Yongjie Wang, Xiaoqi Qiu, Yu Yue, Xu Guo, Zhiwei Zeng, Yuhong Feng, Zhiqi Shen. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.

Service providers tend to deploy application services to several different virtual machines (VMs) to improve the scalability and manageability of the cloud data center (CDC). Therefore, high frequency communication traffic is always involved among those VMs that are deployed the same application service. In order to reduce the communication cost (CC) of CDC, all VMs running the same service should be redeployed in the same subnet as much as possible by using live migration technology, because CC between VMs in different subnets is much higher than that within the same subnet. On the other hand, the migration time (MT) to complete all migration tasks is also crucial for providers and customers, because a prolonged MT will lead to the increased maintenance cost and the deterioration of quality of service (QoS). To address the aforementioned issues, this paper proposes an efficient service-aware VM scheduling approach (ES-VSA) based on a multi-objective evolutionary algorithm to minimize CC and MT, simultaneously. Finally, experiments are conducted on four different scenarios, and the simulation results demonstrate that our proposed algorithm is superior to several state-of-the-art algorithms in terms of both CC and MT.

Since the advent of cloud computing, Elastic Computing (EC) has become the standard architecture for resource allocation and scheduling. EC typically allocates computing resources based on predefined specifications, such as virtual machine or container flavors. However, these flavors are often constrained by fixed CPU-to-memory ratios, which frequently fail to match the actual resource needs of applications. As a result, cloud providers experience high resource allocation rates nearing saturation ( $\gt $ 80%) but with low utilization ( $\lt $ 25%). This study introduces Flexible Computing (FC), a novel approach to resource allocation and scheduling. Unlike EC, FC allocates resources based on an application resource usage profile, derived from the historical resource consumption of workloads, rather than relying on fixed specifications. Additionally, FC incorporates a real-time performance degradation detection mechanism to address performance issues caused by the noisy-neighbor effect when colocated workloads interfere with each other. FC dynamically adjusts resource allocation according to actual usage, ensuring that application performance meets Service Level Agreements (SLAs), while preventing resource waste and performance degradation from improper resource over-commitment. Large-scale experimental validations conducted on the FC architecture within Huawei Cloud data centers demonstrate that, compared to EC, FC can reduce computing resource consumption by over 33% while managing the same workloads. Furthermore, FC's real-time performance degradation detection model achieves a prediction error of less than 5% across various testing environments, highlighting its commercial viability.

With the rapid development of autonomous driving and SLAM technology, the perception system of a vehicle heavily relies on laser and image sensors to capture the real-world scenario and avoid obstacles autonomously. To achieve accurate and robust multi-sensor fusion computation, high-precision extrinsic calibration of camera and laser scanner is a necessary requirement. Traditional multi-sensor calibration methods based on manual features rely on specific scenarios and may not provide feature information over long distances. In this paper, we present a novel approach for robustly calibrating the extrinsic parameters of a solid-state(SS) lidar-camera system in a natural environment. Our proposed method begins with obtaining robust line feature information. we first innovatively employ a super-voxel clustering method to extract global 3D line features from the complete point cloud and then back-project these 3D line features into 2D space. Afterward, a transformer-based edge detection network, EDTER, is used to detect the edge features and estimate the probability pixel-by-pixel. To consider the uncertainty of two-dimensional line features and the inconsistency of residuals at different distances, we construct a line feature weight model for line feature residual calculation. Finally, we minimize the residual errors using least squares optimization to recover the relative pose of the camera and the lidar sensor. We conducted a performance study to compare our proposed method against existing targetless calibration methods on various natural scenarios. The experimental results demonstrate that our proposed method achieves higher robustness, accuracy, and consistency, making it suitable for real-world applications.