Memory scalability has emerged as a critical bottleneck in virtualized cloud environments. Tiered memory architectures that combine limited fast memory with abundant slower memory offer a promising solution, but existing hypervisor-based approaches suffer from significant performance penalties. We present Demeter, introducing a paradigm shift through guest-delegated tiered memory management based on two key insights: (1) delegation to guests eliminates both expensive access tracking at the hypervisor level and frequent TLB flushes that severely degrade memory virtualization performance under two-dimensional address translation, and (2) Processor Event-Based Sampling, which cannot be effectively utilized by hypervisor-based solutions, remains fully functional and highly efficient when properly leveraged within the guest. Building on these insights, Demeter designs an efficient range-based tiered memory management scheme in guest virtual address space to preserve locality information and employs a double balloon-based provisioning mechanism that maintains cloud elasticity while enabling vendor-specific QoS control. Our evaluation with seven real-world workloads across DRAM+PMEM and DRAM+CXL.mem configurations demonstrates that Demeter improves performance by up to 2× compared to existing hypervisor-based approaches and by 28% on average compared to the next best guest-based alternative. Our implementation is fully open source and publicly available at Zenodo.

Disaggregated memory (DM) has garnered increasing attention due to high resource utilization. Fault tolerance is critical for key-value (KV) stores on DM since machine failures are common in datacenters. Existing KV stores on DM are generally based on replication to achieve fault tolerance, which however suffer from high memory space and performance overheads. In this paper, we investigate the efficiency of different fault-tolerant mechanisms on DM and reveal that checkpointing and erasure coding work best for the index and KV pairs respectively. Based on these observations, we present Aceso, a DM-based KV store that employs a hybrid fault-tolerant mechanism, combining checkpointing for the index and erasure coding for KV pairs. However, applying this hybrid mechanism to DM introduces multiple challenges, i.e., performance interference and data loss of checkpointing, slow space reclamation and failure recovery of erasure coding. To address these challenges, Aceso leverages a differential checkpointing scheme to reduce performance interference incurred by the bandwidth consumption to transmit checkpoints, a versioning approach to recover lost index updates on failures, a delta-based space reclamation mechanism to reclaim obsolete KV pairs with negligible overhead, and a tiered recovery scheme to minimize user disruption. Our experiments show that Aceso simultaneously achieves up to 2.7× throughput improvement, up to 54% tail latency reduction, and 44% memory space savings compared with the state-of-the-art replication-based KV store on DM.