A Comparative Study of Divergence Control Algorithms

This paper evaluates and compares the performance of two-phase locking divergence control (2PLDC) and optimistic divergence control (ODC) algorithms using a comprehensive centralized database simulation model. We examine a system with multiclass workloads in which on-line update transactions and long-duration queries progress based on epsilon serializability (ESR). Our results demonstrate that significant performance enhancements can be achieved with a non-zero tolerable inconsistency (ϵ-spec). With sufficient ϵ-spec and limited system resources, both algorithms achieve comparable performance. However, with low resource contention, ODC performs significantly better than 2PLDC. Moreover, given a small ϵ-spec, ODC returns more accurate results on the committed queries then 2PLDC

A Formal Characterization of Epsilon Serializability

Epsilon Serializability (ESR) is a generalization of classic serializability (SR). ESR allows some limited amount of inconsistency in transaction processing (TP), through an interface called epsilon-transactions (ETs). For example, some query ETs may view inconsistent data due to non-SR interleaving with concurrent updates. In this paper, we restrict our attention to the situation where query-only ETs run concurrently with consistent update transactions that are SR without the ETs. This paper presents a formal characterization of ESR and ETs. Using the ACTA framework, the first part of this characterization formally expresses the inter-transaction conflicts that are recognized by ESR and, through that, defines ESR, analogous to the manner in which conflict-based serializability is defined. The second part of the paper is devoted to deriving expressions for: (1) the inconsistency in the values of data -- arising from ongoing updates, (2) the inconsistency of the results of a query ““ arising from the inconsistency of the data read in order to process the query, and (3) the inconsistency exported by an update ET - arising from ongoing queries reading uncommitted data produced by the update ET. These expressions are used to determine the preconditions that ET operations have to satisfy in order to maintain the limits on the inconsistency in the data read by query ETs, the inconsistency exported by update ETs, and the inconsistency in the results of queries. This determination suggests possible mechanisms that can be used to realize ESR

Execution Autonomy in Distributed Transaction Processing

We study the feasibility of execution autonomy in systems with asynchronous transaction processing based on epsilon-serializability (ESR). The abstract correctness criteria defined by ESR are implemented by techniques such as asynchronous divergence control and asynchronous consistency restoration. Concrete application examples in a distributed environment, such as banking, are described in order to illustrate the advantages of using ESR to support execution autonomy

The Homeostasis Protocol: Avoiding Transaction Coordination Through Program Analysis

Datastores today rely on distribution and replication to achieve improved performance and fault-tolerance. But correctness of many applications depends on strong consistency properties - something that can impose substantial overheads, since it requires coordinating the behavior of multiple nodes. This paper describes a new approach to achieving strong consistency in distributed systems while minimizing communication between nodes. The key insight is to allow the state of the system to be inconsistent during execution, as long as this inconsistency is bounded and does not affect transaction correctness. In contrast to previous work, our approach uses program analysis to extract semantic information about permissible levels of inconsistency and is fully automated. We then employ a novel homeostasis protocol to allow sites to operate independently, without communicating, as long as any inconsistency is governed by appropriate treaties between the nodes. We discuss mechanisms for optimizing treaties based on workload characteristics to minimize communication, as well as a prototype implementation and experiments that demonstrate the benefits of our approach on common transactional benchmarks

Performance characteristics of semantics-based concurrency control protocols.

