Inter-datacenter wide area networks (inter-DC WAN) carry a significant amount of data transfers that require to be completed within certain time periods, or deadlines. However, very little work has been done to guarantee such deadlines. The crux is that the current inter-DC WAN lacks an interface for users to specify their transfer deadlines and a mechanism for provider to ensure the completion while maintaining high WAN utilization.This paper addresses the problem by introducing a Deadline-based Network Abstraction (DNA) for inter-DC WANs. DNA allows users to explicitly specify the amount of data to be delivered and the deadline by which it has to be completed. The malleability of DNA provides flexibility in resource allocation. Based on this, we develop a system called Amoeba that implements DNA. Our simulations and testbed experiments show that Amoeba, by harnessing DNA's malleability, accommodates 15% more user requests with deadlines, while achieving 60% higher WAN utilization than prior solutions.
Virtualization poses new challenges to I/O performance. The single-root I/O virtualization (SR-IOV) standard allows an I/O device to be shared by multiple Virtual Machines (VMs), without losing runtime performance. We propose a generic virtualization architecture for SR-IOV devices, which can be implemented on multiple Virtual Machine Monitors (VMMs). With the support of our architecture, the SR-IOV device driver is highly portable and agnostic of underlying VMM. Based on our first implementation of network device driver, we applied several optimizations to reduce virtualization overhead. Then, we carried out comprehensive experiments to evaluate SR-IOV performance and compare it with paravirtualized network driver. The results show SR-IOV can achieve line rate (9.48Gbps) and scale network up to 60 VMs at the cost of only 1.76% additional CPU overhead per VM, without sacrificing throughput. It has better throughout, scalability, and lower CPU utilization than paravirtualization.
Video object detection is more challenging than image object detection because of the deteriorated frame quality. To enhance the feature representation, state-of-the-art methods propagate temporal information into the deteriorated frame by aligning and aggregating entire feature maps from multiple nearby frames. However, restricted by feature map's low storage-efficiency and vulnerable contentaddress allocation, long-term temporal information is not fully stressed by these methods. In this work, we propose the first object guided external memory network for online video object detection. Storage-efficiency is handled by object guided hard-attention to selectively store valuable features, and long-term information is protected when stored in an addressable external data matrix. A set of read/write operations are designed to accurately propagate/allocate and delete multi-level memory feature under object guidance. We evaluate our method on the ImageNet VID dataset and achieve state-of-the-art performance as well as good speedaccuracy tradeoff. Furthermore, by visualizing the external memory, we show the detailed object-level reasoning process across frames.
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