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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.

Server consolidation is a useful solution aiming at cost-efficiency and high resource utilization of data centers and clusters. Nowadays, as the data-intensive and I/O intensive applications are widely used, more attention is paid to the local storage-based clouds which can offer much better I/O performance at relatively low price compared with the shared storage. However, it will obviously increase the migration cost (e.g., energy and time). Meanwhile, there is few suitable resource demand estimation method for local storage-based clouds at present, which plays an important role in system's migration efficiency. And we find out that in this specific storage architecture, almost all the existing server consolidation algorithms do not have a suitable resource demand estimation method and a live migration scheme. To solve this problems, this paper designs and implements Combining Three (C3), a cloud architecture for local storage, C3 significant modules: prediction, consolidation, and migration. It was proved in statistical analysis that ARIMA may be the most suitable prediction model for the server workload, which motivates us to propose the resource estimation predictor. Also, we improve the existing consolidation method by adjusting the sorting index and fit degree during migration. Then, we propose a live migration scheme for local storage environment as the third module of C3. We conduct extensive experiments using real-world traces from Google to validate the effectiveness and superiority of our proposed algorithm.

Server consolidation plays a significant part in energy-saving technology in data centers. Traditionally, cloud service instances commonly use shared storage architecture. Nowadays, data and I/O intensive applications are preferred in this big data era and are used in the majority of Internet companies, much more attention has been paid to the local storage that offer perform better in I/O at a lower price compared with shared storage clouds. But these cloud instances usually contain much more data than shared storage cloud instances. Thus, in such local storage based clouds, the migration cost can be really high. Unfortunately, most existing work about did not consider integrating the demand prediction algorithm that plays a significant part in server consolidation, especially for local storage based cloud, where the migration cost is very high and is in badly need of an efficient resource pre-allocation mechanism. To address this issue, we proposes Aricon, a consolidation method based on local storage. Our approach uses a time series model to forecast the CPU or memory utilization of instances within servers. We investigate the effectiveness of instance and server resource utilization prediction in server consolidation performance in workload traces from real world. To validate the performance of the proposed Aricon, we test the prediction accuracy and compare it several existing consolidation method, and the results show that Aricon not only has low prediction error rate in 10.7% but also schedules computing resources efficiently.