RAMCloud is a DRAM-based storage system that provides inexpensive durability and availability by recovering quickly after crashes, rather than storing replicas in DRAM. RAMCloud scatters backup data across hundreds or thousands of disks, and it harnesses hundreds of servers in parallel to reconstruct lost data. The system uses a log-structured approach for… (More)
Raft is a consensus algorithm for managing a replicated log. It produces a result equivalent to (multi-)Paxos, and it is as efficient as Paxos, but its structure is different from Paxos; this makes Raft more understandable than Paxos and also provides a better foundation for building practical systems. In order to enhance understandabil-ity, Raft separates… (More)
This paper explores the relationship between domain scheduling in avirtual machine monitor (VMM) and I/O performance. Traditionally, VMM schedulers have focused on fairly sharing the processor resources among domains while leaving the scheduling of I/O resources as asecondary concern. However, this can resultin poor and/or unpredictable application… (More)
RAMCloud is a storage system that provides low-latency access to large-scale datasets. To achieve low latency, RAMCloud stores all data in DRAM at all times. To support large capacities (1PB or more), it aggregates the memories of thousands of servers into a single coherent key-value store. RAMCloud ensures the durability of DRAM-based data by keeping… (More)
With scalable high-performance storage entirely in DRAM, RAMCloud will enable a new breed of data-intensive applications.
The operating systems community has ignored network latency for too long. In the past, speed-of-light delays in wide area networks and unoptimized network hardware have made sub-100µs round-trip times impossible. However, in the next few years datacenters will be deployed with low-latency Ethernet. Without the burden of propagation delays in the datacenter… (More)
Research Interests Large-scale software systems and database systems, low-latency in-memory database systems.