by Keith, Hang-kwong Mak.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 122-127).Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 2 --- Background --- p.4Chapter 2.1 --- Read/Write Model --- p.4Chapter 2.2 --- Abstract Data Type Model --- p.5Chapter 2.3 --- Overview of Semantics-Based Concurrency Control Protocols --- p.7Chapter 2.4 --- Concurrency Hierarchy --- p.9Chapter 2.5 --- Control Flow of the Strict Two Phase Locking Protocol --- p.11Chapter 2.5.1 --- Flow of an Operation --- p.12Chapter 2.5.2 --- Response Time of a Transaction --- p.13Chapter 2.5.3 --- Factors Affecting the Response Time of a Transaction --- p.14Chapter 3 --- Semantics-Based Concurrency Control Protocols --- p.16Chapter 3.1 --- Strict Two Phase Locking --- p.16Chapter 3.2 --- Conflict Relations --- p.17Chapter 3.2.1 --- Commutativity (COMM) --- p.17Chapter 3.2.2 --- Forward and Right Backward Commutativity --- p.19Chapter 3.2.3 --- Exploiting Context-Specific Information --- p.21Chapter 3.2.4 --- Relaxing Correctness Criterion by Allowing Bounded Inconsistency --- p.26Chapter 4 --- Related Work --- p.32Chapter 4.1 --- Exploiting Transaction Semantics --- p.32Chapter 4.2 --- Exploting Object Semantics --- p.34Chapter 4.3 --- Sacrificing Consistency --- p.35Chapter 4.4 --- Other Approaches --- p.37Chapter 5 --- Performance Study (Testbed Approach) --- p.39Chapter 5.1 --- System Model --- p.39Chapter 5.1.1 --- Main Memory Database --- p.39Chapter 5.1.2 --- System Configuration --- p.40Chapter 5.1.3 --- Execution of Operations --- p.41Chapter 5.1.4 --- Recovery --- p.42Chapter 5.2 --- Parameter Settings and Performance Metrics --- p.43Chapter 6 --- Performance Results and Analysis (Testbed Approach) --- p.46Chapter 6.1 --- Read/Write Model vs. Abstract Data Type Model --- p.46Chapter 6.2 --- Using Context-Specific Information --- p.52Chapter 6.3 --- Role of Conflict Ratio --- p.55Chapter 6.4 --- Relaxing the Correctness Criterion --- p.58Chapter 6.4.1 --- Overhead and Performance Gain --- p.58Chapter 6.4.2 --- Range Queries using Bounded Inconsistency --- p.63Chapter 7 --- Performance Study (Simulation Approach) --- p.69Chapter 7.1 --- Simulation Model --- p.70Chapter 7.1.1 --- Logical Queueing Model --- p.70Chapter 7.1.2 --- Physical Queueing Model --- p.71Chapter 7.2 --- Experiment Information --- p.74Chapter 7.2.1 --- Parameter Settings --- p.74Chapter 7.2.2 --- Performance Metrics --- p.75Chapter 8 --- Performance Results and Analysis (Simulation Approach) --- p.76Chapter 8.1 --- Relaxing Correctness Criterion of Serial Executions --- p.77Chapter 8.1.1 --- Impact of Resource Contention --- p.77Chapter 8.1.2 --- Impact of Infinite Resources --- p.80Chapter 8.1.3 --- Impact of Limited Resources --- p.87Chapter 8.1.4 --- Impact of Multiple Resources --- p.89Chapter 8.1.5 --- Impact of Transaction Type --- p.95Chapter 8.1.6 --- Impact of Concurrency Control Overhead --- p.96Chapter 8.2 --- Exploiting Context-Specific Information --- p.98Chapter 8.2.1 --- Impact of Limited Resource --- p.98Chapter 8.2.2 --- Impact of Infinite and Multiple Resources --- p.101Chapter 8.2.3 --- Impact of Transaction Length --- p.106Chapter 8.2.4 --- Impact of Buffer Size --- p.108Chapter 8.2.5 --- Impact of Concurrency Control Overhead --- p.110Chapter 8.3 --- Summary and Discussion --- p.113Chapter 8.3.1 --- Summary of Results --- p.113Chapter 8.3.2 --- Relaxing Correctness Criterion vs. Exploiting Context-Specific In- formation --- p.114Chapter 9 --- Conclusions --- p.116Bibliography --- p.122Chapter A --- Commutativity Tables for Queue Objects --- p.128Chapter B --- Specification of a Queue Object --- p.129Chapter C --- Commutativity Tables with Bounded Inconsistency for Queue Objects --- p.132Chapter D --- Some Implementation Issues --- p.134Chapter D.1 --- Important Data Structures --- p.134Chapter D.2 --- Conflict Checking --- p.136Chapter D.3 --- Deadlock Detection --- p.137Chapter E --- Simulation Results --- p.139Chapter E.l --- Impact of Infinite Resources (Bounded Inconsistency) --- p.140Chapter E.2 --- Impact of Multiple Resource (Bounded Inconsistency) --- p.141Chapter E.3 --- Impact of Transaction Type (Bounded Inconsistency) --- p.142Chapter E.4 --- Impact of Concurrency Control Overhead (Bounded Inconsistency) --- p.144Chapter E.4.1 --- Infinite Resources --- p.144Chapter E.4.2 --- Limited Resource --- p.146Chapter E.5 --- Impact of Resource Levels (Exploiting Context-Specific Information) --- p.149Chapter E.6 --- Impact of Buffer Size (Exploiting Context-Specific Information) --- p.150Chapter E.7 --- Impact of Concurrency Control Overhead (Exploiting Context-Specific In- formation) --- p.155Chapter E.7.1 --- Impact of Infinite Resources --- p.155Chapter E.7.2 --- Impact of Limited Resources --- p.157Chapter E.7.3 --- Impact of Transaction Length --- p.160Chapter E.7.4 --- Role of Conflict Ratio --- p.16