential and challenging work. We exploit an end-edge-cloud architecture and propose an adaptive system to schedule the massive camera streams and tasks, which considers all factors affecting the computation and networking resource consumption,e.g., sharing of model computation, video quality, model partition, and task placement. Particularly, the resource consumption ofFaster R-CNN + ResNet101under each partition scheme is profiled for the first time and we notice the partition must be used together with lossless compression techniques to be beneficial. Furthermore, sometimes tasks might be required to migrate because the scheduling decision made by the system changes to adapt to the changing resource supply and demand. In order to avoid the performance degradation during migration, we propose a non-destructive migration scheme and implement it in the system. Simulations demonstrate our system achieves a total utility close to the maximum and our analytics system performs better than state-of-the-art solutions.