Cloud service providers increasingly adopt SmartNICs to offload data-plane services (e.g., DPDK and SPDK) and control-plane tasks (such as disk and NIC initialization). Our analysis of production environments reveals that data-plane services statically provision CPUs for peak load, resulting in 67.5% idle CPU cycles during 99% of their runtime in IaaS clouds, leading to wasted CPU resources. On the other hand, control-plane tasks fail to meet critical Service Level Objectives (SLOs), such as virtual machine startup time. Unfortunately, achieving control-plane SLO improvements through co-scheduling with idle data-plane services remains highly challenging, due to the combined effects of intrinsic scheduling latency and the substantial architectural complexity inherent to control-plane ecosystems.

Crowd counting in complex scene is an important but challenge task. The scale variation of crowd makes the shallow network hard to extract effective features. In this paper, we propose a shallow single column network named CSCNet for crowd counting. The key component is complementary scale context block (CSCB). It is designed to capture complementary scale context and obtains a high accuracy with limited depth of the network. As far as we know, CSCNet is the shallowest single column network in existing works. We demonstrate our methods on three challenge benchmarks. Compared to state-of-the-art methods, CSCNet achieves comparable accuracy with much less complexity. CSCNet provides an alternative to achieve comparable or even better performance with about 30% of depth and 50% of width decrease. Besides, CSCNet performs more stably on both sparse and congested crowd scenes.