Adam Manzanares

MINT: A Reliability Modeling Frameworkfor Energy-Efficient Parallel Disk Systems

The Popular Disk Concentration (PDC) technique and the Massive Array of Idle Disks (MAID) technique are two effective energy conservation schemes for parallel disk systems. The goal of PDC and MAID is to skew I/O load toward a few disks so that other disks can be transitioned to low power states to conserve energy. I/O load skewing techniques like PDC and MAID inherently affect reliability of parallel disks, because disks storing popular data tend to have high failure rates than disks storing cold data. To study reliability impacts of energy-saving techniques on parallel disk systems, we develop a mathematical modeling framework called MINT. We first model the behaviors of parallel disks coupled with power management optimization policies. We make use of data access patterns as input parameters to estimate each disks utilization and power-state transitions. Then, we derive each disks reliability in terms of annual failure rate from the disks utilization, age, operating temperature, and power-state transition frequency. Next, we calculate the reliability of PDC and MAID parallel disk systems in accordance with the annual failure rate of each disk in the systems. Finally, we use real-world trace to validate out MINT model. Validation result shows that the behaviors of PDC and MAID which are modeled by MINT have a similar trend as that in the real-world.

DataMods: Programmable File System Services

As applications become more complex, and the level of concurrency in systems continue to rise, developers are struggling to scale complex data models on top of a traditional byte stream interface. Middleware tailored for specific data models is a common approach to dealing with these challenges, but middleware commonly reproduces scalable services already present in many distributed file systems. We present DataMods, an abstraction over existing services found in large-scale storage systems that allows middleware to take advantage of existing, highly tuned services. Specifically, DataMods provides an abstraction for extending storage system services in order to implement native, domain-specific data models and interfaces throughout the storage hierarchy.

In-vivo Storage System Development

The emergence of high-performance open-source storage systems is allowing application and middleware developers to consider non-standard storage system interfaces. In contrast to the practice of virtually always designing for file-like byte-stream interfaces, co-designed domain-specific storage system interfaces are becoming increasingly common. However, in order for developers to evolve interfaces in high-availability storage systems, services are needed for in-vivo interface evolution that allows the development of interfaces in the context of a live system. Current clustered storage systems that provide interface customizability expose primitive services for managing ad-hoc interfaces. For maximum utility, the ability to create, evolve, and deploy dynamic storage interfaces is needed. However, in large-scale clusters, dynamic interface instantiation will require system-level support that ensures interface version consistency among storage nodes and client applications. We propose that storage systems should provide services that fully manage the life-cycle of dynamic interfaces that are aligned with the common branch-and-merge form of software maintenance, including isolated development workspaces that can be combined into existing production views of the system.