Graph neural networks (GNNs) have achieved success in social recommender systems due to its capacity of capturing high-order collaborative filtering effects. However, some issues, such as vector scale distortion and over-smoothing, are obstacles to improving the performance of GNN-based models. This paper investigates these two issues and proposes an enhanced self-supervised graph learning (ESGL) algorithm based on GNN for social recommendation. By introducing a scale regulation module, ESGL can solve the issue of vector scale distortion. The scale regulation module takes the output of GNN as its input and generates the scaling matrix that limits the scale of embeddings. In addition, ESGL adopts two kinds of self-supervised learning schemes to enhance the recommendation performance. One scheme is to learn the similarity between users, while the other scheme is the embedding-level contrastive learning. The latter regards the initial normalized embedding of a user node and its average normalized embedding generated in the rest layers as positive samples. Experimental results have shown that our model is effective in improving recommendations.

Server consolidation is critical for energy efficiency of cloud-scale computing. In production environments like Aliyun, which is one of the largest public cloud platforms in the world, server consolidation has several challenges. First, the widespread use of local storage remarkably increases the migration cost (time). Second, the resource utilization of service instances varies over time, which may result in migration oscillation. Third, server consolidation must follow practical constraints. E.g., instances can only be migrated within a maintenance window, and both the resource utilization and the number of instances on a server are bounded. This paper designs and implements C4, a Cost-Efficient Consolidating Service for Aliyun's Cloud-Scale Computing, which Aliyun uses to consolidate servers. We analyze user pattern, resource utilization, and migration cost in Aliyun, showing that traditional utilization-based consolidation approaches cannot meet the needs in production environments, especially for the local-storage-based computing. This motivates us to propose the migration cost model, by which to select servers with the minimum migration time to release. We use the Worst-Fit heuristic to migrate instances to balance the load. Evaluation shows that C4 achieves cost-efficient, load-balanced, and oscillation-free consolidating service. We describe experience with over one year of C4 production deployment, lessons learned, and areas for future work.