Murthy V. Devarakonda

Data cache management using frequency-based replacement

We propose a new frequency-based replacement algorithm for managing caches used for disk blocks by a file system, database management system, or disk control unit, which we refer to here as data caches. Previously, LRU replacement has usually been used for such caches. We describe a replacement algorithm based on the concept of maintaining reference counts in which locality has been “factored out”. In this algorithm replacement choices are made using a combination of reference frequency and block age. Simulation results based on traces of file system and I/O activity from actual systems show that this algorithm can offer up to 34% performance improvement over LRU replacement, where the improvement is expressed as the fraction of the performance gain achieved between LRU replacement and the theoretically optimal policy in which the reference string must be known in advance. Furthermore, the implementation complexity and efficiency of this algorithm is comparable to one using LRU replacement.

Prediction-based dynamic load-sharing heuristics

Presents dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30% better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50% improvements relative to their nonpredictive counterpart. >

Recovery in the Calypso file system

This article presents the deign and implementation of the recovery scheme in Calypso. Calypso is a cluster-optimized, distributed file system for UNIX clusters. As in Sprite and AFS, Calypso servers are stateful and scale well to a large number of clients. The recovery scheme in Calypso is nondisruptive, meaning that open files remain open, client modified data are saved, and in-flight operations are properly handled across server recover. The scheme uses distributed state amount the clients to reconstruct the server state on a backup node if disks are multiported or on the rebooted server node. It guarantees data consistency during recovery and provides congestion control. Measurements show that the state reconstruction can be quite fast: for example, in a 32-node cluster, when an average node contains state for about 420 files, the reconstruction time is about 3.3 seconds. However, the time to update a file system after a failure can be a major factor in the overall recovery time, even when using journaling techniques.

Galapagos: model-driven discovery of end-to-end application-storage relationships in distributed systems

Modern business information systems are typically multitiered distributed systems comprising Web services, application services, databases, enterprise information systems, file systems, storage controllers, and other storage systems. In such environments, data is stored in different forms at multiple tiers, with each tier associated with some level of data abstraction. An information entity owned by an application generally maps to several data entities, logically associated across tiers and related to the application. Discovery of such relationships in a distributed system is a challenging problem, complicated by the widespread adoption of virtualization technologies and by the traditional tendency to manage each tier as an independent domain. In this paper, we present a system and methodology for model-driven discovery of end-to-end application-data relationships spanning multiple tiers, from the applications to the lowest levels of the storage hierarchy. The key to our methodology involves modeling how data is used and transformed by distributed software components. An important benefit of our system, which we call Galapagos, is the ability to reflect business decisions expressed at the application level to the level of storage.

Policy-based information lifecycle management in a large-scale file system

Policy-based file lifecycle management is important for balancing storage utilization and for regulatory conformance. It poses two important challenges, the need for simple yet effective policy design and an implementation that scales to billions of files. This paper describes the design and an innovative implementation technique of policy-based lifecycle management in a prototype built as a part of IBMs new SAN file system. The policy specification leverages a key abstraction in the file system called storage pools and its ability to support location independence for files. The policy implementation uses an innovative new technique that combines concurrent policy execution and a policy decisions cache, to enable scaling to billions of files under normal usage patterns.

An architecture for lifecycle management in very large file systems

We present a policy-based architecture STEPS for lifecycle management (LCM) in a mass scale distributed file system. The STEPS architecture is designed in the context of IBMs SAN file system (SFS) and leverages the parallelism and scalability offered by SFS, while providing a centralized point of control for policy-based management. The architecture uses novel concepts like policy cache and rate-controlled migration for efficient and non-intrusive execution of the LCM functions, while ensuring that the architecture scales with very large number of files. The architecture has been implemented and used for lifecycle management in a distributed deployment of SFS with heterogeneous data. We conduct experiments on the implementation to study the performance of the architecture. We observed that STEPS is highly scalable with increase in the number as well as the size of the file objects hosted by SFS. The performance study also demonstrated that most of the efficiency of policy execution is derived from policy cache. Further, a rate-control mechanism is necessary to ensure that users are isolated from LCM operations.

Distributed token management in Calypso file system

This paper presents the design and implementation of a distributed token manager for a cluster-optimized, distributed Unix file system. In this file system, tokens provide cache consistency and support for single-system Unix semantics. The paper describes the token types used, token arbitration protocol, deadlock-free implementation, fault-tolerance, and recovery. The key contributions of the work reported here are: (1) An efficient protocol for token arbitration, which minimizes bottlenecks and hence enhances scalability; (2) A practical approach to handling deadlocks, race conditions, and recovery issues, which complicate token manager design and implementation.<<ETX>>

An essential design pattern for fault-tolerant distributed state sharing

vpvpv pvpvp vpvpv pvpvp vpvpv pvpvp vpvpv pvpvp vpvpv pvpvp N a y e e m I s l a m a n d M u r t h y D e v a r a k o n d a An EssentialDesign Pattern for Fault-Tolerant Distributed State Sharing Because developers of distributed programs focus on performance and fault tolerance, distributed design patterns should highlight these aspects of their solutions. One such OO design pattern—the Recoverable Distributor—allows mixing and matching of fault-tolerance and data-consistency protocols and has been used in a distributed scheduler and a distributed lock manager.

Policy-Based Autonomic Storage Allocation

The goal of autonomic storage allocation is to achieve management of storage resources, including allocation, performance monitoring, and hotspot elimination, by specifying comparatively high-level goals, rather than by means of low-level manual steps. The process of automation should allow specification of policies as administrator specified constraints under which the resources are managed. This paper describes the system design and implementation experiences from a prototype autonomic storage manager being developed in IBM Research. The prototype is being developed for a storage network that includes a SAN switch, an IBM Enterprise Storage Subsystem, and AIX servers. Our early experience from this prototype implementation is that there are a large number of mundane manual steps in storage management and it is feasible to automate them such that the automation is driven by higher-level goals under policy control. However, to manage heterogeneous storage a standard ontology is needed for specification of goals and how to achieve them.

Application-storage discovery

Discovering application dependency on data and storage is a key prerequisite for many storage optimization tasks such as data assignment to storage tiers, storage consolidation, virtualization, and handling unused data. However, in the real world these dependencies are rarely known, and discovering them is a challenge because of virtualization at various levels and the need for discovery methods to be non-intrusive. As a result, many optimization tasks are performed, if at all, without the full knowledge of application-to-storage dependencies. This paper presents a non-intrusive application-to-storage discovery method, and while it is built on our prior work, the storage discovery described here is entirely new. We used this discovery method in two production enterprise environments, consisting of about 323 servers, and we show how the discovered data enables three optimization tasks. First, we relate application criticality with storage tiers. Second, we find unused storage devices and we show how this information together with storage consolidation can be used to achieve power savings of up to two orders of magnitude. Third, we identify opportunities for database storage optimization.