In this paper we introduce SpecRPC, a speculative execution framework that allows applications to concurrently execute dependent operations both locally and through remote procedure calls. The framework tracks dependencies among non-speculative and speculative operations and ensures that incorrect speculations do not affect the correctness of applications that follow our suggested design pattern. By using speculation to parallelize dependent operations, SpecRPC can significantly reduce application latency even if only a fraction of the results can be correctly speculated. We evaluate SpecRPC by using it to implement Replicated Commit, a low-latency distributed transaction commit protocol for geo-replicated database systems. Our evaluation results show that, compared to RPC frameworks that sequentially execute dependent operations, SpecRPC can reduce the average transaction completion time of Replicated Commit by 58%.

Video streaming applications generate a large fraction of Internet traffic. Much of this content is delivered over HTTP using standard web servers. Unlike other types of web workloads, HTTP video streaming workloads are typically disk bound, and therefore an important problem is that of optimizing disk access.

Key-value storage systems are an integral part of many data centre applications, but as demand increases so does the need for high performance. This has motivated new designs that use Remote Direct Memory Access (RDMA) to reduce communication overhead. Current RDMA-enabled key-value stores (RKVSes) target workloads involving small values, running on dedicated servers on which no other applications are running. Outside of these domains, however, there may be other RKVS designs that provide better performance. In this paper, we introduce Nessie, an RKVS that is fully client-driven, meaning no server process is involved in servicing requests. Nessie also decouples its index and storage data structures, allowing indices and data to be placed on different servers. This flexibility can decrease the number of network operations required to service a request. These design elements make Nessie well-suited for a different set of workloads than existing RKVSes. Compared to a server-driven RKVS, Nessie more than doubles system throughput when there is CPU contention on the server, improves throughput by 70 percent for PUT-oriented workloads when data value sizes are 128 KB or larger, and reduces power consumption by 18 percent at 80 percent system utilization and 41 percent at 20 percent system utilization compared with idle power consumption.

Past work has shown that disk prefetching can be an effective technique for improving the performance of disk bound workloads. However, the performance gains are highly dependent on selecting a prefetch size that is appropriate for a specific system and workload. Using a prefetch size that is too small can lead to poor overall disk throughput, whereas prefetch sizes that are too large can lead to data being evicted before it can be used by a subsequent request.