Kernel operations in resource-constrained edge systems, such as memory swapping and deduplication, use the access frequency (hotness) of memory pages to guide page placement and reclamation. However, these operations suffer from page-hotness skew: a page may contain a mix of highly accessed and infrequently accessed objects, which causes inaccurate page-level classification, wasted DRAM capacity, and expensive I/O.
We attribute this skewness to a cross-layer mismatch: the kernel manages memory at page granularity, whereas user-level allocators place objects without considering access hotness.

To bridge this gap, we present HotMalloc, a memory allocator that reduces this skewness through object-granularity hotness-segregated allocation.
HotMalloc uses profile-guided optimization to analyze object access patterns offline and synthesizes an application-specific allocator.
At runtime, HotMalloc identifies object hotness from offset-encoded call-site contexts and co-locates objects with similar hotness on the same pages without adding per-access overhead.
Additionally, HotMalloc exposes simple interfaces to inform the kernel of page hotness.
Evaluation on memory swapping and deduplication shows that HotMalloc significantly reduces skewness and improves hotness-aware kernel operations by 4.6% to 42.1%.