Azure Functions maintains pools of pre-warmed containers to avoid the high container-allocation latency. The size of a pool is important: a pool that is too small leads to high allocation latency, whereas a pool that is too large wastes resources and increases cost. Service providers typically oversize pools to meet service-level objectives (SLOs). Our findings indicate that the cost of maintaining pre-warmed container pools dominates the overall platform cost, motivating the need for effective pool management strategies.

While numerous Content-Defined Chunking (CDC) algorithms exist for data deduplication, their relative performance has not been analyzed in the presence of low-entropy induced byte-shifting. This paper explores and evaluates hash-based and hashless CDC algorithms in the presence of low-entropy data regions, using synthetic datasets. Our evaluation shows that modern CDC algorithms are poor at handling low-entropy blocks when the block sizes are small and that their low-entropy detection ability depends upon the expected average chunk size. Contrary to previous studies focusing on conventional byte-shifting, hash-based algorithms achieve poor space savings compared to their hashless counterparts when low-entropy induced byte-shifting is involved. This can be explained by the greater variability in chunk sizes and the higher percentage of artificial boundaries they exhibit in the presence of these regions. All of these factors together highlight the need for specialized CDC algorithms to detect and eliminate low-entropy data blocks during the deduplication process.

Data deduplication is critical to cloud storage providers and is widely employed to conserve server-side storage space. Data chunking is an important aspect of deduplication, being directly responsible for storage space savings and end-to-end system throughput. While deduplication systems deployed in production favor larger chunk sizes, existing data chunking algorithms are slow and offer minimal throughput increases with increasing chunk size.