When a working set fits into memory, the overhead imposed by the buffer pool renders traditional databases non-competitive with in-memory designs that sacrifice the benefits of a buffer pool. However, despite the large memory available with modern hardware, data skew, shifting workloads, and complex mixed workloads make it difficult to guarantee that a working set will fit in memory. Hence, some recent work has focused on enabling in-memory databases to protect performance when the working data set almost fits in memory. Contrary to those prior efforts, we enable buffer pool designs to match in-memory performance while supporting the "big data" workloads that continue to require secondary storage, thus providing the best of both worlds. We introduce here a novel buffer pool design that adapts pointer swizzling for references between system objects (as opposed to application objects), and uses it to practically eliminate buffer pool overheads for memoryresident data. Our implementation and experimental evaluation demonstrate that we achieve graceful performance degradation when the working set grows to exceed the buffer pool size, and graceful improvement when the working set shrinks towards and below the memory and buffer pool sizes.

In databases with a large buffer pool, a transaction may run in less time than it takes to log the transaction's commit record on stable storage. Such cases motivate a technique called early lock release: immediately after appending its commit record to the log buffer in memory, a transaction may release its locks. Thus, it cuts overall lock duration to a fraction and reduces lock contention accordingly.

The LazyBase scalable database system is specialized for the growing class of data analysis applications that extract knowledge from large, rapidly changing data sets. It provides the scalability of popular NoSQL systems without the query-time complexity associated with their eventual consistency models, offering a clear consistency model and explicit per-query control over the trade-off between latency and result freshness. With an architecture designed around batching and pipelining of updates, LazyBase simultaneously ingests atomic batches of updates at a very high throughput and offers quick read queries to a stale-but-consistent version of the data. Although slightly stale results are sufficient for many analysis queries, fully up-to-date results can be obtained when necessary by also scanning updates still in the pipeline. Compared to the Cassandra NoSQL system, LazyBase provides 4X--5X faster update throughput and 4X faster read query throughput for range queries while remaining competitive for point queries. We demonstrate LazyBase's tradeoff between query latency and result freshness as well as the benefits of its consistency model. We also demonstrate specific cases where Cassandra's consistency model is weaker than LazyBase's.

For implementing content management solutions and enabling new applications associated with data retention, regulatory compliance, and litigation issues, enterprises need to develop advanced analytics to uncover relationships among the documents, e.g., content similarity, provenance, and clustering. In this paper, we evaluate the performance of four syntactic similarity algorithms. Three algorithms are based on Broder's "shingling" technique while the fourth algorithm employs a more recent approach, "content-based chunking". For our experiments, we use a specially designed corpus of documents that includes a set of "similar" documents with a controlled number of modifications. Our performance study reveals that the similarity metric of all four algorithms is highly sensitive to settings of the algorithms' parameters: sliding window size and fingerprint sampling frequency. We identify a useful range of these parameters for achieving good practical results, and compare the performance of the four algorithms in a controlled environment. We validate our results by applying these algorithms to finding near-duplicates in two large collections of HP technical support documents.

We propose a new paradigm for building scalable distributed systems. Our approach does not require dealing with message-passing protocols, a major complication in existing distributed systems. Instead, developers just design and manipulate data structures within our service called Sinfonia. Sinfonia keeps data for applications on a set of memory nodes, each exporting a linear address space. At the core of Sinfonia is a new minitransaction primitive that enables efficient and consistent access to data, while hiding the complexities that arise from concurrency and failures. Using Sinfonia, we implemented two very different and complex applications in a few months: a cluster file system and a group communication service. Our implementations perform well and scale to hundreds of machines.

We present Confluence, an enhancement to a desktop file search tool called Confluence which extracts conceptual relationships between files by their temporal access patterns in the file system. A limitation of a purely file-based approach is that as file operations are increasingly abstracted by applications, their correlation to a user's activity weakens and thereby reduces the applicability of their temporal patterns. To deal with this problem, we augment the file event stream with a stream of window focus events from the UI layer. We present 3 algorithms that analyze this new stream, extracting the user's task information which informs the existing Confluence algorithms. We present results and conclusions from a preliminary user study on Confluence.

We propose a new paradigm for building scalable distributed systems. Our approach does not require dealing with message-passing protocols -- a major complication in existing distributed systems. Instead, developers just design and manipulate data structures within our service called Sinfonia. Sinfonia keeps data for applications on a set of memory nodes, each exporting a linear address space. At the core of Sinfonia is a novel minitransaction primitive that enables efficient and consistent access to data, while hiding the complexities that arise from concurrency and failures. Using Sinfonia, we implemented two very different and complex applications in a few months: a cluster file system and a group communication service. Our implementations perform well and scale to hundreds of machines.

