Disaggregated memory (DM) is a widely discussed datacenter architecture in academia and industry. It decouples computing and memory resources from monolithic servers into two network-connected resource pools. Range indexes are widely adopted by storage systems on DM to efficiently locate and query remote data. However, existing range indexes on DM suffer from either high computing-side cache consumption or high memory-side read amplifications. In this paper, we propose CHIME, a hybrid index combining B+ trees with hopscotch hashing, to achieve low cache consumption and low read amplifications simultaneously. There are three challenges in constructing CHIME on DM, i.e., the complicated optimistic synchronization, the extra metadata access, and the read amplifications introduced by hopscotch hashing. CHIME leverages 1) a three-level optimistic synchronization scheme to synchronize read and write operations with various granularities, 2) an access-aggregated metadata management technique to eliminate extra metadata accesses by piggybacking and replicating metadata, and 3) an effective hotness-aware speculative read mechanism to mitigate the read amplifications of hopscotch hashing. Experimental results show that CHIME outperforms the state-of-the-art range indexes on DM by up to 5.1× with the same cache size and achieves similar performance with up to 8.7× lower cache consumption.

Disaggregated memory (DM) is an increasingly prevalent architecture with high resource utilization. It separates computing and memory resources into two pools and interconnects them with fast networks. Existing range indexes on DM are based on B+ trees, which suffer from large inherent read and write amplifications. The read and write amplifications rapidly saturate the network bandwidth, resulting in low request throughput and high access latency of B+ trees on DM. In this article, we propose that the radix tree is more suitable for DM than the B+ tree due to smaller read and write amplifications. However, constructing a radix tree on DM is challenging due to the costly lock-based concurrency control, the bounded memory-side IOPS, and the complicated computing-side cache validation. To address these challenges, we design SMART , the first radix tree for disaggregated memory with high performance. Specifically, we leverage (1) a hybrid concurrency control scheme including lock-free internal nodes and fine-grained lock-based leaf nodes to reduce lock overhead, (2) a computing-side read-delegation and write-combining technique to break through the IOPS upper bound by reducing redundant I/Os, and (3) a simple yet effective reverse check mechanism for computing-side cache validation. Experimental results show that SMART achieves 6.1× higher throughput under typical write-intensive workloads and 2.8× higher throughput under read-only workloads in YCSB benchmarks, compared with state-of-the-art B+ trees on DM.

In-memory caching systems are fundamental building blocks in cloud services. However, due to the coupled CPU and memory on monolithic servers, existing caching systems cannot elastically adjust resources in a resource-efficient and agile manner. To achieve better elasticity, we propose to port in-memory caching systems to the disaggregated memory (DM) architecture, where compute and memory resources are decoupled and can be allocated flexibly. However, constructing an elastic caching system on DM is challenging since accessing cached objects with CPU-bypass remote memory accesses hinders the execution of caching algorithms. Moreover, the elastic changes of compute and memory resources on DM affect the access patterns of cached data, compromising the hit rates of caching algorithms. We design Ditto, the first caching system on DM, to address these challenges. Ditto first proposes a client-centric caching framework to efficiently execute various caching algorithms in the compute pool of DM, relying only on remote memory accesses. Then, Ditto employs a distributed adaptive caching scheme that adaptively switches to the best-fit caching algorithm in real-time based on the performance of multiple caching algorithms to improve cache hit rates. Our experiments show that Ditto effectively adapts to the changing resources on DM and outperforms the state-of-the-art caching systems by up to 3.6× in real-world workloads and 9× in YCSB benchmarks.

Deep learning (DL) techniques are proven effective in many challenging tasks, and become widely-adopted in practice. However, previous work has shown that DL libraries, the basis of building and executing DL models, contain bugs and can cause severe consequences. Unfortunately, existing testing approaches still cannot comprehensively exercise DL libraries. They utilize existing trained models and only detect bugs in model inference phase. In this work we propose Muffin to address these issues. To this end, Muffin applies a specifically-designed model fuzzing approach, which allows it to generate diverse DL models to explore the target library, instead of relying only on existing trained models. Muffin makes differential testing feasible in the model training phase by tailoring a set of metrics to measure the inconsistencies between different DL libraries. In this way, Muffin can best exercise the library code to detect more bugs. To evaluate the effectiveness of Muffin, we conduct experiments on three widely-used DL libraries. The results demonstrate that Muffin can detect 39 new bugs in the latest release versions of popular DL libraries, including Tensorflow, CNTK, and Theano.