Ralph A. Becker-Szendy

Zygaria: Storage Performance as a Managed Resource

Large-scale storage systems often hold data for multiple applications and users. A problem in such systems is isolating applications and users from each other to prevent their workloads from interacting in unexpected ways. Another is ensuring that each application receives an appropriate level of performance. As part of the solution to these problems, we have designed a hierarchical I/O scheduling algorithm to manage performance resources on an underlying storage device. Our algorithm uses a simple allocation abstraction: an application or user has a corresponding pool of throughput, and manages throughput within its pool by opening sessions. The algorithm ensures that each pool and session receives at least a reserve rate of throughput and caps usage at a limit rate, using hierarchical token buckets and EDF I/O scheduling. Once it has fulfilled the reserves of all active sessions and pools, it shares unused throughput fairly among active sessions and pools such that they tend to receive the same amount. It thus combines deadline scheduling with proportional-style resource sharing in a novel way. We assume that the device performs its own low-level head scheduling, rather than modeling the device in detail. Our implementation shows the correctness of our algorithm, imposes little overhead on the system, and achieves throughput nearly equal to that of an unmanaged device.

Quota enforcement for high-performance distributed storage systems

Storage systems manage quotas to ensure that no one user can use more than their share of storage, and that each user gets the storage they need. This is difficult for large, distributed systems, especially those used for high- performance computing applications, because resource allocation occurs on many nodes concurrently. While quota management is an important problem, no robust scalable solutions have been proposed to date. We present a solution that has less than 0.2% performance overhead while the system is below saturation, compared with not enforcing quota at all. It provides byte-level accuracy at all times, in the absence of failures and cheating. If nodes fail or cheat, we recover within a bounded period. In our scheme quota is enforced asynchronously by intelligent storage servers: storage clients contact a shared management service to obtain vouchers, which the clients can spend like cash at participating storage servers to allocate storage space. Like a digital cash system, the system periodically reconciles voucher usage to ensure that clients do not cheat by spending the same voucher at multiple storage servers. We report on a simulation study that validates this approach and evaluates its performance.