This paper describes the design, implementation, and evaluation of a Federated Array of Bricks (FAB), a distributed disk array that provides the reliability of traditional enterprise arrays with lower cost and better scalability. FAB is built from a collection of bricks, small storage appliances containing commodity disks, CPU, NVRAM, and network interface cards. FAB deploys a new majority-voting-based algorithm to replicate or erasure-code logical blocks across bricks and a reconfiguration algorithm to move data in the background when bricks are added or decommissioned. We argue that voting is practical and necessary for reliable, high-throughput storage systems such as FAB. We have implemented a FAB prototype on a 22-node Linux cluster. This prototype sustains 85MB/second of throughput for a database workload, and 270MB/second for a bulk-read workload. In addition, it can outperform traditional master-slave replication through performance decoupling and can handle brick failures and recoveries smoothly without disturbing client requests.

A federated array of bricks is a scalable distributed storage system composed from inexpensive storage bricks. It achieves high reliability with low cost by using erasure coding across the bricks to maintain data reliability in the face of brick failures. Erasure coding generates n encoded blocks from m data blocks (n > m) and permits the data blocks to be reconstructed from any m of these encoded blocks. We present a new fully decentralized erasure-coding algorithm for an asynchronous distributed system. Our algorithm provides fully linearizable read-write access to erasure-coded data and supports concurrent I/O controllers that may crash and recover. Our algorithm relies on a novel quorum construction where any two quorums intersect in m processes.

As users and companies increasingly depend on shared, networked information services, we continue to see growth in data centers and service providers. This happens as services and servers are consolidated (for ease of management and reduced duplication), while also being distributed (for fault tolerance and to accommodate the global reach of customers). Since access to data is the lifeblood of any organization, a global storage system is a core element in such an infrastructure. Based on success in automatically managing local storage, we believe that the key attribute of such a system is the ability to flexibly adapt to a variety of application semantics and requirements as they arise and as they change over time. Our work has shown that it is possible to automatically design and configure a storage system of one or more disk arrays to meet a set of application requirements and to dynamically reconfigure as needs change, all without human intervention. Work on global data placement expands the scope of this system to a world of distributed data centers.

Enterprise-scale storage systems, which can contain hundreds of host computers and storage devices and up to tens of thousands of disks and logical volumes, are difficult to design. The volume of choices that need to be made is massive, and many choices have unforeseen interactions. Storage system design is tedious and complicated to do by hand, usually leading to solutions that are grossly over-provisioned, substantially under-performing or, in the worst case, both.To solve the configuration nightmare, we present minerva: a suite of tools for designing storage systems automatically. Minerva uses declarative specifications of application requirements and device capabilities; constraint-based formulations of the various sub-problems; and optimization techniques to explore the search space of possible solutions.This paper also explores and evaluates the design decisions that went into Minerva, using specialized micro- and macro-benchmarks. We show that Minerva can successfully handle a workload with substantial complexity (a decision-support database benchmark). Minerva created a 16-disk design in only a few minutes that achieved the same performance as a 30-disk system manually designed by human experts. Of equal importance, Minerva was able to predict the resulting system's performance before it was built.

Modern file systems associate the deletion of a file with the immediate release of storage, and file writes with the irrevocable change of file contents. We argue that this behavior is a relic of the past, when disk storage was a scarce resource. Today, large cheap disks make it possible for the file system to protect valuable data from accidental delete or overwrite.This paper describes the design, implementation, and performance of the Elephant file system, which automatically retains all important versions of user files. Users name previous file versions by combining a traditional pathname with a time when the desired version of a file or directory existed. Storage in Elephant is managed by the system using file-grain user-specified retention policies. This approach contrasts with checkpointing file systems such as Plan-9, AFS, and WAFL that periodically generate efficient checkpoints of entire file systems and thus restrict retention to be guided by a single policy for all files within that file system.Elephant is implemented as a new Virtual File System in the FreeBSD kernel.

September 01 1991 A Modified Quickprop Algorithm Alistair Craig Veitch, Alistair Craig Veitch Department of Computer Science, University of Waikato, New Zealand Search for other works by this author on: This Site Google Scholar Geoffrey Holmes Geoffrey Holmes Department of Computer Science, University of Waikato, New Zealand Search for other works by this author on: This Site Google Scholar Author and Article Information Alistair Craig Veitch Department of Computer Science, University of Waikato, New Zealand Geoffrey Holmes Department of Computer Science, University of Waikato, New Zealand Received: February 11 1991 Accepted: March 04 1991 Online Issn: 1530-888X Print Issn: 0899-7667 © 1991 Massachusetts Institute of Technology1991 Neural Computation (1991) 3 (3): 310–311. https://doi.org/10.1162/neco.1991.3.3.310 Article history Received: February 11 1991 Accepted: March 04 1991 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Alistair Craig Veitch, Geoffrey Holmes; A Modified Quickprop Algorithm. Neural Comput 1991; 3 (3): 310–311. doi: https://doi.org/10.1162/neco.1991.3.3.310 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll JournalsNeural Computation Search Advanced Search This content is only available as a PDF. © 1991 Massachusetts Institute of Technology1991 Article PDF first page preview Close Modal You do not currently have access to this content.