Mitigating the Performance-Efficiency Tradeoff in Resilient Memory Disaggregation

Memory disaggregation has received attention in recent years as a promising idea to reduce the total cost of ownership (TCO) of memory in modern datacenters. However, relying on remote memory expands an application's failure domain and makes it susceptible to tail latency variations. In attempts to making disaggregated memory resilient, stateof-the-art solutions face the classic tradeoff between performance and efficiency: some double the memory overhead of disaggregation by replicating to remote memory, while many others limit performance by replicating to the local disk. We present Hydra, a configurable, erasure-coded resilience mechanism for common memory disaggregation solutions. It can transparently handle uncertainties arising from remote failures, evictions, memory corruptions, and stragglers from network imbalance with a significantly better performance-efficiency tradeoff than the state-of-the-art. We design a fine-tuned data path to achieve single us read/write latency to remote memory, develop decentralized algorithms for cluster-wide memory management, and analyze how to select parameters to mitigate independent and correlated uncertainties. Our integration of Hydra with two major memory disaggregation systems and evaluation on a 50-machine RDMA cluster demonstrates that it achieves the best of both worlds: it improves the latency and throughput of memory-intensive applications by up to 64.78X and 20.61X, respectively, over the state-of-the-art disk backup-based solution. At the same time, it provides performance similar to that of in-memory replication with 1.6X lower memory overhead.