The growing adoption of prediction serving systems (PSSes) has made cost-saving deployment on preemptible spot instances crucial, yet frequent preemptions severely harm availability. While coded computation (CC) can keep availability cost-effectively by encoding original jobs into parity ones, its direct application to spot instances incurs prohibitive decoding overhead and tail latency under frequent preemptions. We identify two findings for optimization: (i) decoding asymmetry (only original job failures require decoding); (ii) preemption unevenness (variation in preemption rates across cloud regions). Leveraging these findings, we propose SpotCC, a new CC framework that strategically dispatches parity jobs to high-preemption (volatile) regions and original jobs to low-preemption (stable) regions. SpotCC designs locality-based and fine-grained volatility identification to reduce decoding operations and mitigate job congestion, respectively, and adaptively tunes configurations for decoding minimization. Experiments show that SpotCC improves P99 latency by 83.9% over state-of-the-arts, while maintaining ultra-low monetary costs.

To improve storage efficiency in large-scale clustered storage systems, deduplication that removes duplicate chunks has been widely deployed in distributed ways. Many distributed deduplication-related studies focus on backup storage, and some recent studies focus on deploying deduplication in clustered primary storage systems which store active data. While fragmentation is one of the traditional challenges in backup deduplication, we observe that a new fragmentation problem arises when performing deduplication in the clustered primary storage system due to the system's concurrent file writes. However, we find that existing state-of-the-art methods that address traditional fragmentation in backup deduplication fail to work effectively for the new fragmentation problem, as they significantly incur additional redundancy or lower the deduplication ratio. In this paper, we revisit fragmentation-solving methods in memory management and our main idea is inspired by the classic garbage collection methods in memory management: relocating fragments consecutively. Based on the idea, we propose an effective deduplication mechanism for clustered primary storage systems, ReoDedup, which applies: i) a cosine-similarity based chunk relocating algorithm that aims to minimize the fragmentation; ii) an adjacency-table based relocating heuristic that reduces the relocating's time complexity by placing two chunks residing in the same file consecutively; and iii) an indexremapping update scheme that alleviates the extra fragmentation caused by updates. We implement ReoDedup atop Ceph and our cloud experiments show that the average read throughput of ReoDedup can be increased by up to$1.72 \times$over state-of-thearts, without any deduplication ratio loss.

Live migration of virtual machines (VM) is one of the key characteristics of virtualization for load balancing, system maintenance, power management, etc., in data centers or clusters. In order to reduce the data transferred and shorten the migration time, the compression techniques have been widely used to accelerate VM migration. However, different compression approaches have different compression ratios and speeds. Because there is a trade-off between compression and transmission, the migration performance improvements obtained from different compression approaches are differentiated, and the improvements vary with the network bandwidth. Besides, the compression window sizes used in most compression algorithms are typically much larger than a single page size, so the traditional single page compression loses some potential compression benefits. In this paper, we design and implement a Bandwidth-Aware Compression (BAC) scheme for VM migration. BAC chooses suitable compression approach according to the network bandwidth available for the migration process, and employs multi-page compression. These features make BAC obtain more migration performance improvements from compression. Experiments under various network scenarios demonstrate that, compared with conventional compression approaches, BAC shortens the total migration time while achieving comparable performance for the total data transferred and the downtime.