Parallel Deferred Update Replication

Deferred update replication (DUR) is an established approach to implementing highly efficient and available storage. While the throughput of read-only transactions scales linearly with the number of deployed replicas in DUR, the throughput of update transactions experiences limited improvements as replicas are added. This paper presents Parallel Deferred Update Replication (P-DUR), a variation of classical DUR that scales both read-only and update transactions with the number of cores available in a replica. In addition to introducing the new approach, we describe its full implementation and compare its performance to classical DUR and to Berkeley DB, a well-known standalone database

The Effects of Parallel Processing on Update Response Time in Distributed Database Design

Network latency and local update are the most significant components of update response time in a distributed database system. Effectively designed distributed database systems can take advantage of parallel processing to minimize this time. We present a design approach to response time minimization for update transactions in a distributed database. Response time is calculated as the sum of local processing and communication, including transmit time, queuing delays, and network latency. We demonstrate that parallelism has significant impacts on the efficiency of data allocation strategies in the design of high transaction-volume distributed databases

Performance Evaluation of Global Reading of Entire Databases

Using simulation and probabilistic analysis, we study the performance of an algorithm to read entire databases with locking concurrency control allowing multiple readers or an exclusive writer. The algorithm runs concurrently with the normal transaction processing (on-the-fly) and locks the entities in the database one by one (incremental). The analysis compares different strategies to resolve the conflicts between the global read algorithm and update. Since the algorithm is parallel in nature, its interference with normal transactions is minimized in parallel and distributed databases. A simulation study shows that one variant of the algorithm can read the entire database with very little overhead and interference with the updates

Parallel methods for the update of partitioned inverted files

Purpose – An issue which tends to be ignored in information retrieval is the issue of updating inverted files. This is largely because inverted files were devised to provide fast query service, and much work has been done with the emphasis strongly on queries. In this paper we study the effect of using parallel methods for the update of inverted files in order to reduce costs, by looking at two types of partitioning for inverted files: document identifier and term identifier. Design/methodology/approach – Raw update service and update with query service are studied with these partitioning schemes using an incremental update strategy. We use standard measures used in parallel computing such as speedup to examine the computing results and also the costs of reorganising indexes while servicing transactions. Findings – Empirical results show that for both transaction processing and index reorganisation the document identifier method is superior. However, there is evidence that the term identifier partitioning method could be useful in a concurrent transaction processing context. Practical implications – There is an increasing need to service updates which is now becoming a requirement of inverted files (for dynamic collections such as the Web), demonstrating that a shift in requirements of inverted file maintenance is needed from the past. Originality/value – The paper is of value to database administrators who manage large-scale and dynamic text collections, and who need to use parallel computing to implement their text retrieval services

Checking integrity constraints - how it differs in centralized, distributed and parallel databases

An important aim of a database system is to guarantee database consistency, which means that the data contained in a database is both accurate and valid. Integrity constraints represent knowledge about data with which a database must be consistent. The process of checking constraints to ensure that update operations or transactions which alter the database will preserve its consistency has proved to be extremely difficult to implement, particularly in distributed and parallel databases. In distributed databases the aim of the constraint checking is to reduce the amount of data needing to be accessed, the number of sites involved and the amount of data transferred across the network. In parallel databases the focus is on the total execution time taken in checking the constraints. This paper highlights the differences between centralized, distributed and parallel databases with respect to constraint checking

Distributed Power Allocation with Rate Constraints in Gaussian Parallel Interference Channels

This paper considers the minimization of transmit power in Gaussian parallel interference channels, subject to a rate constraint for each user. To derive decentralized solutions that do not require any cooperation among the users, we formulate this power control problem as a (generalized) Nash equilibrium game. We obtain sufficient conditions that guarantee the existence and nonemptiness of the solution set to our problem. Then, to compute the solutions of the game, we propose two distributed algorithms based on the single user waterfilling solution: The \emph{sequential} and the \emph{simultaneous} iterative waterfilling algorithms, wherein the users update their own strategies sequentially and simultaneously, respectively. We derive a unified set of sufficient conditions that guarantee the uniqueness of the solution and global convergence of both algorithms. Our results are applicable to all practical distributed multipoint-to-multipoint interference systems, either wired or wireless, where a quality of service in terms of information rate must be guaranteed for each link.Comment: Paper submitted to IEEE Transactions on Information Theory, February 17, 2007. Revised January 11, 200

Rules for deriving efficient independent transaction

A transaction is a collection of operations that performs a single logical function in a database application. Each transaction is a unit of both atomicity and consistency. Thus, transactions are required not to violate any database consistency constraints. In most cases, the update operations in a transaction are executed sequentially. The effect of a single operation in a transaction potentially may be changed by another operation in the same transaction. This implies that the sequential execution sometimes does some redundant work. A transaction with a set of update operations is order dependent if and only if the execution of the transaction following the serialibility order as in the transaction produce an output which will be different than the output produced by interchanging the operations in the transaction. In [8], a transaction is an order dependent transaction if and only if it contains at least two conflicting update operations, i.e. updates that operate on the same data item. In our work, we have identified that there are cases where an update operation operates on a data item that is part of a set of data items operate by the other update operation. Such transaction is known as partly order dependent transaction. An order independent transaction has an important advantage of its update statements being executed in parallel without considering their relative execution orders. With an order independent transaction we can consider its single updates in an arbitrary order. Furthermore, executing these update operations in parallel can reduce the execution time. In this paper, we present rules that can be applied to generate order independent transaction given an order dependent or partly order dependent transaction. In addition, we have identified several rules that can be applied to eliminate redundant and subsumed operations that can incur unnecessary execution cost

PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication

Geo-replicated data platforms are at the backbone of several large-scale online services. Transactional Causal Consistency (TCC) is an attractive consistency level for building such platforms. TCC avoids many anomalies of eventual consistency, eschews the synchronization costs of strong consistency, and supports interactive read-write transactions. Partial replication is another attractive design choice for building geo-replicated platforms, as it increases the storage capacity and reduces update propagation costs. This paper presents PaRiS, the first TCC system that supports partial replication and implements non-blocking parallel read operations, whose latency is paramount for the performance of read-intensive applications. PaRiS relies on a novel protocol to track dependencies, called Universal Stable Time (UST). By means of a lightweight background gossip process, UST identifies a snapshot of the data that has been installed by every DC in the system. Hence, transactions can consistently read from such a snapshot on any server in any replication site without having to block. Moreover, PaRiS requires only one timestamp to track dependencies and define transactional snapshots, thereby achieving resource efficiency and scalability. We evaluate PaRiS on a large-scale AWS deployment composed of up to 10 replication sites. We show that PaRiS scales well with the number of DCs and partitions, while being able to handle larger data-sets than existing solutions that assume full replication. We also demonstrate a performance gain of non-blocking reads vs. a blocking alternative (up to 1.47x higher throughput with 5.91x lower latency for read-dominated workloads and up to 1.46x higher throughput with 20.56x lower latency for write-heavy workloads)