In recent years, the design of green datacenters and their enabling technologies, including renewable power managements, have gained a lot of attraction in both industry and academia. However, the maintenance and upgrade of the underlying server system over time (e.g., server replacement due to failures, capacity increases, or migrations), which make datacenters increasingly more heterogeneous in their key processing components (e.g., capacity and variety of processors, memory and storage devices), present a great challenge to optimal allocation of renewable power supply. In other words, the current heterogeneity-unaware power allocation policies have failed to achieve optimal performance given a limited and time varying renewable power supply. In this paper, we propose a dynamic power allocation framework called GreenHetero, which enables adaptive power allocation among heterogeneous servers in green datacenters to achieve the optimal performance when the renewable power varies. Specifically, the GreenHetero scheduler dynamically maintains and updates a performance-power database for each server configuration and workload type through lightweight profiling method. Based on the database and power prediction, the scheduler leverages a well-designed solver to determine the optimal power allocation ratio among heterogeneous servers at runtime. Finally, the power enforcer is used to implement the power source selections and the power allocation decisions. We build an experimental prototype to evaluate GreenHetero. The evaluation shows that our solution can improve the average performance by 1.2x-2.2x and the renewable power utilization by up to 2.7x under tens of representative datacenter workloads compared with the heterogeneity-unaware baseline scheduler.

Data deduplication, though being efficient for redundancy elimination in storage systems, introduces chunk fragmentation which severely decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Typically, the backup software aggregates files into larger “tar” type files for storage. We observe that, in tar type datasets, a large number of Persistent Fragmented Chunks (PFCs) are repeatedly rewritten by state-of-the-art rewriting algorithms in every backup, which severely impacts restore performance. We found that the existence of PFCs is due to the traditional strategy of storing PFCs along with other chunks in the containers to preserve the stream locality, rendering them always stored in the containers with low utilization. We propose DePFC to reduce PFCs. DePFC identifies and removes PFCs from the containers preserving the stream locality, and groups them together, to increase the utilization of containers holding them for the subsequent backup, thus preventing them from being rewritten again. We further propose an FC Buffer to avoid mistaken rewrites of PFCs and grouping PFCs that cause restore cache thrashing together. Experimental results demonstrate that DePFC improves restore performance of state-of-the-art rewriting algorithms by 44.24-89.42 percent, while attaining comparable deduplication efficiency, and FC Buffer further improves restore performance.