Scalable Concurrency Control and Recovery for Shared Storage Arrays

We are indebted to generous contributions from the member companies of the Parallel Data Consortium. At the time of this writing, these companies include Hewlett-Abstract Shared storage arrays enable thousands of storage devices to be shared and directly accessed by end hosts over switched system-area networks, promising databases and filesystems highly scalable, reliable storage. In such systems, however, concurrent host I/Os can span multiple shared devices and access overlapping ranges potentially leading to inconsistencies for redundancy codes and for data read by end hosts. In order to enable existing applications to run unmodified and simplify the development of future ones, we desire a shared storage array to provide the illusion of a single controller without the scalability bottleneck and single point of failure of an actual single controller. In this paper, we show how rapidly increasing storage device intelligence coupled with storage's special characteristics can be successfully exploited to arrive at a high performance solution to this storage management problem. In particular, we examine four concurrency control schemes and specialize them to shared storage arrays; two centralized ones: simple server locking, and server locking with leased callbacks; and two distributed ones based on device participation: distributed locking using storage-device-embedded lock servers and timestamp ordering using loosely synchronized clocks. Simulation results show that both centralized locking schemes suffer from scalability limitations. Moreover, callback locking is particularly suspect if applications do not have much inherent locality and if the storage system introduces false sharing. Distributed concurrency control with device support is attractive as it scales control capacity with storage and performance capacity and offers the opportunity to piggyback lock/ordering messages on operation requests, eliminating message latency costs. Simulations show that both storage-optimized device-based protocols exhibit close to ideal scaling achieving 90-95% of the throughput possible under totally unprotected operation. Furthermore, timestamp ordering uses less network resources, is free from deadlocks and has performance advantages under high load. We show how timestamp ordering can be extended with careful operation history recording to ensure efficient failure recovery without inducing I/Os under normal operation. This brings the overhead of concurrency control and recovery to a negligible few percent thereby realizing the scalability potential of the shared array I/O architecture. Traditional I/O subsystems use a centralized component to coordinate access to storage when the system includes multiple storage devices (Figure 1(a)). The data may be striped across the devices or stored redundantly such that a single …