We introduce SplitFt, a new fault-tolerance approach for storage-centric applications in disaggregated data centers. SplitFt uses a novel split architecture, where large writes are directly performed on the underlying disaggregated storage system, while small writes are made fault-tolerant within the compute layer. The split architecture enables applications to achieve strong durability guarantees without compromising performance. SplitFt makes small writes fault-tolerant using a new abstraction called near-compute logs or Ncl, which leverages underutilized memory on remote nodes to log small writes in a fast, cheap, and transparent manner. We port three POSIX applications (RocksDB, Redis, and SQLite) to SplitFt and show that they offer strong guarantees compared to weak versions of the applications that can lose data; SplitFt applications do so while approximating weak versions' performance (only 0.1%-10% overhead under YCSB). Compared to strong versions, SplitFt improves performance significantly (2.5× to 27× under write-heavy workloads).

Shared logs offer linearizable total order across storage shards. However, they enforce this order eagerly upon ingestion, leading to high latencies. We observe that in many modern shared-log applications, while linearizable ordering is necessary, it is not required eagerly when ingesting data but only later when data is consumed. Further, readers are naturally decoupled in time from writers in these applications. Based on this insight, we propose LazyLog, a novel shared log abstraction. LazyLog lazily binds records (across shards) to linearizable global positions and enforces this before a log position can be read. Such lazy ordering enables low ingestion latencies. Given the time decoupling, LazyLog can establish the order well before reads arrive, minimizing overhead upon reads. We build two LazyLog systems that provide linearizable total order across shards. Our experiments show that LazyLog systems deliver significantly lower latencies than conventional, eager-ordering shared logs.

Memory disaggregation has attracted great attention recently because of its benefits in efficient memory utilization and ease of management. So far, memory disaggregation research has all taken one of two approaches: building/emulating memory nodes using regular servers or building them using raw memory devices with no processing power. The former incurs higher monetary cost and faces tail latency and scalability limitations, while the latter introduces performance, security, and management problems.